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de Grauw MJJ, Scholten ET, Smit EJ, Rutten MJCM, Prokop M, van Ginneken B, Hering A. The ULS23 challenge: A baseline model and benchmark dataset for 3D universal lesion segmentation in computed tomography. Med Image Anal 2025; 102:103525. [PMID: 40054149 DOI: 10.1016/j.media.2025.103525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 02/21/2025] [Accepted: 02/22/2025] [Indexed: 04/15/2025]
Abstract
Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible at https://uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 ± 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.
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Affiliation(s)
- M J J de Grauw
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - E Th Scholten
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - E J Smit
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - M J C M Rutten
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Radiology, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands
| | - M Prokop
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Radiology, University Medical Center Groningen, Groningen, The Netherlands
| | - B van Ginneken
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - A Hering
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
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Machado L, Alberge L, Philippe H, Ferreres E, Khlaut J, Dupuis J, Le Floch K, Habip Gatenyo D, Roux P, Grégory J, Ronot M, Dancette C, Boeken T, Tordjman D, Manceron P, Hérent P. A promptable CT foundation model for solid tumor evaluation. NPJ Precis Oncol 2025; 9:121. [PMID: 40281056 PMCID: PMC12032241 DOI: 10.1038/s41698-025-00903-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2024] [Accepted: 04/05/2025] [Indexed: 04/29/2025] Open
Abstract
Carcinogenesis is inherently complex, resulting in heterogeneous tumors with variable outcomes and frequent metastatic potential. Conventional longitudinal evaluation methods like RECIST 1.1 remain labor-intensive and prone to measurement errors, while existing AI solutions face critical limitations due to tumor heterogeneity, insufficient annotations, and lack of user interaction. We developed ONCOPILOT, an interactive CT-based foundation model dedicated to 3D tumor segmentation, significantly refining RECIST 1.1 evaluations with active radiologist engagement. Trained on more than 8000 CT scans, ONCOPILOT employs intuitive visual prompts, including point-click, bounding boxes, and edit-points. It attains segmentation accuracy that matches or exceeds state-of-the-art methods, provides radiologist-level precision for RECIST 1.1 measurements, reduces inter-observer variability, and enhances workflow efficiency. Integrating clinical expertise with interactive AI capabilities, ONCOPILOT facilitates widespread access to advanced biomarkers, notably volumetric tumor analyses, thereby supporting improved clinical decision-making, patient stratification, and accelerating advancements in oncology research.
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Affiliation(s)
- Léo Machado
- Raidium, Paris Biotech Santé, Paris, France
- AP-HP. Nord, Department of Radiology, FHU MOSAIC, Beaujon Hospital, Clichy, France
| | | | - Hélène Philippe
- Raidium, Paris Biotech Santé, Paris, France
- AP-HP. Nord, Department of Radiology, FHU MOSAIC, Beaujon Hospital, Clichy, France
- Université Paris Cité, Paris, France
| | | | - Julien Khlaut
- Raidium, Paris Biotech Santé, Paris, France
- Department of Vascular and Oncological Interventional Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, HEKA INRIA, Paris, France
| | | | - Korentin Le Floch
- Raidium, Paris Biotech Santé, Paris, France
- Department of Vascular and Oncological Interventional Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, HEKA INRIA, Paris, France
| | | | - Pascal Roux
- Centre d'Imagerie du Nord, Saint-Denis, France
| | - Jules Grégory
- AP-HP. Nord, Department of Radiology, FHU MOSAIC, Beaujon Hospital, Clichy, France
- Université Paris Cité, Paris, France
| | - Maxime Ronot
- AP-HP. Nord, Department of Radiology, FHU MOSAIC, Beaujon Hospital, Clichy, France.
- Université Paris Cité, Paris, France.
| | | | - Tom Boeken
- Department of Vascular and Oncological Interventional Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, HEKA INRIA, Paris, France
| | | | | | - Paul Hérent
- Raidium, Paris Biotech Santé, Paris, France
- Centre d'Imagerie du Nord, Saint-Denis, France
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Balaguer-Montero M, Marcos Morales A, Ligero M, Zatse C, Leiva D, Atlagich LM, Staikoglou N, Viaplana C, Monreal C, Mateo J, Hernando J, García-Álvarez A, Salvà F, Capdevila J, Elez E, Dienstmann R, Garralda E, Perez-Lopez R. A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer. Cell Rep Med 2025; 6:102032. [PMID: 40118052 PMCID: PMC12047525 DOI: 10.1016/j.xcrm.2025.102032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 12/07/2024] [Accepted: 02/24/2025] [Indexed: 03/23/2025]
Abstract
Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineation. Developed on 1,598 computed tomography (CT) scans and 4,908 liver tumors, SALSA demonstrates superior accuracy in tumor identification and volume quantification, outperforming state-of-the-art models and inter-reader agreement among expert radiologists. SALSA achieves a patient-wise detection precision of 99.65%, and 81.72% at lesion level, in the external validation cohorts. Additionally, it exhibits good overlap, achieving a dice similarity coefficient (DSC) of 0.760, outperforming both state-of-the-art and the inter-radiologist assessment. SALSA's automatic quantification of tumor volume proves to have prognostic value across various solid tumors (p = 0.028). SALSA's robust capabilities position it as a potential medical device for automatic cancer detection, staging, and response evaluation.
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Affiliation(s)
| | - Adrià Marcos Morales
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany
| | - Christina Zatse
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - David Leiva
- Bellvitge University Hospital, 08907 Barcelona, Spain
| | - Luz M Atlagich
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain; Oncocentro Apys, Viña Del Mar 2520598, Chile
| | - Nikolaos Staikoglou
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Cristina Viaplana
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Camilo Monreal
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Joaquin Mateo
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Jorge Hernando
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Alejandro García-Álvarez
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Francesc Salvà
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Jaume Capdevila
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Elena Elez
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain; University of Vic - Central University of Catalonia, 08500 Vic, Spain
| | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain.
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Dioguardi Burgio M, Ronot M, Vilgrain V. ESR Essentials: assessing the radiological response of liver metastases to systemic therapy-practice recommendations by the European Society of Gastrointestinal and Abdominal Radiology. Eur Radiol 2025:10.1007/s00330-025-11540-1. [PMID: 40185923 DOI: 10.1007/s00330-025-11540-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2025] [Revised: 02/15/2025] [Accepted: 02/21/2025] [Indexed: 04/07/2025]
Abstract
The liver is a common site for metastatic spread, especially in advanced colorectal, breast, and pancreatic cancers. Imaging evaluation of liver metastases after systemic treatments like chemotherapy, targeted therapy, or immunotherapy is essential to distinguish treatment response from disease progression. The widely used response evaluation criteria in solid tumours (RECIST 1.1) focus on lesion size changes to evaluate treatment response. However, newer therapies, mainly targeted therapy and immunotherapy, often induce changes beyond size reduction, such as tumour necrosis, fibrosis, cystic transformation, calcifications, and modifications at the liver-tumour interface. These morphological and enhancement changes can be evaluated on CT and MRI and may better reflect the biological response in specific clinical settings. Overall, RECIST 1.1 criteria are recommended for assessing the radiological response of liver metastases after systemic treatment. The use of alternative radiological criteria validated on CT (such as Chun or Choi criteria) is recommended in specific clinical settings (e.g. metastatic colorectal cancer or metastatic gastrointestinal stromal tumours). Additionally, CT and MR modifications that reflect fibrosis, necrosis, calcifications, and haemorrhage can serve as ancillary indicators of tumoural response. These alternative criteria and radiological findings should be systematically assessed, particularly in liver metastases with minimal size changes, to better identify responders. KEY POINTS: RECIST 1.1 is the standard for evaluating tumour response in solid tumours and is recommended for the assessment of liver metastases after systemic therapy. CT attenuation, enhancement, and liver/tumour interface may correlate better with tumoural response compared to size reduction. CT and MR changes suggesting necrosis, fibrosis, calcifications, and haemorrhage can be used as additional indicators of tumoural response.
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Affiliation(s)
- Marco Dioguardi Burgio
- Université Paris Cité, Inserm, Centre de recherche sur l'inflammation, Paris, France.
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Clichy, France.
| | - Maxime Ronot
- Université Paris Cité, Inserm, Centre de recherche sur l'inflammation, Paris, France
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Clichy, France
| | - Valérie Vilgrain
- Université Paris Cité, Inserm, Centre de recherche sur l'inflammation, Paris, France
- Department of Radiology, Hôpital Beaujon, AP-HP.Nord, Clichy, France
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van der Loo I, Bucho TMT, Hanley JA, Beets-Tan RGH, Imholz ALT, Trebeschi S. Measurement variability of radiologists when measuring brain tumors. Eur J Radiol 2025; 183:111874. [PMID: 39657547 DOI: 10.1016/j.ejrad.2024.111874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Accepted: 12/02/2024] [Indexed: 12/12/2024]
Abstract
BACKGROUND In oncology trials, response evaluation criteria are pivotal in developing new treatments. This study examines the influence of measurement variability in brain lesions on response classification, considering long-standing cut-offs for progression and response were determined before the era of submillimeter resolutions of medical imaging. METHODS We replicate a key study using modern radiological tools. Sixteen radiologists were tasked with measuring twelve near-spherical brain tumors using visual estimation (eyeballing), diameter measurements and artificial intelligence (AI) assisted segmentations. Analyses for inter- and intraobserver variability from the original were replicated. Additionally, we researched the effect of measurement error on the misclassification of progressive disease using a computer simulation model. RESULTS The combined effect of intra- and interobserver error varied between 13.6 and 22.2% for eyeballing and 6.8-7.2% for diameter measurement, using AI-assisted segmentation as reference. We observed erroneously declared progression (cut-off at 20% increase) in repeat measurements of the same tumor in 25.5% of instances for eyeballing and in 1.1% for diameter measurements. Response (cut-off at 30% decrease) was erroneously declared in 12.3% for eyeballing and in 0% for diameter measurements. The simulation model demonstrated a more pronounced impact of measurement error on cases with fewer total number of lesions. CONCLUSIONS This study provides a minimum expected measurement error using real-world data. The impact of measurement error on response evaluation criteria misclassification in brain lesions was most pronounced for eyeballing. Future research should focus on measurement error for different tumor types and assess its impact on response classification during patient follow-up.
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Affiliation(s)
- Iris van der Loo
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW - Research Institute for Oncology & Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Teresa M Tareco Bucho
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW - Research Institute for Oncology & Reproduction, Maastricht University, Maastricht, the Netherlands
| | - James A Hanley
- Department of Epidemiology and Biostatistics, McGill University, Montréal, Canada
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW - Research Institute for Oncology & Reproduction, Maastricht University, Maastricht, the Netherlands; Faculty of Health Sciences, University of Southern Denmark, Odense M, Denmark
| | - Alex L T Imholz
- Department of Oncology, Deventer Ziekenhuis, Deventer, the Netherlands
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands; GROW - Research Institute for Oncology & Reproduction, Maastricht University, Maastricht, the Netherlands.
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Meyer M, Ota H, Messiou C, Benson C, Henzler T, Mattonen SA, Marin D, Bartsch A, Schoenberg SO, Riedel RF, Hohenberger P. Prospective evaluation of quantitative response parameter in patients with Gastrointestinal Stroma Tumor undergoing tyrosine kinase inhibitor therapy-Impact on clinical outcome. Int J Cancer 2024; 155:2047-2057. [PMID: 39023303 DOI: 10.1002/ijc.35094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/18/2024] [Accepted: 06/04/2024] [Indexed: 07/20/2024]
Abstract
The purpose of this study was to determine if dual-energy CT (DECT) vital iodine tumor burden (ViTB), a direct assessment of tumor vascularity, allows reliable response assessment in patients with GIST compared to established CT criteria such as RECIST1.1 and modified Choi (mChoi). From 03/2014 to 12/2019, 138 patients (64 years [32-94 years]) with biopsy proven GIST were entered in this prospective, multi-center trial. All patients were treated with tyrosine kinase inhibitors (TKI) and underwent pre-treatment and follow-up DECT examinations for a minimum of 24 months. Response assessment was performed according to RECIST1.1, mChoi, vascular tumor burden (VTB) and DECT ViTB. A change in therapy management could be because of imaging (RECIST1.1 or mChoi) and/or clinical progression. The DECT ViTB criteria had the highest discrimination ability for progression-free survival (PFS) of all criteria in both first line and second line and thereafter treatment, and was significantly superior to RECIST1.1 and mChoi (p < .034). Both, the mChoi and DECT ViTB criteria demonstrated a significantly early median time-to-progression (both delta 2.5 months; both p < .036). Multivariable analysis revealed 6 variables associated with shorter overall survival: secondary mutation (HR = 4.62), polymetastatic disease (HR = 3.02), metastatic second line and thereafter treatment (HR = 2.33), shorter PFS determined by the DECT ViTB criteria (HR = 1.72), multiple organ metastases (HR = 1.51) and lower age (HR = 1.04). DECT ViTB is a reliable response criteria and provides additional value for assessing TKI treatment in GIST patients. A significant superior response discrimination ability for median PFS was observed, including non-responders at first follow-up and patients developing resistance while on therapy.
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Affiliation(s)
- Mathias Meyer
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Hideki Ota
- Department of Diagnostic Radiology, Tohoku University Hospital, Miyagi, Japan
| | - Christina Messiou
- Department of Radiology, Royal Marsden Hospital and Institute of Cancer Research, London, UK
| | | | - Thomas Henzler
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
| | - Sarah A Mattonen
- Department of Medical Biophysics, Western University, London, Canada
| | - Daniele Marin
- Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Anna Bartsch
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
- Department of Orthopedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany
| | - Richard F Riedel
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA
| | - Peter Hohenberger
- Division of Surgical Oncology and Thoracic Surgery, University Medical Center Mannheim, Ruprecht-Karl University of Heidelberg, Mannheim, Germany
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Illy M, Bartoli A, Mancini J, Duffaud F, Vidal V, Tradi F. Dedicated software to harmonize the follow-up of oncological patients. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2024; 12:100051. [PMID: 39391594 PMCID: PMC11462215 DOI: 10.1016/j.redii.2024.100051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 08/04/2024] [Indexed: 10/12/2024]
Abstract
Objective To test and evaluate a sofware dedicated to the follow-up of oncological CT scans for potential use in the Radiology department. Materials and methods In this retrospective study, 37 oncological patients with baseline and follow-up CT scans were reinterpreted using a dedicated software. Baseline CT scans were chosen from the imaging reports available in our PACS (picture archiving and communicatin systems). Follow-up interpretations were independently assessed with the software. We evaluated the target lesion sums and the tumor response based on RECIST 1.1 (Response Evaluation Criteria in Solid Tumors). Results There was no significant difference in the target lesion sums and the tumor response assessments between the PACS data and the imaging software. There was no over or underestimation of the disease with the software. There was a sigificant deviation (progression versus stability) in three cases. For two patients, this difference was related to the evaluation of the response of non-target lesions. The difference in the third patient was due to comparison with a previous CT scan than to the baseline exam. There was a miscalculation in 13 % of the reports and in 28 % of the cases the examination was compared to the previous CT scan. Finally, the tumor response was not detailed in 43 % of the follow-up reports. Conclusion The use of dedicated oncology monitoring software could help in reducing intepretation time and in limiting human errors.
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Affiliation(s)
- Mathias Illy
- Radiology Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Axel Bartoli
- Radiology Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Julien Mancini
- Public Health Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Florence Duffaud
- Oncology Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Vincent Vidal
- Radiology Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
| | - Farouk Tradi
- Radiology Department, hôpital de la Timone, 264, rue Saint-Pierre, 13005 Marseille, France
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Dahm IC, Kolb M, Altmann S, Nikolaou K, Gatidis S, Othman AE, Hering A, Moltz JH, Peisen F. Reliability of Automated RECIST 1.1 and Volumetric RECIST Target Lesion Response Evaluation in Follow-Up CT-A Multi-Center, Multi-Observer Reading Study. Cancers (Basel) 2024; 16:4009. [PMID: 39682195 DOI: 10.3390/cancers16234009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2024] [Revised: 11/11/2024] [Accepted: 11/19/2024] [Indexed: 12/18/2024] Open
Abstract
OBJECTIVES To evaluate the performance of a custom-made convolutional neural network (CNN) algorithm for fully automated lesion tracking and segmentation, as well as RECIST 1.1 evaluation, in longitudinal computed tomography (CT) studies compared to a manual Response Evaluation Criteria in Solid Tumors (RECIST 1.1) evaluation performed by three radiologists. METHODS Baseline and follow-up CTs of patients with stage IV melanoma (n = 58) was investigated in a retrospective reading study. Three radiologists performed manual measurements of metastatic lesions. Fully automated segmentations were generated, and diameters and volumes were computed from the segmentation results, with subsequent RECIST 1.1 evaluation. We measured (1) the intra- and inter-reader variability in the manual diameter measurements, (2) the agreement between manual and automated diameter measurements, as well as the resulting RECIST 1.1 categories, and (3) the agreement between the RECIST 1.1 categories derived from automated diameter measurement compared to automated volume measurements. RESULTS In total, 114 target lesions were measured at baseline and follow-up. The intraclass correlation coefficients (ICCs) for the intra- and inter-reader reliability of the diameter measurements were excellent, being >0.90 for all readers. There was moderate to almost perfect agreement when comparing the timepoint response category derived from the mean manual diameter measurements from all three readers with those derived from automated diameter measurements (Cohen's k 0.67-0.76). The agreement between the manual and automated volumetric timepoint responses was substantial (Fleiss' k 0.66-0.68) and that between the automated diameter and volume timepoint responses was substantial to almost perfect (Cohen's k 0.81). CONCLUSIONS The automated diameter measurement of preselected target lesions in follow-up CT is reliable and can potentially help to accelerate RECIST evaluation.
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Affiliation(s)
- Isabel C Dahm
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Manuel Kolb
- Department of Radiology, Te Whatu Ora Waikato, Hamilton 3240, New Zealand
| | - Sebastian Altmann
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
- Image-Guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence (EXC 2180), 72076 Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstr. 1, 55131 Mainz, Germany
| | - Alessa Hering
- Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany
- Diagnostic Image Analysis Group, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Jan H Moltz
- Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359 Bremen, Germany
| | - Felix Peisen
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Str. 3, 72076 Tuebingen, Germany
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Chung HC, Saada-Bouzid E, Longo F, Yanez E, Im SA, Castanon E, Desautels DN, Graham DM, Garcia-Corbacho J, Lopez J, Dutcus C, Okpara CE, Ghori R, Jin F, Groisberg R, Korakis I. Lenvatinib plus pembrolizumab for patients with previously treated, advanced, triple-negative breast cancer: Results from the triple-negative breast cancer cohort of the phase 2 LEAP-005 Study. Cancer 2024; 130:3278-3288. [PMID: 39031824 DOI: 10.1002/cncr.35387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/14/2024] [Accepted: 03/25/2024] [Indexed: 07/22/2024]
Abstract
BACKGROUND Novel treatments are needed for patients with advanced, triple-negative breast cancer (TNBC) that progresses or recurs after first-line treatment with chemotherapy. The authors report results from the TNBC cohort of the multicohort, open-label, single-arm, phase 2 LEAP-005 study of lenvatinib plus pembrolizumab in patients with advanced solid tumors (ClinicalTrials.gov identifier NCT03797326). METHODS Eligible patients had metastatic or unresectable TNBC with disease progression after one or two lines of therapy. Patients received lenvatinib (20 mg daily) plus pembrolizumab (200 mg every 3 weeks; up to 35 cycles). The primary end points were the objective response rate according to Response Evaluation Criteria in Solid Tumors, version 1.1, and safety (adverse events graded by the National Cancer Institute's Common Terminology Criteria for Adverse Events, version 4.0). Duration of response, progression-free survival, and overall survival were secondary end points. RESULTS Thirty-one patients were enrolled. The objective response rate by investigator assessment was 23% (95% confidence interval [CI], 10%-41%). Overall, the objective response rate by blinded independent central review (BICR) was 32% (95% CI, 17%-51%); and, in patients who had programmed cell death ligand 1 combined positive scores ≥10 (n = 8) and <10 (n = 22), the objective response rate was 50% (95% CI, 16%-84%) and 27% (95% CI, 11%-50%), respectively. The median duration of response by BICR was 12.1 months (range, from 3.0+ to 37.9+ months). The median progression-free survival by BICR was 5.1 months (95% CI, 1.9-11.8 months) and the median overall survival was 11.4 months (95% CI, 4.1-21.7 months). Treatment-related adverse events occurred in 94% of patients (grade 3, 52%; grade 4, 0%). One patient died due to a treatment-related adverse event of subarachnoid hemorrhage. CONCLUSIONS The combination of lenvatinib plus pembrolizumab demonstrated antitumor activity with a manageable safety profile in patients with previously treated, advanced TNBC.
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Affiliation(s)
- Hyun Cheol Chung
- Department of Medical Oncology, Yonsei Cancer Center, Yonsei Song-Dang Institute for Cancer Research, Yonsei University College of Medicine, Seoul, Korea
| | - Esma Saada-Bouzid
- Department of Medical Oncology, Cote d'Azur University, Centre Antoine Lacassagne, Nice, France
| | - Federico Longo
- Medical Oncology Department, Ramón y Cajal University Hospital, Instituto Ramón y Cajal de Investigación Sanitaria, Centro de Investigación Biomédica en Red Cáncer, Alcalá University, Madrid, Spain
| | - Eduardo Yanez
- Oncology-Hematology Unit, University of Frontera, Araucanía, Chile
| | - Seock-Ah Im
- Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Eduardo Castanon
- Department of Oncology, Clínica Universidad de Navarra, Madrid, Spain
| | - Danielle N Desautels
- Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Donna M Graham
- Experimental Cancer Medicine Team, The Christie National Health Service Foundation Trust, Manchester, UK
- University of Manchester, Manchester, UK
| | | | - Juanita Lopez
- Phase I Drug Development Unit, The Royal Marsden Hospital and The Institute of Cancer Research, Sutton, UK
| | | | | | - Razi Ghori
- Merck & Co., Inc., Rahway, New Jersey, USA
| | - Fan Jin
- Merck & Co., Inc., Rahway, New Jersey, USA
| | | | - Iphigenie Korakis
- Department of Medicine and Clinical Research Unit, Institut Claudius Regaud/Institut Universitaire du Cancer de Toulouse (IUCT-Oncopole), Toulouse, France
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Iannessi A, Beaumont H, Ojango C, Bertrand AS, Liu Y. RECIST 1.1 assessments variability: a systematic pictorial review of blinded double reads. Insights Imaging 2024; 15:199. [PMID: 39112819 PMCID: PMC11306910 DOI: 10.1186/s13244-024-01774-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 07/07/2024] [Indexed: 08/10/2024] Open
Abstract
Reader variability is intrinsic to radiologic oncology assessments, necessitating measures to enhance consistency and accuracy. RECIST 1.1 criteria play a crucial role in mitigating this variability by standardizing evaluations, aiming to establish an accepted "truth" confirmed by histology or patient survival. Clinical trials utilize Blind Independent Centralized Review (BICR) techniques to manage variability, employing double reads and adjudicators to address inter-observer discordance effectively. It is essential to dissect the root causes of variability in response assessments, with a specific focus on the factors influencing RECIST evaluations. We propose proactive measures for radiologists to address variability sources such as radiologist expertise, image quality, and accessibility of contextual information, which significantly impact interpretation and assessment precision. Adherence to standardization and RECIST guidelines is pivotal in diminishing variability and ensuring uniform results across studies. Variability factors, including lesion selection, new lesion appearance, and confirmation bias, can have profound implications on assessment accuracy and interpretation, underscoring the importance of identifying and addressing these factors. Delving into the causes of variability aids in enhancing the accuracy and consistency of response assessments in oncology, underscoring the role of standardized evaluation protocols and mitigating risk factors that contribute to variability. Access to contextual information is crucial. CRITICAL RELEVANCE STATEMENT: By understanding the causes of diagnosis variability, we can enhance the accuracy and consistency of response assessments in oncology, ultimately improving patient care and clinical outcomes. KEY POINTS: Baseline lesion selection and detection of new lesions play a major role in the occurrence of discordance. Image interpretation is influenced by contextual information, the lack of which can lead to diagnostic uncertainty. Radiologists must be trained in RECIST criteria to reduce errors and variability.
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Affiliation(s)
- Antoine Iannessi
- Cancer Center Antoine Lacassagne 33 Av. de Valombrose, 06100, Nice, France
- Median Technologies SA 1800 Route des Crêtes, 06560, Valbonne, France
| | - Hubert Beaumont
- Median Technologies SA 1800 Route des Crêtes, 06560, Valbonne, France.
| | - Christine Ojango
- Median Technologies SA 1800 Route des Crêtes, 06560, Valbonne, France
| | - Anne-Sophie Bertrand
- Imaging Center Beaulieu-sur-mer 18 Bd Eugène Gauthier, 06310, Beaulieu-sur-Mer, France
| | - Yan Liu
- Median Technologies SA 1800 Route des Crêtes, 06560, Valbonne, France
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Yeghaian M, Tareco Bucho TM, de Bruin M, Schmitz A, Bodalal Z, Smit EF, Beets-Tan RGH, van den Broek D, Trebeschi S. Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy? J Cancer Res Clin Oncol 2024; 150:329. [PMID: 38922374 PMCID: PMC11208229 DOI: 10.1007/s00432-024-05814-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 05/21/2024] [Indexed: 06/27/2024]
Abstract
PURPOSE In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors. METHODS We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC. RESULTS The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey. CONCLUSION Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.
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Affiliation(s)
- Melda Yeghaian
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Teresa M Tareco Bucho
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Melissa de Bruin
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Alexander Schmitz
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Zuhir Bodalal
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Egbert F Smit
- Pulmonology Department, Leiden University Medical Center, Leiden, The Netherlands
| | - Regina G H Beets-Tan
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Daan van den Broek
- Department of Laboratory Medicine, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefano Trebeschi
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands.
- Radiology Department, Netherlands Cancer Institute, Amsterdam, The Netherlands.
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12
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Tareco Bucho TM, Tissier RLM, Groot Lipman KBW, Bodalal Z, Delli Pizzi A, Nguyen-Kim TDL, Beets-Tan RGH, Trebeschi S. How Does Target Lesion Selection Affect RECIST? A Computer Simulation Study. Invest Radiol 2024; 59:465-471. [PMID: 37921780 DOI: 10.1097/rli.0000000000001045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
OBJECTIVES Response Evaluation Criteria in Solid Tumors (RECIST) is grounded on the assumption that target lesion selection is objective and representative of the change in total tumor burden (TTB) during therapy. A computer simulation model was designed to challenge this assumption, focusing on a particular aspect of subjectivity: target lesion selection. MATERIALS AND METHODS Disagreement among readers and the disagreement between individual reader measurements and TTB were analyzed as a function of the total number of lesions, affected organs, and lesion growth. RESULTS Disagreement rises when the number of lesions increases, when lesions are concentrated on a few organs, and when lesion growth borders the thresholds of progressive disease and partial response. There is an intrinsic methodological error in the estimation of TTB via RECIST 1.1, which depends on the number of lesions and their distributions. For example, for a fixed number of lesions at 5 and 15, distributed over a maximum of 4 organs, the error rates are observed to be 7.8% and 17.3%, respectively. CONCLUSIONS Our results demonstrate that RECIST can deliver an accurate estimate of TTB in localized disease, but fails in cases of distal metastases and multiple organ involvement. This is worsened by the "selection of the largest lesions," which introduces a bias that makes it hardly possible to perform an accurate estimate of the TTB. Including more (if not all) lesions in the quantitative analysis of tumor burden is desirable.
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Affiliation(s)
- Teresa M Tareco Bucho
- From the Radiology Department (T.T.B., K.G.L., Z.B., T.D.L.N.-K., R.B.-T., S.T.), Biostatistics Unit (R.T.), and Thoracic Oncology (K.G.L.), Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands (T.T.B., K.G.L., Z.B., R.B.-T., S.T.); Institute for Advanced Biomedical Technologies, Gabriele d'Annunzio University of Chieti-Pescara, Italy (A.D.P.); Department of Innovative Technologies in Medicine and Dentistry, Gabriele d'Annunzio University of Chieti-Pescara, Italy (A.D.P.); Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland (T.D.L.N.-K.); Institute of Radiology and Nuclear Medicine, Stadtspital Zürich, Zurich, Switzerland (T.D.L.N.-K.); and Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark (R.B.-T.)
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13
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Tareco Bucho TM, Petrychenko L, Abdelatty MA, Bogveradze N, Bodalal Z, Beets-Tan RG, Trebeschi S. Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation. Eur J Radiol Open 2024; 12:100562. [PMID: 38660370 PMCID: PMC11039940 DOI: 10.1016/j.ejro.2024.100562] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024] Open
Abstract
Background The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.
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Affiliation(s)
- Teresa M. Tareco Bucho
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Liliana Petrychenko
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Mohamed A. Abdelatty
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Radiology, Kasr Al Ainy Hospital, Cairo University, Cairo, Egypt
| | - Nino Bogveradze
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Zuhir Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
| | - Regina G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Denmark
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands
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Wesdorp NJ, Zeeuw JM, Postma SCJ, Roor J, van Waesberghe JHTM, van den Bergh JE, Nota IM, Moos S, Kemna R, Vadakkumpadan F, Ambrozic C, van Dieren S, van Amerongen MJ, Chapelle T, Engelbrecht MRW, Gerhards MF, Grunhagen D, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Liem MSL, van Lienden KP, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Marquering HA, Stoker J, Swijnenburg RJ, Punt CJA, Huiskens J, Kazemier G. Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases. Eur Radiol Exp 2023; 7:75. [PMID: 38038829 PMCID: PMC10692044 DOI: 10.1186/s41747-023-00383-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/08/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). METHODS In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. RESULTS In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation. CONCLUSIONS Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. RELEVANCE STATEMENT Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency. KEY POINTS • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.
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Affiliation(s)
- Nina J Wesdorp
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands.
| | - J Michiel Zeeuw
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands.
| | - Sam C J Postma
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Joran Roor
- Department of Health, SAS Institute B.V, Huizen, the Netherlands
| | - Jan Hein T M van Waesberghe
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Janneke E van den Bergh
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Irene M Nota
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Shira Moos
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ruby Kemna
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Fijoy Vadakkumpadan
- Department of Computer Vision and Machine Learning, SAS Institute Inc, Cary, NC, USA
| | - Courtney Ambrozic
- Department of Computer Vision and Machine Learning, SAS Institute Inc, Cary, NC, USA
| | - Susan van Dieren
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | | | - Thiery Chapelle
- Department of Hepatobiliary, Transplantation, and Endocrine Surgery, Antwerp University Hospital, Antwerp, Belgium
| | - Marc R W Engelbrecht
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Dirk Grunhagen
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Thomas M van Gulik
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - John J Hermans
- Department of Medical Imaging, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Koert P de Jong
- Department of HPB Surgery and Liver Transplantation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Joost M Klaase
- Department of HPB Surgery and Liver Transplantation, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Mike S L Liem
- Department of Surgery, Medical Spectrum Twente, Enschede, the Netherlands
| | - Krijn P van Lienden
- Department of Interventional Radiology, St Antonius Hospital, Nieuwegein, the Netherlands
| | - I Quintus Molenaar
- Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Surgery, St Antonius Hospital, Nieuwegein, the Netherlands
| | - Gijs A Patijn
- Department of Surgery, Isala Hospital, Zwolle, the Netherlands
| | - Arjen M Rijken
- Department of Surgery, Amphia Hospital, Breda, the Netherlands
| | - Theo M Ruers
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Johannes H W de Wilt
- Department of Surgery, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Henk A Marquering
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jaap Stoker
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Rutger-Jan Swijnenburg
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Cornelis J A Punt
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost Huiskens
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
| | - Geert Kazemier
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, the Netherlands
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Beaumont H, Iannessi A. Can we predict discordant RECIST 1.1 evaluations in double read clinical trials? Front Oncol 2023; 13:1239570. [PMID: 37869080 PMCID: PMC10585359 DOI: 10.3389/fonc.2023.1239570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/05/2023] [Indexed: 10/24/2023] Open
Abstract
Background In lung clinical trials with imaging, blinded independent central review with double reads is recommended to reduce evaluation bias and the Response Evaluation Criteria In Solid Tumor (RECIST) is still widely used. We retrospectively analyzed the inter-reader discrepancies rate over time, the risk factors for discrepancies related to baseline evaluations, and the potential of machine learning to predict inter-reader discrepancies. Materials and methods We retrospectively analyzed five BICR clinical trials for patients on immunotherapy or targeted therapy for lung cancer. Double reads of 1724 patients involving 17 radiologists were performed using RECIST 1.1. We evaluated the rate of discrepancies over time according to four endpoints: progressive disease declared (PDD), date of progressive disease (DOPD), best overall response (BOR), and date of the first response (DOFR). Risk factors associated with discrepancies were analyzed, two predictive models were evaluated. Results At the end of trials, the discrepancy rates between trials were not different. On average, the discrepancy rates were 21.0%, 41.0%, 28.8%, and 48.8% for PDD, DOPD, BOR, and DOFR, respectively. Over time, the discrepancy rate was higher for DOFR than DOPD, and the rates increased as the trial progressed, even after accrual was completed. It was rare for readers to not find any disease, for less than 7% of patients, at least one reader selected non-measurable disease only (NTL). Often the readers selected some of their target lesions (TLs) and NTLs in different organs, with ranges of 36.0-57.9% and 60.5-73.5% of patients, respectively. Rarely (4-8.1%) two readers selected all their TLs in different locations. Significant risk factors were different depending on the endpoint and the trial being considered. Prediction had a poor performance but the positive predictive value was higher than 80%. The best classification was obtained with BOR. Conclusion Predicting discordance rates necessitates having knowledge of patient accrual, patient survival, and the probability of discordances over time. In lung cancer trials, although risk factors for inter-reader discrepancies are known, they are weakly significant, the ability to predict discrepancies from baseline data is limited. To boost prediction accuracy, it would be necessary to enhance baseline-derived features or create new ones, considering other risk factors and looking into optimal reader associations.
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Solass W, Nadiradze G, Reymond MA, Bösmüller H. The Role of Additional Staining in the Assessment of the Peritoneal Regression Grading Score (PRGS) in Peritoneal Metastasis of Gastric Origin. Appl Immunohistochem Mol Morphol 2023; 31:583-589. [PMID: 37698957 DOI: 10.1097/pai.0000000000001152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/26/2023] [Indexed: 09/14/2023]
Abstract
INTRODUCTION The Peritoneal Regression Grading Score (PRGS) is a 4-tied histologic regression grading score for determining the response of peritoneal metastasis to chemotherapy. Peritoneal biopsies in every abdominal quadrant are recommended. A positive therapy response is defined as a decreasing or stable mean PRGS between 2 therapy cycles. The added value of periodic acid satin (PAS) and Ber-EP4 staining over HE staining for diagnosing PRGS1 (the absence of vital tumor cells) is unclear. MATERIALS AND METHODS A total of 339 biopsies obtained during 76 laparoscopies in 33 patients with peritoneal metastasis of gastric cancer were analyzed. Biopsies classified as PRGS 1 (no residual tumor, n=95) or indefinite (n=50) were stained with PAS, and remaining indefinite or PRGS1 cases additionally stained with BerEP4. RESULTS After PAS-staining tumor cells were detected in 28 out of 145 biopsies (19%), the remaining 117 biopsies were immunostained with Ber-EP4. Tumor cells were detected in 22 biopsies (19%). In total, additional staining allowed the detection of residual tumor cells in 50 out of 339 biopsies (15%) and changed the therapy response assessment in 7 out of 33 (21%) patients. CONCLUSIONS In summary, 25% (24 out of 95) of initially tumor-free samples (PRGS1) showed residual tumor cells after additional staining with PAS and/or BerEp4. Immunohistochemistry provided important additional information (the presence of tumor cells) in 22 of all 339 biopsies (11.2%). Further staining reduced the instances of unclear diagnosis from 50 to 0 and changed the therapy response assessment in 7 out of 33 patients (21%). We recommend additional staining in PRGS1 or unclear cases.
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Affiliation(s)
- Wiebke Solass
- Institute of Tissue Medicine and Pathology Bern, University Bern, Switzerland
- National Center for Pleura and Peritoneum
- Institute of Pathology
| | - Giorgi Nadiradze
- National Center for Pleura and Peritoneum
- Department of General and Transplant Surgery, University Hospital Tuebingen, Eberhard-Karls-University, Tuebingen, Germany
| | - Marc A Reymond
- National Center for Pleura and Peritoneum
- Department of General and Transplant Surgery, University Hospital Tuebingen, Eberhard-Karls-University, Tuebingen, Germany
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Lin TA, Sherry AD, Ludmir EB. Challenges, Complexities, and Considerations in the Design and Interpretation of Late-Phase Oncology Trials. Semin Radiat Oncol 2023; 33:429-437. [PMID: 37684072 PMCID: PMC10917127 DOI: 10.1016/j.semradonc.2023.06.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.
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Affiliation(s)
- Timothy A Lin
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexander D Sherry
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ethan B Ludmir
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX.; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX..
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Siegel MJ, Ippolito JE, Wahl RL, Siegel BA. Discrepant Assessments of Progressive Disease in Clinical Trials between Routine Clinical Reads and Formal RECIST 1.1 Interpretations. Radiol Imaging Cancer 2023; 5:e230001. [PMID: 37540134 PMCID: PMC10546354 DOI: 10.1148/rycan.230001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/01/2023] [Accepted: 06/21/2023] [Indexed: 08/05/2023]
Abstract
Purpose To analyze the frequency of discrepant interpretations of progressive disease (PD) between routine clinical and formal Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 interpretations in patients enrolled in solid tumor clinical trials and investigate the causes of discordance. Materials and Methods This retrospective study included patients in solid tumor clinical trials undergoing imaging response assessments based on RECIST 1.1 from January to July 2021. Routine clinical interpretations (RCIs) performed as part of standard workflow and not requiring formal use of any established response criteria were compared with separate local core laboratory interpretations (CLIs) by specially trained radiologists who used software that tracks target lesion measurements, changes in nontarget lesions, and appearance of new lesions longitudinally. The comparison focused on discordant interpretations of PD. Results Among 1053 patients who had both RCIs and CLIs performed, PD was diagnosed on one or both reads in 327 patients (median age, 63.6 [range, 22.4-83.2] years; 57.8% female patients). The RCIs and CLIs agreed with PD status in 65% (213 of 327) of assessments. In 32% (105 of 327) of assessments, RCIs overdiagnosed PD when CLIs diagnosed stable disease, and in 3% (nine of 327), CLIs diagnosed PD when RCIs diagnosed stable disease. Reasons for discrepant RCIs of PD included erroneous target lesion measurements (58%, 61 of 105), erroneous diagnosis of nontarget progression (30%, 32 of 105), and misclassification of new lesions as cancer (11%, 12 of 105). Most patients (93%, 98 of 105) with RCI overdiagnosis of PD remained in the clinical trial for one or more treatment cycles. Conclusion PD was frequently overdiagnosed on RCIs versus formal RECIST 1.1 CLIs which could result in patients removed from the clinical trial inappropriately. Keywords: Oncology, Cancer, Tumor Response, MR Imaging, CT © RSNA, 2023 See also commentary by Margolis and Ruchalski in this issue.
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Affiliation(s)
- Marilyn J. Siegel
- From the Edward Mallinckrodt Institute of Radiology and Alvin J.
Siteman Cancer Center, Washington University School of Medicine, 510 S
Kingshighway Blvd, St Louis, MO 63110
| | - Joseph E. Ippolito
- From the Edward Mallinckrodt Institute of Radiology and Alvin J.
Siteman Cancer Center, Washington University School of Medicine, 510 S
Kingshighway Blvd, St Louis, MO 63110
| | - Richard L. Wahl
- From the Edward Mallinckrodt Institute of Radiology and Alvin J.
Siteman Cancer Center, Washington University School of Medicine, 510 S
Kingshighway Blvd, St Louis, MO 63110
| | - Barry A. Siegel
- From the Edward Mallinckrodt Institute of Radiology and Alvin J.
Siteman Cancer Center, Washington University School of Medicine, 510 S
Kingshighway Blvd, St Louis, MO 63110
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Edland KH, Tjensvoll K, Oltedal S, Dalen I, Lapin M, Garresori H, Glenjen N, Gilje B, Nordgård O. Monitoring of circulating tumour DNA in advanced pancreatic ductal adenocarcinoma predicts clinical outcome and reveals disease progression earlier than radiological imaging. Mol Oncol 2023; 17:1857-1870. [PMID: 37341038 PMCID: PMC10483602 DOI: 10.1002/1878-0261.13472] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/03/2023] [Accepted: 06/19/2023] [Indexed: 06/22/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with a need for better tools to guide treatment selection and follow-up. The aim of this prospective study was to investigate the prognostic value and treatment monitoring potential of longitudinal circulating tumour DNA (ctDNA) measurements in patients with advanced PDAC undergoing palliative chemotherapy. Using KRAS peptide nucleic acid clamp-PCR, we measured ctDNA levels in plasma samples obtained at baseline and every 4 weeks during chemotherapy from 81 patients with locally advanced and metastatic PDAC. Cox proportional hazard regression showed that ctDNA detection at baseline was an independent predictor of progression-free and overall survival. Joint modelling demonstrated that the dynamic ctDNA level was a strong predictor of time to first disease progression. Longitudinal ctDNA measurements during chemotherapy successfully revealed disease progression in 20 (67%) of 30 patients with ctDNA detected at baseline, with a median lead time of 23 days (P = 0.01) over radiological imaging. Here, we confirmed the clinical relevance of ctDNA in advanced PDAC with regard to both the prediction of clinical outcome and disease monitoring during treatment.
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Affiliation(s)
| | - Kjersti Tjensvoll
- Department of Hematology and OncologyStavanger University HospitalNorway
| | - Satu Oltedal
- Department of Hematology and OncologyStavanger University HospitalNorway
| | - Ingvild Dalen
- Section of Biostatistics, Department of ResearchStavanger University HospitalNorway
| | - Morten Lapin
- Department of Hematology and OncologyStavanger University HospitalNorway
| | - Herish Garresori
- Department of Hematology and OncologyStavanger University HospitalNorway
| | - Nils Glenjen
- Department of OncologyHaukeland University HospitalBergenNorway
| | - Bjørnar Gilje
- Department of Hematology and OncologyStavanger University HospitalNorway
| | - Oddmund Nordgård
- Department of Hematology and OncologyStavanger University HospitalNorway
- Department of Chemistry, Bioscience and Environmental Technology, Faculty of Science and TechnologyUniversity of StavangerNorway
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20
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Ronot M, Dioguardi Burgio M, Gregory J, Hentic O, Vullierme MP, Ruszniewski P, Zappa M, de Mestier L. Appropriate use of morphological imaging for assessing treatment response and disease progression of neuroendocrine tumors. Best Pract Res Clin Endocrinol Metab 2023; 37:101827. [PMID: 37858478 DOI: 10.1016/j.beem.2023.101827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Neuroendocrine tumors (NETs) are relatively rare neoplasms displaying heterogeneous clinical behavior, ranging from indolent to aggressive forms. Patients diagnosed with NETs usually receive a varied array of treatments, including somatostatin analogs, locoregional treatments (ablation, intra-arterial therapy), cytotoxic chemotherapy, peptide receptor radionuclide therapy (PRRT), and targeted therapies. To maximize therapeutic efficacy while limiting toxicity (both physical and economic), there is a need for accurate and reliable tools to monitor disease evolution and progression and to assess the effectiveness of these treatments. Imaging morphological methods, primarily relying on computed tomography (CT) and magnetic resonance imaging (MRI), are indispensable modalities for the initial evaluation and continuous monitoring of patients with NETs, therefore playing a pivotal role in gauging the response to treatment. The primary goal of assessing tumor response is to anticipate and weigh the benefits of treatments, especially in terms of survival gain. The World Health Organization took the pioneering step of introducing assessment criteria based on cross-sectional imaging. This initial proposal standardized the measurement of lesion sizes, laying the groundwork for subsequent criteria. The Response Evaluation Criteria in Solid Tumors (RECIST) subsequently refined and enhanced these standards, swiftly gaining acceptance within the oncology community. New treatments were progressively introduced, targeting specific features of NETs (such as tumor vascularization or expression of specific receptors), and achieving significant qualitative changes within tumors, although associated with minimal or paradoxical effects on tumor size. Several alternative criteria, adapted from those used in other cancer types and focusing on tumor viability, the slow growth of NETs, or refining the existing size-based RECIST criteria, have been proposed in NETs. This review article aims to describe and discuss the optimal utilization of CT and MRI for assessing the response of NETs to treatment; it provides a comprehensive overview of established and emerging criteria for evaluating tumor response, along with comparative analyses. Molecular imaging will not be addressed here and is covered in a dedicated article within this special issue.
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Affiliation(s)
- Maxime Ronot
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France.
| | - Marco Dioguardi Burgio
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Jules Gregory
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Radiology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Olivia Hentic
- Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
| | | | - Philippe Ruszniewski
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
| | - Magaly Zappa
- Department of Radiology, Cayenne University Hospital, Cayenne, Guyanne, France
| | - Louis de Mestier
- Université Paris-Cité, Centre de Recherche sur l'Inflammation, INSERM UMR1149, FHU MOSAIC, Paris, France; Université Paris-Cité, Department of Pancreatology and Digestive Oncology, Beaujon Hospital (APHP.Nord), Clichy, France
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21
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Huff DT, Santoro-Fernandes V, Chen S, Chen M, Kashuk C, Weisman AJ, Jeraj R, Perk TG. Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability. Phys Med Biol 2023; 68:175031. [PMID: 37567220 PMCID: PMC10461173 DOI: 10.1088/1361-6560/acef8f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/25/2023] [Accepted: 08/11/2023] [Indexed: 08/13/2023]
Abstract
Objective.Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients.Approach.Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10-29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences.Main results.The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p> 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p< 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 minSignificance.The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.
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Affiliation(s)
- Daniel T Huff
- AIQ Solutions, Madison, WI, United States of America
| | - Victor Santoro-Fernandes
- University of Wisconsin-Madison, Department of Medical Physics, Madison, WI, United States of America
| | - Song Chen
- The First Hospital of China Medical University, Department of Nuclear Medicine, Shenyang, Liaoning, CN, People’s Republic of China
| | - Meijie Chen
- The First Hospital of China Medical University, Department of Nuclear Medicine, Shenyang, Liaoning, CN, People’s Republic of China
| | - Carl Kashuk
- AIQ Solutions, Madison, WI, United States of America
| | - Amy J Weisman
- AIQ Solutions, Madison, WI, United States of America
| | - Robert Jeraj
- University of Wisconsin-Madison, Department of Medical Physics, Madison, WI, United States of America
- University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, SI, Slovenia
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22
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He J, Li W, Zhou J, Sun H, Zhou C, Liu Y, Quan T, Fan W, Pan Z, Lin J, Peng J. Evaluation of total tumor volume reduction ratio in initially unresectable colorectal liver metastases after first-line systemic treatment. Eur J Radiol 2023; 165:110950. [PMID: 37437437 DOI: 10.1016/j.ejrad.2023.110950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 05/30/2023] [Accepted: 06/22/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE Total tumor volume (TTV) may play an essential role in the estimation of tumor burden. This study is aimed to investigate the clinical value of the reduction ratio of TTV as a valuable indicator of clinical outcomes in patients with colorectal liver metastases (CRLM). METHODS A total of 240 initially unresectable CRLM patients who underwent first-line systemic treatment were enrolled in this study. TTV at baseline and at the end of first-line treatment were assessed using a three-dimensional reconstruction system according to CT or MRI images. Survival was evaluated using Kaplan-Meier analysis and compared using Cox proportional hazard ratios (HR). RESULTS A total of 212 (88.3%) patients achieved tumor regression with a median reduction ratio of TTV of 86.0%. An increasing reduction ratio of TTV was associated with a gradually ascending successful conversion outcome. Patients with a reduction ratio >86.0% had better survival than those with a reduction ratio 0-86.0% or <0 (5-year overall survival (OS) rates, 64.4% vs. 44.9% vs. 23.5%, P < 0.001; 5-year progression-free survival (PFS) rates, 36.3% vs. 28.2% vs. 6.5%, P < 0.001). Multivariate analysis indicated that the reduction ratio of TTV ≤ 86.0% (OR [95%CI]: 4.956 [2.654-9.253], P < 0.001) was an independent factor for conversion failure outcome. Cox analyses revealed that the reduction ratio of TTV ≤ 86.0% was an independent factor for both unfavorable OS (HR [95%CI]: 2.216 [1.332-3.688], P = 0.002) and PFS (HR [95%CI]: 2.023 [1.376-2.974], P < 0.001). CONCLUSIONS The reduction ratio of TTV was an effective indicator for conversion outcome and long-term prognosis in patients with initially unresectable CRLM after first-line systemic treatment.
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Affiliation(s)
- Jiarui He
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, PR China.
| | - Weihao Li
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
| | - Jian Zhou
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China; South China Hospital, Medical School, Shenzhen University, Shenzhen 518116, PR China.
| | - Hui Sun
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, PR China.
| | - Chi Zhou
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
| | - Yujun Liu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, PR China.
| | - Tingting Quan
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, PR China.
| | - Wenhua Fan
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
| | - Zhizhong Pan
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
| | - Junzhong Lin
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
| | - Jianhong Peng
- Department of Colorectal Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, PR China.
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23
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Gouel P, Callonnec F, Levêque É, Valet C, Blôt A, Cuvelier C, Saï S, Saunier L, Pepin LF, Hapdey S, Libraire J, Vera P, Viard B. Evaluation of the capability and reproducibility of RECIST 1.1. measurements by technologists in breast cancer follow-up: a pilot study. Sci Rep 2023; 13:9148. [PMID: 37277412 DOI: 10.1038/s41598-023-36315-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023] Open
Abstract
The evaluation of tumor follow-up according to RECIST 1.1 has become essential in clinical practice given its role in therapeutic decision making. At the same time, radiologists are facing an increase in activity while facing a shortage. Radiographic technologists could contribute to the follow-up of these measures, but no studies have evaluated their ability to perform them. Ninety breast cancer patients were performed three CT follow-ups between September 2017 and August 2021. 270 follow-up treatment CT scans were analyzed including 445 target lesions. The rate of agreement of classifications RECIST 1.1 between five technologists and radiologists yielded moderate (k value between 0.47 and 0.52) and substantial (k value = 0.62 and k = 0.67) agreement values. 112 CT were classified as progressive disease (PD) by the radiologists, and 414 new lesions were identified. The analysis showed a percentage of strict agreement of progressive disease classification between reader-technologists and radiologists ranging from substantial to almost perfect agreement (range 73-97%). Analysis of intra-observer agreement was strong at almost perfect (k > 0.78) for 3 technologists. These results are encouraging regarding the ability of selected technologists to perform measurements according to RECIST 1.1 criteria by CT scan with good identification of disease progression.
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Affiliation(s)
- Pierrick Gouel
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France.
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France.
| | - Françoise Callonnec
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Émilie Levêque
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Céline Valet
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Axelle Blôt
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Clémence Cuvelier
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Sonia Saï
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Lucie Saunier
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Louis-Ferdinand Pepin
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Sébastien Hapdey
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France
| | - Julie Libraire
- Department of Statistics and Clinical Research Unit, Henri Becquerel Cancer Center, Rouen, Normandy, France
| | - Pierre Vera
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
- QuantIF-LITIS EA4108, University of Rouen, Rouen, Normandy, France
| | - Benjamin Viard
- Department of Medical Imaging, Henri Becquerel Cancer Center, Rouen, Normandy, France
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Iannessi A, Beaumont H. Breaking down the RECIST 1.1 double read variability in lung trials: What do baseline assessments tell us? Front Oncol 2023; 13:988784. [PMID: 37007064 PMCID: PMC10060958 DOI: 10.3389/fonc.2023.988784] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 02/03/2023] [Indexed: 03/18/2023] Open
Abstract
BackgroundIn clinical trials with imaging, Blinded Independent Central Review (BICR) with double reads ensures data blinding and reduces bias in drug evaluations. As double reads can cause discrepancies, evaluations require close monitoring which substantially increases clinical trial costs. We sought to document the variability of double reads at baseline, and variabilities across individual readers and lung trials.Material and methodsWe retrospectively analyzed data from five BICR clinical trials evaluating 1720 lung cancer patients treated with immunotherapy or targeted therapy. Fifteen radiologists were involved. The variability was analyzed using a set of 71 features derived from tumor selection, measurements, and disease location. We selected a subset of readers that evaluated ≥50 patients in ≥two trials, to compare individual reader’s selections. Finally, we evaluated inter-trial homogeneity using a subset of patients for whom both readers assessed the exact same disease locations. Significance level was 0.05. Multiple pair-wise comparisons of continuous variables and proportions were performed using one-way ANOVA and Marascuilo procedure, respectively.ResultsAcross trials, on average per patient, target lesion (TL) number ranged 1.9 to 3.0, sum of tumor diameter (SOD) 57.1 to 91.9 mm. MeanSOD=83.7 mm. In four trials, MeanSOD of double reads was significantly different. Less than 10% of patients had TLs selected in completely different organs and 43.5% had at least one selected in different organs. Discrepancies in disease locations happened mainly in lymph nodes (20.1%) and bones (12.2%). Discrepancies in measurable disease happened mainly in lung (19.6%). Between individual readers, the MeanSOD and disease selection were significantly different (p<0.001). In inter-trials comparisons, on average per patient, the number of selected TLs ranged 2.1 to 2.8, MeanSOD 61.0 to 92.4 mm. Trials were significantly different in MeanSOD (p<0.0001) and average number of selected TLs (p=0.007). The proportion of patients having one of the top diseases was significantly different only between two trials for lung. Significant differences were observed for all other disease locations (p<0.05).ConclusionsWe found significant double read variabilities at baseline, evidence of reading patterns and a means to compare trials. Clinical trial reliability is influenced by the interplay of readers, patients and trial design.
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Jain M, Tivtikyan A, Kamalov D, Avdonin S, Rakhmatullin T, Pisarev E, Zvereva M, Samokhodskaya L, Kamalov A. Development of a Sensitive Digital Droplet PCR Screening Assay for the Detection of GPR126 Non-Coding Mutations in Bladder Cancer Urine Liquid Biopsies. Biomedicines 2023; 11:495. [PMID: 36831030 PMCID: PMC9953558 DOI: 10.3390/biomedicines11020495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023] Open
Abstract
Recent whole-genome sequencing studies identified two novel recurrent mutations in the enhancer region of GPR126 in urothelial bladder cancer (UBC) tumor samples. This mutational hotspot is the second most common after the TERT promoter in UBC. The aim of the study was to develop a digital droplet PCR screening assay for the simultaneous detection of GPR126 mutations in a single tube. Its performance combined with TERT promoter mutation analysis was evaluated in urine of healthy volunteers (n = 50) and patients with cystitis (n = 22) and UBC (n = 70). The developed assay was validated using DNA constructs carrying the studied variants. None of the mutations were detected in control and cystitis group samples. GPR126 mutations were observed in the urine of 25/70 UBC patients (area under the ROC curve (AUC) of 0.679; mutant allele fraction (MAF) of 21.61 [8.30-44.52] %); TERT mutations-in 40/70 (AUC of 0.786; MAF = 28.29 [19.03-38.08] %); ≥1 mutation-in 47/70 (AUC of 0.836)). The simultaneous presence of GPR126 and TERT mutations was observed in 18/70 cases, with no difference in MAFs for the paired samples (31.96 [14.78-47.49] % vs. 27.13 [17.00-37.62] %, p = 0.349, respectively). The combined analysis of these common non-coding mutations in urine allows the sensitive and non-invasive detection of UBC.
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Affiliation(s)
- Mark Jain
- Medical Research and Educational Center, Lomonosov Moscow State University, 119992 Moscow, Russia
| | - Alexander Tivtikyan
- Medical Research and Educational Center, Lomonosov Moscow State University, 119992 Moscow, Russia
| | - David Kamalov
- Medical Research and Educational Center, Lomonosov Moscow State University, 119992 Moscow, Russia
| | - Savva Avdonin
- Department of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Tagir Rakhmatullin
- Department of Fundamental Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Eduard Pisarev
- Department of Bioinformatics and Bioengineering, Lomonosov Moscow State University, 119991 Moscow, Russia
- Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Maria Zvereva
- Department of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Larisa Samokhodskaya
- Medical Research and Educational Center, Lomonosov Moscow State University, 119992 Moscow, Russia
| | - Armais Kamalov
- Medical Research and Educational Center, Lomonosov Moscow State University, 119992 Moscow, Russia
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Choi JW, Dean EA, Lu H, Thompson Z, Qi J, Krivenko G, Jain MD, Locke FL, Balagurunathan Y. Repeatability of metabolic tumor burden and lesion glycolysis between clinical readers. Front Immunol 2023; 14:994520. [PMID: 36875072 PMCID: PMC9975754 DOI: 10.3389/fimmu.2023.994520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 01/10/2023] [Indexed: 02/17/2023] Open
Abstract
The Metabolic Tumor Volume (MTV) and Tumor Lesion Glycolysis (TLG) has been shown to be independent prognostic predictors for clinical outcome in Diffuse Large B-cell Lymphoma (DLBCL). However, definitions of these measurements have not been standardized, leading to many sources of variation, operator evaluation continues to be one major source. In this study, we propose a reader reproducibility study to evaluate computation of TMV (& TLG) metrics based on differences in lesion delineation. In the first approach, reader manually corrected regional boundaries after automated detection performed across the lesions in a body scan (Reader M using a manual process, or manual). The other reader used a semi-automated method of lesion identification, without any boundary modification (Reader A using a semi- automated process, or auto). Parameters for active lesion were kept the same, derived from standard uptake values (SUVs) over a 41% threshold. We systematically contrasted MTV & TLG differences between expert readers (Reader M & A). We find that MTVs computed by Readers M and A were both concordant between them (concordant correlation coefficient of 0.96) and independently prognostic with a P-value of 0.0001 and 0.0002 respectively for overall survival after treatment. Additionally, we find TLG for these reader approaches showed concordance (CCC of 0.96) and was prognostic for over -all survival (p ≤ 0.0001 for both). In conclusion, the semi-automated approach (Reader A) provides acceptable quantification & prognosis of tumor burden (MTV) and TLG in comparison to expert reader assisted measurement (Reader M) on PET/CT scans.
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Affiliation(s)
- Jung W Choi
- Department of Diagnostic Imaging and Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL, United States
| | - Erin A Dean
- Blood and Marrow Transplant and Cellular Immunotherapy, H. Lee. Moffitt Cancer Center, Tampa, FL, United States.,Division of Hematology and Oncology, University of Florida, Gainesville, FL, , United States
| | - Hong Lu
- Cancer Physiology, H. Lee. Moffitt Cancer Center, Tampa, FL, United States.,Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Zachary Thompson
- Biostatistics & Bioinformatics, H. Lee. Moffitt Cancer Center, Tampa, FL, United States
| | - Jin Qi
- Cancer Physiology, H. Lee. Moffitt Cancer Center, Tampa, FL, United States
| | - Gabe Krivenko
- Blood and Marrow Transplant and Cellular Immunotherapy, H. Lee. Moffitt Cancer Center, Tampa, FL, United States
| | - Michael D Jain
- Blood and Marrow Transplant and Cellular Immunotherapy, H. Lee. Moffitt Cancer Center, Tampa, FL, United States
| | - Frederick L Locke
- Blood and Marrow Transplant and Cellular Immunotherapy, H. Lee. Moffitt Cancer Center, Tampa, FL, United States
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27
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Liu X, Wang R, Zhu Z, Wang K, Gao Y, Li J, Zhang Y, Wang X, Zhang X, Wang X. Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria. BMC Cancer 2022; 22:1285. [PMID: 36476181 PMCID: PMC9730687 DOI: 10.1186/s12885-022-10366-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 11/24/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and improve the efficiency of imaging interpretation. OBJECTIVE To develop an automated algorithm for segmentation of liver metastases based on a deep learning method and assess its efficacy for treatment response assessment according to the RECIST 1.1 criteria. METHODS One hundred and sixteen treated patients with clinically confirmed liver metastases were enrolled. All patients had baseline and post-treatment MR images. They were divided into an initial (n = 86) and validation cohort (n = 30) according to the examined time. The metastatic foci on DWI images were annotated by two researchers in consensus. Then the treatment responses were assessed by the two researchers according to RECIST 1.1 criteria. A 3D U-Net algorithm was trained for automated liver metastases segmentation using the initial cohort. Based on the segmentation of liver metastases, the treatment response was assessed automatically with a rule-based program according to the RECIST 1.1 criteria. The segmentation performance was evaluated using the Dice similarity coefficient (DSC), volumetric similarity (VS), and Hausdorff distance (HD). The area under the curve (AUC) and Kappa statistics were used to assess the accuracy and consistency of the treatment response assessment by the deep learning model and compared with two radiologists [attending radiologist (R1) and fellow radiologist (R2)] in the validation cohort. RESULTS In the validation cohort, the mean DSC, VS, and HD were 0.85 ± 0.08, 0.89 ± 0.09, and 25.53 ± 12.11 mm for the liver metastases segmentation. The accuracies of R1, R2 and automated segmentation-based assessment were 0.77, 0.65, and 0.74, respectively, and the AUC values were 0.81, 0.73, and 0.83, respectively. The consistency of treatment response assessment based on automated segmentation and manual annotation was moderate [K value: 0.60 (0.34-0.84)]. CONCLUSION The deep learning-based liver metastases segmentation was capable of evaluating treatment response according to RECIST 1.1 criteria, with comparable results to the junior radiologist and superior to that of the fellow radiologist.
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Affiliation(s)
- Xiang Liu
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Rui Wang
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Zemin Zhu
- Department of Hepatobiliary and Pancreatic Surgery, Zhuzhou Central Hospital, Zhuzhou, 412000, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China
| | - Yue Gao
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Jialun Li
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 100011, China
| | - Yaofeng Zhang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 100011, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, 100011, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China.
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28
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Sedlaczek OL, Kleesiek J, Gallagher FA, Murray J, Prinz S, Perez-Lopez R, Sala E, Caramella C, Diffetock S, Lassau N, Priest AN, Suzuki C, Vargas R, Giandini T, Vaiani M, Messina A, Blomqvist LK, Beets-Tan RGH, Oberrauch P, Schlemmer HP, Bach M. Quantification and reduction of cross-vendor variation in multicenter DWI MR imaging: results of the Cancer Core Europe imaging task force. Eur Radiol 2022; 32:8617-8628. [PMID: 35678860 PMCID: PMC9705481 DOI: 10.1007/s00330-022-08880-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 03/25/2022] [Accepted: 05/12/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES In the Cancer Core Europe Consortium (CCE), standardized biomarkers are required for therapy monitoring oncologic multicenter clinical trials. Multiparametric functional MRI and particularly diffusion-weighted MRI offer evident advantages for noninvasive characterization of tumor viability compared to CT and RECIST. A quantification of the inter- and intraindividual variation occurring in this setting using different hardware is missing. In this study, the MRI protocol including DWI was standardized and the residual variability of measurement parameters quantified. METHODS Phantom and volunteer measurements (single-shot T2w and DW-EPI) were performed at the seven CCE sites using the MR hardware produced by three different vendors. Repeated measurements were performed at the sites and across the sites including a traveling volunteer, comparing qualitative and quantitative ROI-based results including an explorative radiomics analysis. RESULTS For DWI/ADC phantom measurements using a central post-processing algorithm, the maximum deviation could be decreased to 2%. However, there is no significant difference compared to a decentralized ADC value calculation at the respective MRI devices. In volunteers, the measurement variation in 2 repeated scans did not exceed 11% for ADC and is below 20% for single-shot T2w in systematic liver ROIs. The measurement variation between sites amounted to 20% for ADC and < 25% for single-shot T2w. Explorative radiomics classification experiments yield better results for ADC than for single-shot T2w. CONCLUSION Harmonization of MR acquisition and post-processing parameters results in acceptable standard deviations for MR/DW imaging. MRI could be the tool in oncologic multicenter trials to overcome the limitations of RECIST-based response evaluation. KEY POINTS • Harmonizing acquisition parameters and post-processing homogenization, standardized protocols result in acceptable standard deviations for multicenter MR-DWI studies. • Total measurement variation does not to exceed 11% for ADC in repeated measurements in repeated MR acquisitions, and below 20% for an identical volunteer travelling between sites. • Radiomic classification experiments were able to identify stable features allowing for reliable discrimination of different physiological tissue samples, even when using heterogeneous imaging data.
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Affiliation(s)
- Oliver Lukas Sedlaczek
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Division of Translational Medical Oncology, National Center for Tumor Diseases Heidelberg (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- Department of Radiology, University Hospital Heidelberg, Heidelberg, Germany.
| | - Jens Kleesiek
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | | | - Jacob Murray
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Sebastian Prinz
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Raquel Perez-Lopez
- Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Evia Sala
- Department of Radiology, University of Cambridge and Cancer Research UK Cambridge Centre, Cambridge, UK
| | - Caroline Caramella
- Imaging Department, Gustave Roussy, BIOMAPS, UMR1281, INSERM, CEA, CNRS, Université Paris Saclay, Villejuif, Paris, France
| | - Sebastian Diffetock
- Imaging Department, Gustave Roussy, BIOMAPS, UMR1281, INSERM, CEA, CNRS, Université Paris Saclay, Villejuif, Paris, France
| | - Nathalie Lassau
- Imaging Department, Gustave Roussy, BIOMAPS, UMR1281, INSERM, CEA, CNRS, Université Paris Saclay, Villejuif, Paris, France
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge, UK
| | - Chikako Suzuki
- Department of Radiation Physics and Nuclear Medicine, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden
| | - Roberto Vargas
- Department of Radiology, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden
| | - Tommaso Giandini
- Medical Physics Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Marta Vaiani
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Antonella Messina
- Department of Radiology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Lennart K Blomqvist
- Department of Radiation Physics and Nuclear Medicine, Karolinska University Hospital and Department of Molecular Medicine and Surgery, Karolinska Institutet, Solna, Sweden
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra Oberrauch
- Division of Translational Medical Oncology, National Center for Tumor Diseases Heidelberg (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Division of Translational Medical Oncology, National Center for Tumor Diseases Heidelberg (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Michael Bach
- Department of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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Olsen EA, Whittaker S, Willemze R, Pinter-Brown L, Foss F, Geskin L, Schwartz L, Horwitz S, Guitart J, Zic J, Kim YH, Wood GS, Duvic M, Ai W, Girardi M, Gru A, Guenova E, Hodak E, Hoppe R, Kempf W, Kim E, Lechowicz MJ, Ortiz-Romero P, Papadavid E, Quaglino P, Pittelkow M, Prince HM, Sanches JA, Sugaya M, Vermeer M, Zain J, Knobler R, Stadler R, Bagot M, Scarisbrick J. Primary cutaneous lymphoma: recommendations for clinical trial design and staging update from the ISCL, USCLC, and EORTC. Blood 2022; 140:419-437. [PMID: 34758074 PMCID: PMC9353153 DOI: 10.1182/blood.2021012057] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 10/15/2021] [Indexed: 11/20/2022] Open
Abstract
The number of patients with primary cutaneous lymphoma (PCL) relative to other non-Hodgkin lymphomas (NHLs) is small and the number of subtypes large. Although clinical trial guidelines have been published for mycosis fungoides/Sézary syndrome, the most common type of PCL, none exist for the other PCLs. In addition, staging of the PCLs has been evolving based on new data on potential prognostic factors, diagnosis, and assessment methods of both skin and extracutaneous disease and a desire to align the latter with the Lugano guidelines for all NHLs. The International Society for Cutaneous Lymphomas (ISCL), the United States Cutaneous LymphomaConsortium (USCLC), and the Cutaneous Lymphoma Task Force of the European Organization for the Research and Treatment of Cancer (EORTC) now propose updated staging and guidelines for the study design, assessment, endpoints, and response criteria in clinical trials for all the PCLs in alignment with that of the Lugano guidelines. These recommendations provide standardized methodology that should facilitate planning and regulatory approval of new treatments for these lymphomas worldwide, encourage cooperative investigator-initiated trials, and help to assess the comparative efficacy of therapeutic agents tested across sites and studies.
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Affiliation(s)
- Elise A Olsen
- Department of Dermatology and Department of Medicine, Division of Hematologic Malignancies, Duke University Medical Center, Durham, NC
| | - Sean Whittaker
- School of Basic and Medical Biosciences, Kings College London and St. Johns Institute of Dermatology, London, United Kingdom
| | - Rein Willemze
- Department of Dermatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lauren Pinter-Brown
- Departments of Medicine and Dermatology, Chao Family Comprehensive Cancer Center, University of California-Irvine, Irvine, CA
| | - Francine Foss
- Hematology and Stem Cell Transplantation, Yale University School of Medicine, New Haven, CT
| | - Larisa Geskin
- Department of Dermatology, Columbia University Medical Center, New York, NY
| | - Lawrence Schwartz
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Steven Horwitz
- Department of Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY
| | - Joan Guitart
- Departments of Dermatology and Pathology, Northwestern University, Chicago, IL
| | - John Zic
- Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN
| | - Youn H Kim
- Department of Dermatology, Stanford University School of Medicine and Stanford Cancer Institute, Stanford, CA
| | - Gary S Wood
- Department of Dermatology, University of Wisconsin-Madison, Madison, WI
| | - Madeleine Duvic
- University of Texas MD Anderson Cancer Center, Dermatology Unit, Houston, TX
| | - Wei Ai
- Department of Medicine, Division of Hematology and Oncology, University of California, San Francisco, CA
| | - Michael Girardi
- Department of Dermatology, Yale School of Medicine, New Haven, CT
| | - Alejandro Gru
- Divisions of Dermatopathology and Hematopathology, Department of Pathology, Emily Couric Clinical Cancer Center, University of Virginia, Charlottesville, VA
| | - Emmanuella Guenova
- Department of Dermatology, Lausanne University Hospital, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Emmilia Hodak
- Division of Dermatology, Rabin Medical Center, Beilinson Hospital, Tel Aviv University, Tel Aviv, Israel
| | - Richard Hoppe
- Department of Radiation Oncology, Stanford University, Stanford, CA
| | - Werner Kempf
- Department of Dermatology, University Hospital Zurich and Kempf and Pfaltz Histologische Diagnostik, Zurich, Switzerland
| | - Ellen Kim
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mary Jo Lechowicz
- Department of Hematology and Medical Oncology, Emory University School of Medicine and Winship Cancer Institute, Atlanta, GA
| | - Pablo Ortiz-Romero
- Department of Dermatology, Hospital Universitario 12 de Octubre, i+12 Institute, CIBERONC, Medical School, Universidad Complutense, Madrid, Spain
| | - Evangelia Papadavid
- Department of Dermatology-Venereology, National and Kapodistrian University of Athens, Athens, Greece
| | - Pietro Quaglino
- Dermatologic Clinic, Department of Medical Sciences, University of Turin Medical School, Turin, Italy
| | - Mark Pittelkow
- Department of Dermatology, Mayo Clinic Arizona, Scottsdale, AZ
| | - H Miles Prince
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria, Australia
| | - Jose Antonio Sanches
- Department of Dermatology, University of Sao Paulo Medical School, São Paulo, Brazil
| | - Makoto Sugaya
- Department of Dermatology, International University of Health and Welfare, Chiba, Japan
| | - Maarten Vermeer
- Department of Dermatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jasmine Zain
- Department of Hematology and Hematopoetic Stem Cell Transplantation, City of Hope National Medical Center, Duarte, CA
| | - Robert Knobler
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Rudolf Stadler
- University Clinic for Dermatology, Johannes Wesling Medical Centre, University of Bochum, Minden, Germany
| | - Martine Bagot
- Department of Dermatology, Université de Paris, AP-HP, Hôpital Saint-Louis, Paris, France; and
| | - Julia Scarisbrick
- Department of Dermatology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
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30
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He LN, Chen T, Fu S, Chen C, Jiang Y, Zhang X, Du W, Li H, Wang Y, Ali WAS, Zhou Y, Lin Z, Yang Y, Huang Y, Zhao H, Fang W, Zhang L, Hong S. Reducing number of target lesions for RECIST1.1 to predict survivals in patients with advanced non-small-cell lung cancer undergoing anti-PD1/PD-L1 monotherapy. Lung Cancer 2022; 165:10-17. [PMID: 35051754 DOI: 10.1016/j.lungcan.2021.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/30/2021] [Accepted: 12/22/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES The Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 provides conventional and standardized response assessment for multiple solid tumors. We investigated the smallest number of target lesions that can be measured without compromising response categorization and survival prediction in patients with advanced non-small-cell lung cancer (aNSCLC) undergoing anti-PD-1/PD-L1 monotherapy. MATERIAL AND METHODS 125 aNSCLC patients with at least two measurable lesions undergoing PD-1/PD-L1 inhibitor treatment were retrospectively studied. Tumor measurements allowing up to two lesions per organ and five lesions in total were reviewed. Inter-individual agreement and κ values for inter-method concordance on response status were evaluated based on up to five target lesions versus the largest one through four lesions. C-index was calculated to evaluate the prognostic accuracy of response categorization based on the selected number of target lesions for predicting overall survival (OS). Cox regression analysis was conducted for survival analysis. RESULTS The highly consistent response assignment (99.2%) could be obtained when measuring the largest two lesions versus up to five lesions. Using the largest two through four lesions produced κ values of 0.986, 1.000 and 1.000 for response assessment, values significantly higher than those obtained when measuring the largest single lesion (κ = 0.850). C-index for overall survival (OS) was similar when assessing the largest one through five lesions, ranging from 0.646 to 0.654. Cox regression analyses showed that radiological response significantly predicted OS, irrespective of the number of target lesions selected. CONCLUSIONS Reducing the number of target lesions does not affect OS prediction in aNSCLC patients treated with anti-PD-1/PD-L1 therapy. Considering the high intra-individual and inter-method concordance, using the largest two lesions in total is proposed to assess response.
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Affiliation(s)
- Li-Na He
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Tao Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sha Fu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Pathology Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chen Chen
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yongluo Jiang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xuanye Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wei Du
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Haifeng Li
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixing Wang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wael Abdullah Sultan Ali
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yixin Zhou
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of VIP Region, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zuan Lin
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yunpeng Yang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yan Huang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Hongyun Zhao
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Clinical Research, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Wenfeng Fang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Zhang
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Shaodong Hong
- State Key Laboratory of Oncology in South China, Guangzhou, China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, China; Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China.
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31
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Johnson BE, Creason AL, Stommel JM, Keck JM, Parmar S, Betts CB, Blucher A, Boniface C, Bucher E, Burlingame E, Camp T, Chin K, Eng J, Estabrook J, Feiler HS, Heskett MB, Hu Z, Kolodzie A, Kong BL, Labrie M, Lee J, Leyshock P, Mitri S, Patterson J, Riesterer JL, Sivagnanam S, Somers J, Sudar D, Thibault G, Weeder BR, Zheng C, Nan X, Thompson RF, Heiser LM, Spellman PT, Thomas G, Demir E, Chang YH, Coussens LM, Guimaraes AR, Corless C, Goecks J, Bergan R, Mitri Z, Mills GB, Gray JW. An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer. Cell Rep Med 2022; 3:100525. [PMID: 35243422 PMCID: PMC8861971 DOI: 10.1016/j.xcrm.2022.100525] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/15/2021] [Accepted: 01/19/2022] [Indexed: 12/15/2022]
Abstract
Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.
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Affiliation(s)
- Brett E. Johnson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Allison L. Creason
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jayne M. Stommel
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jamie M. Keck
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Swapnil Parmar
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Courtney B. Betts
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Aurora Blucher
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher Boniface
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Elmar Bucher
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Erik Burlingame
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Todd Camp
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Koei Chin
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jennifer Eng
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joseph Estabrook
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Heidi S. Feiler
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Michael B. Heskett
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Zhi Hu
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Annette Kolodzie
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Ben L. Kong
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pharmacy Services, Oregon Health & Science University, Portland, OR 97239, USA
| | - Marilyne Labrie
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jinho Lee
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Patrick Leyshock
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Souraya Mitri
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Janice Patterson
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jessica L. Riesterer
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Multiscale Microscopy Core, Oregon Health & Science University, Portland, OR 97239, USA
| | - Shamilene Sivagnanam
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Julia Somers
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, OR 97239, USA
| | - Guillaume Thibault
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Benjamin R. Weeder
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christina Zheng
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Xiaolin Nan
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR 97239, USA
| | - Reid F. Thompson
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, OR 97239, USA
| | - Laura M. Heiser
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Paul T. Spellman
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - George Thomas
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Emek Demir
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
- Computational Biology Program, Oregon Health & Science University, Portland, OR 97239, USA
| | - Lisa M. Coussens
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Alexander R. Guimaraes
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Christopher Corless
- Department of Pharmacy Services, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Pathology & Laboratory Medicine, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jeremy Goecks
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
| | - Raymond Bergan
- Fred & Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Zahi Mitri
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Medicine, Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Gordon B. Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Joe W. Gray
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA
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Li H, Shen J, Shou J, Han W, Gong L, Xu Y, Chen P, Wang K, Zhang S, Sun C, Zhang J, Niu Z, Pan H, Cai W, Fang Y. Exploring the Interobserver Agreement in Computer-Aided Radiologic Tumor Measurement and Evaluation of Tumor Response. Front Oncol 2022; 11:691638. [PMID: 35174064 PMCID: PMC8841678 DOI: 10.3389/fonc.2021.691638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 12/31/2021] [Indexed: 12/03/2022] Open
Abstract
The accurate, objective, and reproducible evaluation of tumor response to therapy is indispensable in clinical trials. This study aimed at investigating the reliability and reproducibility of a computer-aided contouring (CAC) tool in tumor measurements and its impact on evaluation of tumor response in terms of RECIST 1.1 criteria. A total of 200 cancer patients were retrospectively collected in this study, which were randomly divided into two sets of 100 patients for experiential learning and testing. A total of 744 target lesions were identified by a senior radiologist in distinctive body parts, of which 278 lesions were in data set 1 (learning set) and 466 lesions were in data set 2 (testing set). Five image analysts were respectively instructed to measure lesion diameter using manual and CAC tools in data set 1 and subsequently tested in data set 2. The interobserver variability of tumor measurements was validated by using the coefficient of variance (CV), the Pearson correlation coefficient (PCC), and the interobserver correlation coefficient (ICC). We verified that the mean CV of manual measurement remained constant between the learning and testing data sets (0.33 vs. 0.32, p = 0.490), whereas it decreased for the CAC measurements after learning (0.24 vs. 0.19, p < 0.001). The interobserver measurements with good agreement (CV < 0.20) were 29.9% (manual) vs. 49.0% (CAC) in the learning set (p < 0.001) and 30.9% (manual) vs. 64.4% (CAC) in the testing set (p < 0.001). The mean PCCs were 0.56 ± 0.11 mm (manual) vs. 0.69 ± 0.10 mm (CAC) in the learning set (p = 0.013) and 0.73 ± 0.07 mm (manual) vs. 0.84 ± 0.03 mm (CAC) in the testing set (p < 0.001). ICCs were 0.633 (manual) vs. 0.698 (CAC) in the learning set (p < 0.001) and 0.716 (manual) vs. 0.824 (CAC) in the testing set (p < 0.001). The Fleiss’ kappa analysis revealed that the overall agreement was 58.7% (manual) vs. 58.9% (CAC) in the learning set and 62.9% (manual) vs. 74.5% (CAC) in the testing set. The 80% agreement of tumor response evaluation was 55.0% (manual) vs. 66.0% in the learning set and 60.6% (manual) vs. 79.7% (CAC) in the testing set. In conclusion, CAC can reduce the interobserver variability of radiological tumor measurements and thus improve the agreement of imaging evaluation of tumor response.
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Affiliation(s)
- Hongsen Li
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaying Shen
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiawei Shou
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weidong Han
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liu Gong
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yiming Xu
- Quantilogic Healthcare Zhejiang Co. Ltd, Hangzhou, China
| | - Peng Chen
- School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Kaixin Wang
- School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Shuangfeng Zhang
- School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Chao Sun
- School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Jie Zhang
- School of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhongfeng Niu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongming Pan
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yong Fang, ; Wenli Cai, ; Hongming Pan,
| | - Wenli Cai
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- *Correspondence: Yong Fang, ; Wenli Cai, ; Hongming Pan,
| | - Yong Fang
- Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Yong Fang, ; Wenli Cai, ; Hongming Pan,
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33
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Fournier L, de Geus-Oei LF, Regge D, Oprea-Lager DE, D’Anastasi M, Bidaut L, Bäuerle T, Lopci E, Cappello G, Lecouvet F, Mayerhoefer M, Kunz WG, Verhoeff JJC, Caruso D, Smits M, Hoffmann RT, Gourtsoyianni S, Beets-Tan R, Neri E, deSouza NM, Deroose CM, Caramella C. Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper. Front Oncol 2022; 11:800547. [PMID: 35083155 PMCID: PMC8784734 DOI: 10.3389/fonc.2021.800547] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 11/30/2021] [Indexed: 12/15/2022] Open
Abstract
Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.
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Affiliation(s)
- Laure Fournier
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Université de Paris, Assistance Publique–Hôpitaux de Paris (AP-HP), Hopital europeen Georges Pompidou, Department of Radiology, Paris Cardiovascular Research Center (PARCC) Unité Mixte de Recherche (UMRS) 970, Institut national de la santé et de la recherche médicale (INSERM), Paris, France
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, Netherlands
| | - Daniele Regge
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, Fondazione del Piemonte per l’Oncologia-Istituto Di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Turin, Italy
| | - Daniela-Elena Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers [Vrije Universiteit (VU) University], Amsterdam, Netherlands
| | - Melvin D’Anastasi
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Medical Imaging Department, Mater Dei Hospital, University of Malta, Msida, Malta
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, United Kingdom
| | - Tobias Bäuerle
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine Unit, Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS) – Humanitas Research Hospital, Milan, Italy
| | - Giovanni Cappello
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, Fondazione del Piemonte per l’Oncologia-Istituto Di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Turin, Italy
| | - Frederic Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Marius Mayerhoefer
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang G. Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, Germany
| | - Joost J. C. Verhoeff
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Damiano Caruso
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Marion Smits
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
- Brain Tumour Centre, Erasmus Medical Centre (MC) Cancer Institute, Rotterdam, Netherlands
| | - Ralf-Thorsten Hoffmann
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Institute and Policlinic for Diagnostic and Interventional Radiology, University Hospital, Carl-Gustav-Carus Technical University Dresden, Dresden, Germany
| | - Sofia Gourtsoyianni
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, Athens, Greece
| | - Regina Beets-Tan
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- School For Oncology and Developmental Biology (GROW) School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Emanuele Neri
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Diagnostic and Interventional Radiology, Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Nandita M. deSouza
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, United States
| | - Christophe M. Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine & Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph Centre International des Cancers Thoraciques, Université Paris-Saclay, Le Plessis-Robinson, France
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Foran DJ, Durbin EB, Chen W, Sadimin E, Sharma A, Banerjee I, Kurc T, Li N, Stroup AM, Harris G, Gu A, Schymura M, Gupta R, Bremer E, Balsamo J, DiPrima T, Wang F, Abousamra S, Samaras D, Hands I, Ward K, Saltz JH. An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities. J Pathol Inform 2022; 13:5. [PMID: 35136672 PMCID: PMC8794027 DOI: 10.4103/jpi.jpi_31_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features). MATERIALS AND METHODS As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated. RESULTS Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics. CONCLUSION To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.
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Affiliation(s)
- David J. Foran
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Eric B. Durbin
- Kentucky Cancer Registry, Markey Cancer Center, University of Kentucky, Lexington, KY, USA
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Wenjin Chen
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Evita Sadimin
- Center for Biomedical Informatics, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
- Department of Pathology and Laboratory Medicine, Rutgers-Robert Wood Johnson Medical School, Piscataway, NJ, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Imon Banerjee
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nan Li
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Antoinette M. Stroup
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Gerald Harris
- New Jersey State Cancer Registry, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA
| | - Annie Gu
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
| | - Maria Schymura
- New York State Cancer Registry, New York State Department of Health, Albany, NY, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Erich Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Balsamo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tammy DiPrima
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Feiqiao Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Shahira Abousamra
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Isaac Hands
- Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, Lexington, KY, USA
| | - Kevin Ward
- Georgia State Cancer Registry, Georgia Department of Public Health, Atlanta, GA, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
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Crombé A, Fadli D, Spinnato P, Michot A, Cousin S, Le Loarer F, Kind M. Natural speed of growth of untreated soft-tissue sarcomas: A dimension-based imaging analysis. Eur J Radiol 2021; 146:110082. [PMID: 34871937 DOI: 10.1016/j.ejrad.2021.110082] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/05/2021] [Accepted: 11/28/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE The interval from first symptoms to diagnosis, staging and referral to reference center can last months for soft-tissue sarcoma (STS) patients. Meanwhile, patients can undergo different imaging that capture the 'natural' tumor changes, before medical intervention. Aim was to depict these 'natural' dimensional variations and to correlate them with patients' outcome. METHODS Single-center retrospective study including all consecutive adults with newly-diagnosed STS, ≥2 pre-treatment imaging (CT-scan or MRI) on the tumor (Exam-0 and Exam-1), and managed in reference center between 2007 and 2018. Longest diameter (LD) and volume were calculated on both examinations to obtain the naïve dimensional growth before any intervention. SARCULATOR nomogram was applied on data at Exam-0 and Exam-1. Correlations with overall, metastatic and local relapse-free survivals (OS, MFS and LFS), and with pre-treatment pathological features were performed. RESULTS 137 patients were included (median age: 65 years). Average naïve growth was +39.4% in LD and +503% in volume during an average Exam-0-to-Exam-1 interval of 130 days. The 10-year distant metastasis and OS predictions were different at Exam-0 and Exam-1 (P < 0.0001 for both). All the changes in radiological measurements significantly correlated with pre-treatment number of mitosis, grade and complex genomic (P-value range: <0.0001-0.0481). Multivariate Cox modeling identified the relative change in LD/month and absolute change in LD/month as independent predictors for OS and LFS, respectively (P = 0.0003 and 0.0001, respectively). CONCLUSION When available, the natural speed of growth on pre-treatment imaging should be evaluated to improve the estimation of pre-treatment histological grade and patients' OS and LFS.
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Affiliation(s)
- Amandine Crombé
- Department of Oncologic Imaging, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France; University of Bordeaux, F-33000 Bordeaux, France; Models in Oncology (MONC) Team, INRIA Bordeaux Sud-Ouest, CNRS, UMR 5251, F-33405 Talence, France.
| | - David Fadli
- Department of Oncologic Imaging, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Audrey Michot
- University of Bordeaux, F-33000 Bordeaux, France; Department of Oncologic Surgery, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France
| | - Sophie Cousin
- Department of Medical Oncology, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France
| | - François Le Loarer
- University of Bordeaux, F-33000 Bordeaux, France; Department of Pathology, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institut, Regional Comprehensive Cancer Center of Bordeaux, F-33076 Bordeaux, France
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Wesdorp NJ, Bolhuis K, Roor J, van Waesberghe JHTM, van Dieren S, van Amerongen MJ, Chapelle T, Dejong CHC, Engelbrecht MRW, Gerhards MF, Grunhagen D, van Gulik TM, Hermans JJ, de Jong KP, Klaase JM, Liem MSL, van Lienden KP, Molenaar IQ, Patijn GA, Rijken AM, Ruers TM, Verhoef C, de Wilt JHW, Swijnenburg RJ, Punt CJA, Huiskens J, Kazemier G. The Prognostic Value of Total Tumor Volume Response Compared With RECIST1.1 in Patients With Initially Unresectable Colorectal Liver Metastases Undergoing Systemic Treatment. ANNALS OF SURGERY OPEN 2021; 2:e103. [PMID: 37637880 PMCID: PMC10455281 DOI: 10.1097/as9.0000000000000103] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/17/2021] [Indexed: 01/20/2023] Open
Abstract
Objectives Compare total tumor volume (TTV) response after systemic treatment to Response Evaluation Criteria in Solid Tumors (RECIST1.1) and assess the prognostic value of TTV change and RECIST1.1 for recurrence-free survival (RFS) in patients with colorectal liver-only metastases (CRLM). Background RECIST1.1 provides unidimensional criteria to evaluate tumor response to systemic therapy. Those criteria are accepted worldwide but are limited by interobserver variability and ignore potentially valuable information about TTV. Methods Patients with initially unresectable CRLM receiving systemic treatment from the randomized, controlled CAIRO5 trial (NCT02162563) were included. TTV response was assessed using software specifically developed together with SAS analytics. Baseline and follow-up computed tomography (CT) scans were used to calculate RECIST1.1 and TTV response to systemic therapy. Different thresholds (10%, 20%, 40%) were used to define response of TTV as no standard currently exists. RFS was assessed in a subgroup of patients with secondarily resectable CRLM after induction treatment. Results A total of 420 CT scans comprising 7820 CRLM in 210 patients were evaluated. In 30% to 50% (depending on chosen TTV threshold) of patients, discordance was observed between RECIST1.1 and TTV change. A TTV decrease of >40% was observed in 47 (22%) patients who had stable disease according to RECIST1.1. In 118 patients with secondarily resectable CRLM, RFS was shorter for patients with less than 10% TTV decrease compared with patients with more than 10% TTV decrease (P = 0.015), while RECIST1.1 was not prognostic (P = 0.821). Conclusions TTV response assessment shows prognostic potential in the evaluation of systemic therapy response in patients with CRLM.
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Affiliation(s)
- Nina J. Wesdorp
- From the Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Karen Bolhuis
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Joran Roor
- Department of Health, SAS Institute B.V., Huizen, The Netherlands
| | - Jan-Hein T. M. van Waesberghe
- Department of Radiology and Molecular Imaging, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Susan van Dieren
- Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Martin J. van Amerongen
- Department of Medical Imaging, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Thiery Chapelle
- Department of Hepatobiliary, Transplantation, and Endocrine Surgery, Antwerp University Hospital, Antwerp, Belgium
| | - Cornelis H. C. Dejong
- Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Surgery, Universitätsklinikum Aachen, Aachen, Germany
| | - Marc R. W. Engelbrecht
- Department of Radiology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Michael F. Gerhards
- Department of Surgery, Onze Lieve Vrouwe Gasthuis Hospital, Amsterdam, The Netherlands
| | - Dirk Grunhagen
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus University Medical Center Cancer Institute, Rotterdam, The Netherlands
| | - Thomas M. van Gulik
- Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - John J. Hermans
- Department of Medical Imaging, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Koert P. de Jong
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Joost M. Klaase
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mike S. L. Liem
- Department of Surgery, Medical Spectrum Twente, Enschede, The Netherlands
| | - Krijn P. van Lienden
- Department of Interventional Radiology, St Antonius Hospital, Nieuwegein, The Netherlands
| | - I. Quintus Molenaar
- Department of Surgery, Regional Academic Cancer Center Utrecht, University Medical Center Utrecht and St Antonius Hospital, Nieuwegein, The Netherlands
| | - Gijs A. Patijn
- Department of Surgery, Isala Hospital, Zwolle, The Netherlands
| | - Arjen M. Rijken
- Department of Surgery, Amphia Hospital, Breda, The Netherlands
| | - Theo M. Ruers
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Cornelis Verhoef
- Department of Surgical Oncology and Gastrointestinal Surgery, Erasmus University Medical Center Cancer Institute, Rotterdam, The Netherlands
| | - Johannes H. W. de Wilt
- Department of Surgery, Radboud University Medical Center, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Rutger-Jan Swijnenburg
- Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J. A. Punt
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joost Huiskens
- Department of Health, SAS Institute B.V., Huizen, The Netherlands
| | - Geert Kazemier
- From the Department of Surgery, Cancer Center Amsterdam, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Torres FS, Akbar S, Raman S, Yasufuku K, Schmidt C, Hosny A, Baldauf-Lenschen F, Leighl NB. End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography. JCO Clin Cancer Inform 2021; 5:1141-1150. [PMID: 34797702 DOI: 10.1200/cci.21.00096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification. METHODS We developed a fully automated imaging-based prognostication technique (IPRO) using deep learning to predict 1-year, 2-year, and 5-year mortality from pretreatment CTs of patients with stage I-IV lung cancer. Using six publicly available data sets from The Cancer Imaging Archive, we performed a retrospective five-fold cross-validation using pretreatment CTs of 1,689 patients, of whom 1,110 were diagnosed with non-small-cell lung cancer and had available TNM staging information. We compared the association of IPRO and TNM staging with patients' survival status and assessed an Ensemble risk score that combines IPRO and TNM staging. Finally, we evaluated IPRO's ability to stratify patients within TNM stages using hazard ratios (HRs) and Kaplan-Meier curves. RESULTS IPRO showed similar prognostic power (concordance index [C-index] 1-year: 0.72, 2-year: 0.70, 5-year: 0.68) compared with that of TNM staging (C-index 1-year: 0.71, 2-year: 0.71, 5-year: 0.70) in predicting 1-year, 2-year, and 5-year mortality. The Ensemble risk score yielded superior performance across all time points (C-index 1-year: 0.77, 2-year: 0.77, 5-year: 0.76). IPRO stratified patients within TNM stages, discriminating between highest- and lowest-risk quintiles in stages I (HR: 8.60), II (HR: 5.03), III (HR: 3.18), and IV (HR: 1.91). CONCLUSION Deep learning applied to pretreatment CT combined with TNM staging enhances prognostication and risk stratification in patients with lung cancer.
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Affiliation(s)
- Felipe Soares Torres
- Joint Department of Medical Imaging, Toronto General Hospital, Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Srinivas Raman
- Princess Margaret Cancer Centre, Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Kazuhiro Yasufuku
- Division of Thoracic Surgery, University Health Network and University of Toronto, Toronto, ON, Canada
| | | | - Ahmed Hosny
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.,Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | | | - Natasha B Leighl
- Department of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Medicine, University of Toronto, Toronto, ON, Canada
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Dual-Energy CT Vital Iodine Tumor Burden for Response Assessment in Patients With Metastatic GIST Undergoing TKI Therapy: Comparison to Standard CT and FDG PET/CT Criteria. AJR Am J Roentgenol 2021; 218:659-669. [PMID: 34668385 DOI: 10.2214/ajr.21.26636] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background: CT-based criteria for assessing gastroinstestinal stromal tumor (GIST) response to tyroskine kinase inhibitor (TKI) therapy are limited partly because tumor attenuation is influenced by treatment-related changes including hemorrhage and calcification. Iodine concentration may be less impacted by such changes. Objective: To determine whether DECT vital iodine tumor burden (TB) provides improved differentiation between responders and non-responders in patients with metastatic GIST undergoing TKI therapy compared to established CT and PET/CT criteria. Methods: An anthropomorphic phantom with spherical inserts mimicking GIST lesions of varying iodine concentrations and having non-enhancing central necrotic cores underwent DECT to determine a threshold iodine concentration. Forty patients (median age 57 years; 25 women, 15 men) treated with TKI for metaststic GIST were retrospectively evaluated. Patients underwent baseline and follow-up DECT and FDG PET/CT. Response assessment was performed using RECIST 1.1, modified Choi (mChoi), vascular tumor burden (VTB), DECT vital iodine TB, and European Organization for Research and Treatment of Cancer (EORTC PET) criteria. DECT vital iodine TB used the same percentage changes as RECIST 1.1 response categories. Progression-free survival (PFS) was compared between responders and non-responders for each response criteria using Cox proportional hazard ratios and Harrell's c-indices. Results: The phantom experiment identified a 0.5 mg/mL threshold to differentiate vital from non-vital tissue. Using DECT vital iodine TB, median PFS was significantly different between non-responders and responders (587 vs 167 days, respectively; p=.02). Hazard ratio for progression for DECT vital iodine TB non-responders versus responders was 6.9, versus 7.6 for EORTC PET, 3.3 for VTB, 2.3 for RECIST 1.1, and 2.1 for mChoi. C-index was 0.74 for EORTC PET, 0.73 for DECT vital iodine TB, 0.67 for VTB, 0.61 for RECIST 1.1, and 0.58 for mChoi. C-index was significantly greater for DECT vital iodine TB than RECIST 1.1 (p=.02) and mChoi (p=.002), but not different than VTB and EORTC PET (p>.05). Conclusion: DECT vital iodine TB criteria showed comparable performance as EORTC PET and outperformed RECIST 1.1 and mChoi for response assessment of metastatic GIST under TKI therapy. Clinical Impact: DECT vital iodine TB could help guide early management decisions in patients on TKI therapy.
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Beaumont H, Iannessi A, Wang Y, Voyton CM, Cillario J, Liu Y. Blinded Independent Central Review (BICR) in New Therapeutic Lung Cancer Trials. Cancers (Basel) 2021; 13:cancers13184533. [PMID: 34572761 PMCID: PMC8465869 DOI: 10.3390/cancers13184533] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Double reads in blinded independent central reviews (BICRs) are recommended to control the quality of trials but they are prone to discordances. We analyzed inter-reader discordances in a pool of lung cancer trials using RECIST 1.1. METHODS We analyzed six lung cancer BICR trials that included 1833 patients (10,684 time points) involving 17 radiologists. We analyzed the rate of discrepancy of each trial at the time-point and patient levels as well as testing inter-trial differences. The analysis of adjudication made it possible to compute the readers' endorsement rates, the root causes of adjudications, and the proportions of "errors" versus "medically justifiable differences". RESULTS The trials had significantly different discrepancy rates both at the time-point (average = 34.3%) and patient (average = 59.2%) levels. When considering only discrepancies for progressive disease, homogeneous discrepancy rates were found with an average of 32.9%, while readers' endorsement rates ranged between 27.7% and 77.8%. Major causes of adjudication were different per trial, with medically justifiable differences being the most common, triggering 74.2% of total adjudications. CONCLUSIONS We provide baseline performances for monitoring reader performance in trials with double reads. Intelligent reading system implementation along with appropriate reader training and monitoring are solutions that could mitigate a large portion of the commonly encountered reading errors.
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Affiliation(s)
- Hubert Beaumont
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
| | - Antoine Iannessi
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
- Centre Antoine Lacassagne, 33 Avenue de Valombrose, 06100 Nice, France
| | - Yi Wang
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
| | - Charles M. Voyton
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
| | - Jennifer Cillario
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
| | - Yan Liu
- Median Technologies, 1800 Route des Crêtes, 06560 Valbonne, France; (H.B.); (A.I.); (Y.W.); (C.M.V.); (J.C.)
- Correspondence: ; Tel.: +33-32475608490
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Ko CC, Yeh LR, Kuo YT, Chen JH. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res 2021; 9:52. [PMID: 34215324 PMCID: PMC8252278 DOI: 10.1186/s40364-021-00306-8] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Lee-Ren Yeh
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Jeon-Hor Chen
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan. .,Tu & Yuan Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697 - 5020, USA.
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Woo M, Devane AM, Lowe SC, Lowther EL, Gimbel RW. Deep learning for semi-automated unidirectional measurement of lung tumor size in CT. Cancer Imaging 2021; 21:43. [PMID: 34162439 PMCID: PMC8220702 DOI: 10.1186/s40644-021-00413-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 06/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Performing Response Evaluation Criteria in Solid Tumor (RECISTS) measurement is a non-trivial task requiring much expertise and time. A deep learning-based algorithm has the potential to assist with rapid and consistent lesion measurement. PURPOSE The aim of this study is to develop and evaluate deep learning (DL) algorithm for semi-automated unidirectional CT measurement of lung lesions. METHODS This retrospective study included 1617 lung CT images from 8 publicly open datasets. A convolutional neural network was trained using 1373 training and validation images annotated by two radiologists. Performance of the DL algorithm was evaluated 244 test images annotated by one radiologist. DL algorithm's measurement consistency with human radiologist was evaluated using Intraclass Correlation Coefficient (ICC) and Bland-Altman plotting. Bonferroni's method was used to analyze difference in their diagnostic behavior, attributed by tumor characteristics. Statistical significance was set at p < 0.05. RESULTS The DL algorithm yielded ICC score of 0.959 with human radiologist. Bland-Altman plotting suggested 240 (98.4 %) measurements realized within the upper and lower limits of agreement (LOA). Some measurements outside the LOA revealed difference in clinical reasoning between DL algorithm and human radiologist. Overall, the algorithm marginally overestimated the size of lesion by 2.97 % compared to human radiologists. Further investigation indicated tumor characteristics may be associated with the DL algorithm's diagnostic behavior of over or underestimating the lesion size compared to human radiologist. CONCLUSIONS The DL algorithm for unidirectional measurement of lung tumor size demonstrated excellent agreement with human radiologist.
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Affiliation(s)
- MinJae Woo
- Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA
| | - A Michael Devane
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Steven C Lowe
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Ervin L Lowther
- Department of Radiology, Prisma Health System, 200 Patewood Drive, Greenville, SC, 29615, USA
| | - Ronald W Gimbel
- Department of Public Health Sciences, Clemson University, 501 Edwards Hall, Clemson, SC, 29634, USA.
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Iannessi A, Beaumont H, Liu Y, Bertrand AS. RECIST 1.1 and lesion selection: How to deal with ambiguity at baseline? Insights Imaging 2021; 12:36. [PMID: 33738548 PMCID: PMC7973344 DOI: 10.1186/s13244-021-00976-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022] Open
Abstract
Response Evaluation Criteria In Solid Tumors (RECIST) is still the predominant criteria base for assessing tumor burden in oncology clinical trials. Despite several improvements that followed its first publication, RECIST continues to allow readers a lot of freedom in their evaluations. Notably in the selection of tumors at baseline. This subjectivity is the source of many suboptimal evaluations. When starting a baseline analysis, radiologists cannot always identify tumor malignancy with any certainty. Also, with RECIST, some findings can be deemed equivocal by radiologists with no confirmatory ground truth to rely on. In the specific case of Blinded Independent Central Review clinical trials with double reads using RECIST, the selection of equivocal tumors can have two major consequences: inter-reader variability and modified sensitivity of the therapeutic response. Apart from the main causes leading to the selection of an equivocal lesion, due to the uncertainty of the radiological characteristics or due to the censoring of on-site evaluations, several other situations can be described more precisely. These latter involve cases where an equivocal is selected as target or non-target lesions, the management of equivocal lymph nodes and the case of few target lesions. In all cases, awareness of the impact of selecting a non-malignant lesion will lead radiologists to make selections in the most rational way. Also, in clinical trials where the primary endpoint differs between phase 2 (response-related) and phase 3 (progression-related) trials, our impact analysis will help them to devise strategies for the management of equivocal lesions.
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Affiliation(s)
| | | | - Yan Liu
- Median Technologies, 06560, Valbonne, France
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Dromain C, Sundin A, Najran P, Vidal Trueba H, Dioguardi Burgio M, Crona J, Opalinska M, Carvalho L, Franca R, Borg P, Vietti Violi N, Schaefer N, Lopez C, Pezzutti D, de Mestier L, Lamarca A, Costa F, Pavel M, Ronot M. Tumor Growth Rate to Predict the Outcome of Patients with Neuroendocrine Tumors: Performance and Sources of Variability. Neuroendocrinology 2021; 111:831-839. [PMID: 32717738 DOI: 10.1159/000510445] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 07/21/2020] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Tumor growth rate (TGR), percentage of change in tumor volume/month, has been previously identified as an early radiological biomarker for treatment monitoring in neuroendocrine tumor (NET) patients. We assessed the performance and reproducibility of TGR at 3 months (TGR3m) as a predictor factor of progression-free survival (PFS), including the impact of imaging method and reader variability. METHODS Baseline and 3-month (±1 month) CT/MRI images from patients with advanced, grade 1-2 NETs were retrospectively reviewed by 2 readers. Influence of number of targets, tumor burden, and location of lesion on the performance of TGR3m to predict PFS was assessed by uni/multivariable Cox regression analysis. Agreement between readers was assessed by Lin's concordance coefficient (LCC) and kappa coefficient (KC). RESULTS A total of 790 lesions were measured in 222 patients. Median PFS was 22.9 months. On univariable analysis, number of lesions (</≥4), tumor burden, and presence of liver metastases were significantly correlated with PFS. On multivariate analysis, ≥4 lesions (HR: 1.89 [95% CI: 1.01-3.57]), TGR3m ≥0.8%/month (HR: 4.01 [95% CI: 2.31-6.97]), and watch and wait correlated with shorter PFS. No correlation was found between TGR3m and number of lesions (rho: -0.2; p value: 0.1930). No difference in mean TGR3m across organs was shown (p value: 0.6). Concordance between readers was acceptable (LCC: 0.52 [95% CI: 0.38-0.65]; KC: 0.57, agreement: 81.55%). TGR3m remained a significant prognostic factor when data from the second reader were employed (HR: 4.35 [95% CI: 2.44-7.79]; p value <0.001) regardless his expertise (HR: 1.21 [95% CI: 0.70-2.09]; p value: 0.493). DISCUSSION/CONCLUSION TGR3m is a robust and early radiological biomarker able to predict PFS. It may be used to identify patients with advanced NETs who require closer radiological follow-up.
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Affiliation(s)
- Clarisse Dromain
- Department of Radiology, CHUV University Hospital, UNIL University of Lausanne, Lausanne, Switzerland
| | - Anders Sundin
- Department of Radiology, Institution of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Pavan Najran
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Hector Vidal Trueba
- Department of Radiology, Hospital Universitario Marques de Valdecilla, Santander, Spain
| | | | - Joakim Crona
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Marta Opalinska
- Nuclear Medicine Unit, Department of Endocrinology, University Hospital, Krakow, Poland
| | - Luciana Carvalho
- Department of Radiology, Sirio-Libanes Hospital, Sao Paulo, Brazil
| | - Regis Franca
- Department of Radiology, Sirio-Libanes Hospital, Sao Paulo, Brazil
| | - Philip Borg
- Department of Radiology, The Christie NHS Foundation Trust, Manchester, United Kingdom
| | - Naik Vietti Violi
- Department of Radiology, CHUV University Hospital, UNIL University of Lausanne, Lausanne, Switzerland
| | - Niklaus Schaefer
- Department of Radiology, CHUV University Hospital, UNIL University of Lausanne, Lausanne, Switzerland
| | - Carlos Lopez
- Department of Oncology, Hospital Universitario Marques de Valdecilla, Santander, Spain
| | - Daniela Pezzutti
- Department of Radiology, Israelita Albert Einstein Hospital, Sao Paulo, Brazil
| | - Louis de Mestier
- Department of Oncology, Beaujon University Hospital, Clichy, France
| | - Angela Lamarca
- Department of Medical Oncology, The Christie NHS Foundation Trust, Division of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Frederico Costa
- Department of Medical Oncology, Sirio-Libanes Hospital, Sao Paulo, Brazil
| | - Marianne Pavel
- Department of Endocrinology, Universitatsklinikum Erlangen, Erlangen, Germany
| | - Maxime Ronot
- Department of Radiology, Beaujon University Hospital, Clichy, France,
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Kale P, Choudary GV, Sandeep P, Lakshmi A, Kumar VS, Mantri R. Correlation of three-dimensional computerized tomographic renal parenchymal volumetry with DTPA split renal function in prospective donors - A retrospective study. INDIAN JOURNAL OF TRANSPLANTATION 2021. [DOI: 10.4103/ijot.ijot_71_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Dhall G, O’Neil SH, Ji L, Haley K, Whitaker AM, Nelson MD, Gilles F, Gardner SL, Allen JC, Cornelius AS, Pradhan K, Garvin JH, Olshefski RS, Hukin J, Comito M, Goldman S, Atlas MP, Walter AW, Sands S, Sposto R, Finlay JL. Excellent outcome of young children with nodular desmoplastic medulloblastoma treated on "Head Start" III: a multi-institutional, prospective clinical trial. Neuro Oncol 2020; 22:1862-1872. [PMID: 32304218 PMCID: PMC7746930 DOI: 10.1093/neuonc/noaa102] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND "Head Start" III, was a prospective clinical trial using intensive induction followed by myeloablative chemotherapy and autologous hematopoietic cell rescue (AuHCR) to either avoid or reduce the dose/volume of irradiation in young children with medulloblastoma. METHODS Following surgery, patients received 5 cycles of induction followed by myeloablative chemotherapy using carboplatin, thiotepa, and etoposide with AuHCR. Irradiation was reserved for children >6 years old at diagnosis or with residual tumor post-induction. RESULTS Between 2003 and 2009, 92 children <10 years old with medulloblastoma were enrolled. Five-year event-free survival (EFS) and overall survival (OS) rates (±SE) were 46 ± 5% and 62 ± 5% for all patients, 61 ± 8% and 77 ± 7% for localized medulloblastoma, and 35 ± 7% and 52 ± 7% for disseminated patients. Nodular/desmoplastic (ND) medulloblastoma patients had 5-year EFS and OS (±SE) rates of 89 ± 6% and 89 ± 6% compared with 26 ± 6% and 53 ± 7% for classic and 38 ± 13% and 46 ± 14% for large-cell/anaplastic (LCA) medulloblastoma, respectively. In multivariate Cox regression analysis, histology was the only significant independent predictor of EFS after adjusting for stage, extent of resection, regimen, age, and sex (P <0.0001). Five-year irradiation-free EFS was 78 ± 8% for ND and 21 ± 5% for classic/LCA medulloblastoma patients. Myelosuppression was the most common toxicity, with 2 toxic deaths. Twenty-four survivors completed neurocognitive evaluation at a mean of 4.9 years post-diagnosis. IQ and memory scores were within average range overall, whereas processing speed and adaptive functioning were low-average. CONCLUSION We report excellent survival and preservation of mean IQ and memory for young children with ND medulloblastoma using high-dose chemotherapy, with most patients surviving without irradiation.
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Affiliation(s)
- Girish Dhall
- Division of Pediatric Hematology-Oncology, University of Alabama Birmingham, Birmingham, Alabama, USA
| | - Sharon H O’Neil
- Division of Neurology and The Saban Research Institute, Children’s Hospital Los Angeles (CHLA), Los Angeles, California, USA
| | - Lingyun Ji
- Department of Preventive Medicine, Keck School of Medicine at the University of Southern California, Los Angeles, California, USA
| | - Kelley Haley
- Division of Hematology-Oncology CHLA, Los Angeles, California, USA
| | | | | | - Floyd Gilles
- Department of Pathology CHLA, Los Angeles, California, USA
| | - Sharon L Gardner
- Division of Pediatric Hematology-Oncology, NYU Medical Center, New York, New York, USA
| | - Jeffrey C Allen
- Division of Pediatric Hematology-Oncology, NYU Medical Center, New York, New York, USA
| | - Albert S Cornelius
- Division of Pediatric Hematology-Oncology, Helen DeVos Children’s Hospital, Grand Rapids, Michigan, USA
| | - Kamnesh Pradhan
- Division of Pediatric Hematology-Oncology, Riley Hospital for Children, Indianapolis, Indiana, USA
| | - James H Garvin
- Division of Pediatric Hematology-Oncology, New York Presbyterian Hospital, New York, New York, USA
| | - Randal S Olshefski
- Division of Pediatric Hematology-Oncology, Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Juliette Hukin
- Division of Pediatric Hematology-Oncology, British Columbia Children’s Hospital, Vancouver, British Columbia, Canada
| | - Melanie Comito
- Division of Pediatric Hematology-Oncology, Penn State Hershey Children’s Hospital, Hershey, Pennsylvania, USA
| | - Stewart Goldman
- Division of Pediatric Hematology-Oncology, Lurie Children’s Hospital, Chicago, Illinois, USA
| | - Mark P Atlas
- Division of Pediatric Hematology-Oncology, Children’s Medical Center of New York, New York, New York, USA
| | - Andrew W Walter
- Division of Pediatric Hematology-Oncology, Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Stephen Sands
- Departments of Psychiatry and Behavioral Sciences and Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Richard Sposto
- Division of Hematology-Oncology CHLA, Los Angeles, California, USA
| | - Jonathan L Finlay
- Division of Pediatric Hematology-Oncology, Nationwide Children’s Hospital, Columbus, Ohio, USA
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Radiological Assessment and Outcome of Local Disease Progression after Neoadjuvant Chemotherapy in Children and Adolescents with Localized Osteosarcoma. J Clin Med 2020; 9:jcm9124070. [PMID: 33348627 PMCID: PMC7767085 DOI: 10.3390/jcm9124070] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/09/2020] [Accepted: 12/15/2020] [Indexed: 01/30/2023] Open
Abstract
Objective: We examined the interobserver reliability of local progressive disease (L-PD) determination using two major radiological response evaluation criteria systems (Response evaluation Criteria in Solid Tumors (RECIST) and the European and American Osteosarcoma Study (EURAMOS)) in patients diagnosed with localized osteosarcoma (OS). Additionally, we describe the outcomes of patients determined to experience L-PD. Materials and Methods: Forty-seven patients diagnosed with localized OS between 2000 and 2012 at our institution were identified. Paired magnetic resonance imaging of the primary tumor from diagnosis and post-neoadjuvant chemotherapy were blindly assessed by two experienced radiologists and determined L-PD as per RECIST and EURAMOS radiological criteria. Interobserver reliability was measured using the kappa statistic (κ). The Kaplan Meier method and log-rank test was used to assess differences between groups. Results: Of 47 patients (median age at diagnosis 12.9 years), 16 (34%) had L-PD (by RECIST or EURAMOS radiological definition). There was less agreement between the radiologists using EURAMOS radiological criteria for L-PD (80.9%, κ = 0.48) than with RECIST criteria (97.9%, κ = 0.87). Patients with radiologically defined L-PD had a 5-year progression-free survival (PFS) of 55.6%, compared to a 5 year-PFS of 82.7% in the group of patients without L-PD (n = 31) (Log rank p = 0.0185). Conclusions: The interobserver reliability of L-PD determination is higher using RECIST than EURAMOS. RECIST can be considered for response assessment in OS clinical trials. The presence of L-PD was associated with worse outcomes.
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Wolosker MB, Diamante Leiderman DB, Estevan FA, Wolosker N, Zerati AE, Amaro E. Comparative Analysis of Artery Anatomy Evaluated by Postmortem Tomography, CT Angiography, and Postmortem and Predeath CT Scans. Ann Vasc Surg 2020; 72:124-137. [PMID: 32949733 DOI: 10.1016/j.avsg.2020.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/23/2020] [Accepted: 09/12/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND To date, no study has been performed analyzing changes in the vascular system comparing paired examinations of patients alive and after death with the use of cardiopulmonary bypass and computed tomography (CT) angiography. MATERIALS AND METHODS The aim of this study was to analyze in a large series (38 patients) the aorta and its branches by CT (without contrast) and CT angiography of patients still alive and after death comparing their diameters and length variations. RESULTS The variation between in vivo tomography and virtopsy methods was greater in the evaluation of distances between vascular segments than in the diameters; less than 30% of the distances evaluated in the entire study had acceptable variation between methods, regardless of the use of contrast scans. We observed better repeatability rates in the comparison between in vivo and postmortem contrast-enhanced examinations. Comparing the examinations of the still alive individuals with the contrast-enhanced tomography after death, we observed a higher concordance rate. The best variations between the methods were observed in the evaluation of the diameters in the contrast-enhanced examination of the ascending aorta, aortic arch, thoracic aorta, and thoracoabdominal transition. CONCLUSIONS The measurements obtained in postmortem angiography images partially reflect the vascular anatomy of the main branches in the thoracoabdominal region in vivo. However, postmortem CT without contrast was not performed in the same comparison. We believe that adjustments to the contrast injection technique may eventually improve these results.
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Affiliation(s)
| | | | | | - Nelson Wolosker
- University of São Paulo, Faculty of Medicine, São Paulo, Brazil; Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | - Edson Amaro
- University of São Paulo, Faculty of Medicine, São Paulo, Brazil; Hospital Israelita Albert Einstein, São Paulo, Brazil
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Nell E, Ober C, Rendahl A, Forrest L, Lawrence J. Volumetric tumor response assessment is inefficient without overt clinical benefit compared to conventional, manual veterinary response assessment in canine nasal tumors. Vet Radiol Ultrasound 2020; 61:592-603. [PMID: 32702179 DOI: 10.1111/vru.12895] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/27/2020] [Accepted: 05/07/2020] [Indexed: 02/04/2023] Open
Abstract
Accurate assessment of tumor response to therapy is critical in guiding management of veterinary oncology patients and is most commonly performed using response evaluation criteria in solid tumors criteria. This process can be time consuming and have high intra- and interobserver variability. The primary aim of this serial measurements, secondary analysis study was to compare manual linear tumor response assessment to semi-automated, contoured response assessment in canine nasal tumors. The secondary objective was to determine if tumor measurements or clinical characteristics, such as stage, would correlate to progression-free interval. Three investigators evaluated paired CT scans of skulls of 22 dogs with nasal tumors obtained prior to and following radiation therapy. The automatically generated tumor volumes were not useful for canine nasal tumors in this study, characterized by poor intraobserver agreement between automatically generated contours and hand-adjusted contours. The radiologist's manual linear method of determining response evaluation criteria in solid tumors categorization and tumor volume is significantly faster (P < .0001) but significantly underestimates nasal tumor volume (P < .05) when compared to a contour-based method. Interobserver agreement was greater for volume determination using the contour-based method when compared to response evaluation criteria in solid tumors categorization utilizing the same method. However, response evaluation criteria in solid tumors categorization and percentage volume change were strongly correlated, providing validity to response evaluation criteria in solid tumors as a rapid method of tumor response assessment for canine nasal tumors. No clinical characteristics or tumor measurements were significantly associated with progression-free interval.
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Affiliation(s)
- Esther Nell
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
| | - Christopher Ober
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
| | - Aaron Rendahl
- Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
| | - Lisa Forrest
- Department of Surgical Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jessica Lawrence
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, University of Minnesota, St Paul, Minnesota, USA
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Lombard A, Mistry H, Chapman SC, Gueoguieva I, Aarons L, Ogungbenro K. Impact of tumour size measurement inter-operator variability on model-based drug effect evaluation. Cancer Chemother Pharmacol 2020; 85:817-825. [PMID: 32170415 PMCID: PMC7125250 DOI: 10.1007/s00280-020-04049-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 02/27/2020] [Indexed: 12/20/2022]
Abstract
Purpose During oncology clinical trials, tumour size (TS) measurements are commonly used to monitor disease progression and to assess drug efficacy. We explored inter-operator variability within a subset of a phase III clinical trial conducted from August 1995 to February 1997 and its impact on drug effect evaluation using a tumour growth inhibition model. Methods One hundred twenty lesions were measured twice at each time point; once at the hospital and once at the centralised centre. A visual analysis was performed to identify trends within the profiles over time. Linear regression and relative error ratios were used to explore the inter-operator variability of raw TS measurements and model-based estimates. Results While correlation between patient-level estimates of drug effect was poor (r2 = 0.28), variability between the study-level estimates was much less affected (9%). Conclusions The global evaluation of drug effect using modelling approaches might not be affected by inter-operator variability. However, the exploration of covariates for drug effect and the characterisation of an exposure–tumour shrinkage relationship seems limited by the high measurement variability that translates to a poor correlation of individual drug effect estimates. This might be addressed by the use of more precise computer-aided measurement methods. Electronic supplementary material The online version of this article (10.1007/s00280-020-04049-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aurélie Lombard
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK.
| | - Hitesh Mistry
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
- Division of Cancer Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | | | | | - Leon Aarons
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
| | - Kayode Ogungbenro
- Centre for Applied Pharmacokinetic Research, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, M13 9PT, UK
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50
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Hersberger KE, Mendiratta-Lala M, Fischer R, Kaza RK, Francis IR, Olszewski MS, Harju JF, Shi W, Manion FJ, Al-Hawary MM, Sahai V. Quantitative Imaging Assessment for Clinical Trials in Oncology. J Natl Compr Canc Netw 2019; 17:1505-1511. [PMID: 31805530 DOI: 10.6004/jnccn.2019.7331] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 06/18/2019] [Indexed: 11/17/2022]
Abstract
BACKGROUND Objective radiographic assessment is crucial for accurately evaluating therapeutic efficacy and patient outcomes in oncology clinical trials. Imaging assessment workflow can be complex; can vary with institution; may burden medical oncologists, who are often inadequately trained in radiology and response criteria; and can lead to high interobserver variability and investigator bias. This article reviews the development of a tumor response assessment core (TRAC) at a comprehensive cancer center with the goal of providing standardized, objective, unbiased tumor imaging assessments, and highlights the web-based platform and overall workflow. In addition, quantitative response assessments by the medical oncologists, radiologist, and TRAC are compared in a retrospective cohort of patients to determine concordance. PATIENTS AND METHODS The TRAC workflow includes an image analyst who pre-reviews scans before review with a board-certified radiologist and then manually uploads annotated data on the proprietary TRAC web portal. Patients previously enrolled in 10 lung cancer clinical trials between January 2005 and December 2015 were identified, and the prospectively collected quantitative response assessments by the medical oncologists were compared with retrospective analysis of the same dataset by a radiologist and TRAC. RESULTS This study enlisted 49 consecutive patients (53% female) with a median age of 60 years (range, 29-78 years); 2 patients did not meet study criteria and were excluded. A linearly weighted kappa test for concordance for TRAC versus radiologist was substantial at 0.65 (95% CI, 0.46-0.85; standard error [SE], 0.10). The kappa value was moderate at 0.42 (95% CI, 0.20-0.64; SE, 0.11) for TRAC versus oncologists and only fair at 0.34 (95% CI, 0.12-0.55; SE, 0.11) for oncologists versus radiologist. CONCLUSIONS Medical oncologists burdened with the task of tumor measurements in patients on clinical trials may introduce significant variability and investigator bias, with the potential to affect therapeutic response and clinical trial outcomes. Institutional imaging cores may help bridge the gap by providing unbiased and reproducible measurements and enable a leaner workflow.
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Affiliation(s)
- Katherine E Hersberger
- aDepartment of Internal Medicine, University of Michigan Medical School
- bUniversity of Michigan Rogel Cancer Center; and
| | | | | | - Ravi K Kaza
- cDepartment of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Isaac R Francis
- bUniversity of Michigan Rogel Cancer Center; and
- cDepartment of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | | | - John F Harju
- bUniversity of Michigan Rogel Cancer Center; and
| | - Wei Shi
- bUniversity of Michigan Rogel Cancer Center; and
| | | | - Mahmoud M Al-Hawary
- bUniversity of Michigan Rogel Cancer Center; and
- cDepartment of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Vaibhav Sahai
- aDepartment of Internal Medicine, University of Michigan Medical School
- aDepartment of Internal Medicine, University of Michigan Medical School
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