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van Amsterdam WAC, de Jong PA, Verhoeff JJC, Leiner T, Ranganath R. From algorithms to action: improving patient care requires causality. BMC Med Inform Decis Mak 2024; 24:111. [PMID: 38664664 PMCID: PMC11046962 DOI: 10.1186/s12911-024-02513-3] [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: 01/31/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
In cancer research there is much interest in building and validating outcome prediction models to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.
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Affiliation(s)
- Wouter A C van Amsterdam
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands.
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
- Mayo Clinic, Rochester, MN, USA
| | - Rajesh Ranganath
- Courant Institute of Mathematical Sciences, Department of Computer Science, New York University, New York City, NY, USA
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Chambers KH. Radiomic Biomarkers as a Non-Invasive Tool for Assessing Biological Age. Can Geriatr J 2023; 26:410-411. [PMID: 37662058 PMCID: PMC10444525 DOI: 10.5770/cgj.26.685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023] Open
Affiliation(s)
- Kevoyne H Chambers
- School of Medicine, Jiangsu University, Zhenjiang City, Jiangsu Province, China
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Zhang Y, Hu HH, Zhou SH, Xia WY, Zhang Y, Zhang JP, Fu XL, Yu W. PET-based radiomics visualizes tumor-infiltrating CD8 T cell exhaustion to optimize radiotherapy/immunotherapy combination in mouse models of lung cancer. Biomark Res 2023; 11:10. [PMID: 36694213 PMCID: PMC9875413 DOI: 10.1186/s40364-023-00454-z] [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: 10/08/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Cumulative preclinical and clinical evidences showed radiotherapy might augment systemic antitumoral responses to immunotherapy for metastatic non-small cell lung cancer, but the optimal timing of combination is still unclear. The overall infiltration and exhausted subpopulations of tumor-infiltrating CD8+ T cells might be a potential biomarker indicating the response to immune checkpoint inhibitors (ICI), the alteration of which is previously uncharacterized during peri-irradiation period, while dynamic monitoring is unavailable via repeated biopsies in clinical practice. METHODS Basing on tumor-bearing mice model, we investigated the dynamics of overall infiltration and exhausted subpopulations of CD8+ T cells after ablative irradiation. With the understanding of distinct metabolic characteristics accompanied with T cell exhaustion, we developed a PET radiomics approach to identify and visualize T cell exhaustion status. RESULTS CD8+ T cell infiltration increased from 3 to 14 days after ablative irradiation while terminally exhausted populations significantly predominated CD8+ T cells during late course of this infiltrating period, indicating that 3-7 days post-irradiation might be a potential appropriate window for delivering ICI treatment. A PET radiomics approach was established to differentiate T cell exhaustion status, which fitted well in both ICI and irradiation settings. We also visualized the underlying association of more heterogeneous texture on PET images with progressed T cell exhaustion. CONCLUSIONS We proposed a non-invasive imaging predictor which accurately assessed heterogeneous T cell exhaustion status relevant to ICI treatment and irradiation, and might serve as a promising solution to timely estimate immune-responsiveness of tumor microenvironment and the optimal timing of combined therapy.
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Affiliation(s)
- Ying Zhang
- grid.412524.40000 0004 0632 3994Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200030 China
| | - Hui-Hui Hu
- grid.412524.40000 0004 0632 3994Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200030 China
| | - Shi-Hong Zhou
- grid.412524.40000 0004 0632 3994Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wu-Yan Xia
- grid.412524.40000 0004 0632 3994Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200030 China
| | - Yan Zhang
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Immunology, Department of Immunology and Microbiology, State Key Laboratory of Oncogenes and Related Genes, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian-Ping Zhang
- grid.452404.30000 0004 1808 0942Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiao-Long Fu
- grid.412524.40000 0004 0632 3994Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200030 China
| | - Wen Yu
- grid.412524.40000 0004 0632 3994Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200030 China
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Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, Lo Presti G, Vincini MG, Rampinelli C, Cremonesi M, de Marinis F, Spaggiari L, Gandini S, Guckenberger M, Orecchia R, Jereczek-Fossa BA. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res 2022; 11:2452-2463. [PMID: 36636424 PMCID: PMC9830263 DOI: 10.21037/tlcr-22-248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
Background No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Cristiano Rampinelli
- Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;,Division of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roberto Orecchia
- Scientific Direction, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022; 11:2051-2063. [PMID: 36386457 PMCID: PMC9641045 DOI: 10.21037/tlcr-22-158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 08/22/2022] [Indexed: 09/13/2023]
Abstract
BACKGROUND 18F-FDG PET and CT radiomics has been the object of a wide research for over 20 years but its contribution to clinical practice remains not yet well established. We have investigated its impact versus that of only histo-clinical data, for the routine management of non-small-cell lung cancer (NSCLC). METHODS Our patients were retrospectively considered. They all had a FDG PET-CT and immuno-histo-chemistry (IHC) to assess PD-L1 expression at the beginning of the disease. A prognosis univariate and multivariate Cox survival analyses was performed for overall survival (OS) and progression free survival (PFS) prediction, including a training/testing procedure. Two sets of 47 PET and 47 CT radiomics features (RFs) were extracted. Difference between RFs according to PD-L1 expression, the histology status and the stage level were tested using suited non parametric statistical tests and the receiver operating characteristics (ROC) curve and the area under curve (AUC). RESULTS From 2017 to 2019, 212 NSCLC patients treated in our institution were included. The main conventional prognostic variables were stage and gender with a low added prognostic value in the models including PET and CT RFs. Neither PET nor CT RFs were significant to separate the different levels of PD-L1 expression. Several RFs differ between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) tumours and a large number of PET and CT RFs are significantly linked to patient stage. CONCLUSIONS In our population, PET and CT RFs show their intrinsic power to predict survival but do not significantly improve OS and PFS prediction in the different multivariate models, in comparison to conventional data. It would seem necessary to carry out one's own survival analysis before determining a radiomics signature.
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Affiliation(s)
- Pascal Hannequin
- Annecy Nuclear Medicine Center, Le Pericles, B Allée de la Mandallaz, Metz-Tessy, France
| | - Chantal Decroisette
- Pneumology Department, CHANGE Annecy, 1 Avenue de l’hôpital, Metz-Tessy, France
| | - Pascale Kermanach
- Mont Blanc Histo-Pathology Laboratory, 40 Route de l’Aiglière, Argonay, France
| | - Giulia Berardi
- Pneumology Department, University Hospital la Tronche, Boulevard de la Chantourne, La Tronche, France
| | - Vincent Bourbonne
- Radiation Oncology Department, University Hospital, 2 Avenue Foch, Brest, France
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Creff G, Jegoux F, Palard X, Depeursinge A, Abgral R, Marianowski R, Leclere JC, Eugene T, Malard O, Crevoisier RD, Devillers A, Castelli J. 18F-FDG PET/CT-Based Prognostic Survival Model After Surgery for Head and Neck Cancer. J Nucl Med 2022; 63:1378-1385. [PMID: 34887336 PMCID: PMC9454462 DOI: 10.2967/jnumed.121.262891] [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: 07/11/2021] [Revised: 11/16/2021] [Indexed: 12/24/2022] Open
Abstract
The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting overall survival (OS) and distant metastasis-free survival (DMFS) in a cohort of head and neck squamous cell carcinoma patients treated with surgery, to generate a prognostic model of OS and DMFS, and to validate this prognostic model with an independent cohort. Methods: A total of 382 consecutive patients with head and neck squamous cell carcinoma, divided into training (n = 318) and validation (n = 64) cohorts, were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and distance parameters for the primary tumor and lymph nodes defined by 2 segmentation methods (relative SUVmax threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The concordance index (c-index) was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and nodal MTV, with a c-index of 0.64 (P < 0.001). For DMFS, the significant parameters were T stage, nodal MTV, and maximal tumor-node distance, with a c-index of 0.76 (P < 0.001). These combinations of parameters were externally validated, with c-indices of 0.63 (P < 0.001) and 0.71 (P < 0.001) for OS and DMFS, respectively. Conclusion: The nodal MTV associated with the maximal tumor-node distance was significantly correlated with the risk of DMFS. Moreover, this parameter, in addition to clinical parameters, was associated with a higher risk of death. These prognostic factors may be used to tailor individualized treatment.
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Affiliation(s)
- Gwenaelle Creff
- Department of Otolaryngology-Head and Neck Surgery (HNS), University Hospital, Rennes, France;
| | - Franck Jegoux
- Department of Otolaryngology–Head and Neck Surgery (HNS), University Hospital, Rennes, France
| | - Xavier Palard
- Department of Nuclear Medicine, Cancer Institute, Rennes, France
| | | | - Ronan Abgral
- Department of Nuclear Medicine, University Hospital, Brest, France
| | - Remi Marianowski
- Department of Otolaryngology–HNS, University Hospital, Brest, France
| | | | - Thomas Eugene
- Department of Nuclear Medicine, University Hospital, Nantes, France
| | - Olivier Malard
- Department of Otolaryngology–HNS, University Hospital, Nantes, France
| | - Renaud De Crevoisier
- Department of Radiation Oncology, Cancer Institute, Rennes, France; and,LTSI (Image and Signal Processing Laboratory), INSERM, U1099, Rennes, France
| | - Anne Devillers
- Department of Nuclear Medicine, Cancer Institute, Rennes, France
| | - Joel Castelli
- Department of Radiation Oncology, Cancer Institute, Rennes, France; and,LTSI (Image and Signal Processing Laboratory), INSERM, U1099, Rennes, France
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18F Site-Specific Labelling of a Single-Chain Antibody against Activated Platelets for the Detection of Acute Thrombosis in Positron Emission Tomography. Int J Mol Sci 2022; 23:ijms23136886. [PMID: 35805892 PMCID: PMC9267009 DOI: 10.3390/ijms23136886] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 12/10/2022] Open
Abstract
Positron emission tomography is the imaging modality of choice when it comes to the high sensitivity detection of key markers of thrombosis and inflammation, such as activated platelets. We, previously, generated a fluorine-18 labelled single-chain antibody (scFv) against ligand-induced binding sites (LIBS) on activated platelets, binding it to the highly abundant platelet glycoprotein integrin receptor IIb/IIIa. We used a non-site-specific bio conjugation approach with N-succinimidyl-4-[18F]fluorobenzoate (S[18F]FB), leading to a mixture of products with reduced antigen binding. In the present study, we have developed and characterised a novel fluorine-18 PET radiotracer, based on this antibody, using site-specific bio conjugation to engineer cysteine residues with N-[2-(4-[18F]fluorobenzamido)ethyl]maleimide ([18F]FBEM). ScFvanti-LIBS and control antibody mut-scFv, with engineered C-terminal cysteine, were reduced, and then, they reacted with N-[2-(4-[18F]fluorobenzamido)ethyl]maleimide ([18F]FBEM). Radiolabelled scFv was injected into mice with FeCl3-induced thrombus in the left carotid artery. Clots were imaged in a PET MR imaging system, and the amount of radioactivity in major organs was measured using an ionisation chamber and image analysis. Assessment of vessel injury, as well as the biodistribution of the radiolabelled scFv, was studied. In the in vivo experiments, we found uptake of the targeted tracer in the injured vessel, compared with the non-injured vessel, as well as a high uptake of both tracers in the kidney, lung, and muscle. As expected, both tracers cleared rapidly via the kidney. Surprisingly, a large quantity of both tracers was taken up by organs with a high glutathione content, such as the muscle and lung, due to the instability of the maleimide cysteine bond in vivo, which warrants further investigations. This limits the ability of the novel antibody radiotracer 18F-scFvanti-LIBS to bind to the target in vivo and, therefore, as a useful agent for the sensitive detection of activated platelets. We describe the first fluorine-18 variant of the scFvanti-LIBS against activated platelets using site-specific bio conjugation.
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Gong J, Zhang W, Huang W, Liao Y, Yin Y, Shi M, Qin W, Zhao L. CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: a multicenter study. Radiother Oncol 2022; 174:8-15. [PMID: 35750106 DOI: 10.1016/j.radonc.2022.06.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 05/18/2022] [Accepted: 06/15/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND AND PURPOSE To establish and validate a contrast-enhanced computed tomography-based hybrid radiomics nomogram for prediction of local recurrence-free survival (LRFS) in esophageal squamous cell cancer (ESCC) patients receiving definitive (chemo)radiotherapy in a multicenter setting. MATERIALS AND METHODS This retrospective study included 302 ESCC patients from Xijing Hospital receiving definitive (chemo)radiotherapy, which were randomly assigned to the training (n=201) and internal validation set (n=101). And 74 and 21 ESCC patients from the other two centers were used as the external validation set (n=95). A hybrid radiomics nomogram was established by integrating clinical factors, radiomic signature and deep-learning signature in training set and was tested in two validation sets. RESULTS The deep-learning signature showed better prognostic performance than radiomic signature for predicting LRFS in training (C-index:0.73 vs 0.70), internal (Cindex: 0.72 vs 0.64) and external validation set (C-index:0.72 vs 0.63), which could stratify patients into high and low-risk group with different prognosis (cut-off value: -0.06). Low-risk groups had better LRFS than high-risk groups in training (p<0.0001; 2-y LRFS 71.1% vs 33.0%), internal (p<0.01; 2-y LRFS 58.8% vs 34.8%) and external validation sets (p<0.0001; 2-y LRFS 61.9% vs 22.4%), respectively. The hybrid radiomics nomogram established by integrating radiomic signature, deep-learning signature with clinical factors including T stage and concurrent chemotherapy outperformed any one or two combinations in training (C-index:0.82), internal (Cindex: 0.78), and external validation set (C-index:0.76). Calibration curves showed good agreement. CONCLUSIONS The hybrid radiomics based on pretreatment contrast-enhanced computed tomography provided a promising way to predict local recurrence of ESCC patients receiving definitive (chemo)radiotherapy.
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Affiliation(s)
- Jie Gong
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University. Xi'an, China
| | - Wencheng Zhang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wei Huang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
| | - Ye Liao
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University. Xi'an, China
| | - Yutian Yin
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University. Xi'an, China
| | - Mei Shi
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University. Xi'an, China.
| | - Wei Qin
- Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Lina Zhao
- Department of Radiation Oncology, Xijing Hospital, Air Force Medical University. Xi'an, China.
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Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [ 18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022; 52:759-780. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/10/2022] [Accepted: 04/13/2022] [Indexed: 02/07/2023]
Abstract
Lung cancer is the second most common cancer and the leading cause of cancer-related death worldwide. Molecular imaging using [18F]fluorodeoxyglucose Positron Emission Tomography and/or Computed Tomography ([18F]FDG-PET/CT) plays an essential role in the diagnosis, evaluation of response to treatment, and prediction of outcomes. The images are evaluated using qualitative and conventional quantitative indices. However, there is far more information embedded in the images, which can be extracted by sophisticated algorithms. Recently, the concept of uncovering and analyzing the invisible data extracted from medical images, called radiomics, is gaining more attention. Currently, [18F]FDG-PET/CT radiomics is growingly evaluated in lung cancer to discover if it enhances the diagnostic performance or implication of [18F]FDG-PET/CT in the management of lung cancer. In this review, we provide a short overview of the technical aspects, as they are discussed in different articles of this special issue. We mainly focus on the diagnostic performance of the [18F]FDG-PET/CT-based radiomics and the role of artificial intelligence in non-small cell lung cancer, impacting the early detection, staging, prediction of tumor subtypes, biomarkers, and patient's outcomes.
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Affiliation(s)
- Reyhaneh Manafi-Farid
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Emran Askari
- Department of Nuclear Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Christian Pirich
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Mahboobeh Asadi
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Maziar Khateri
- Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland; Geneva University Neurocenter, Geneva University, Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
| | - Mohsen Beheshti
- Division of Molecular Imaging and Theranostics, Department of Nuclear Medicine, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria.
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Bouron C, Mathie C, Seegers V, Morel O, Jézéquel P, Lasla H, Guillerminet C, Girault S, Lacombe M, Sher A, Lacoeuille F, Patsouris A, Testard A. Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [ 18F]FDG PET/CT in Early Triple-Negative Breast Cancer. Cancers (Basel) 2022; 14:cancers14030637. [PMID: 35158904 PMCID: PMC8833829 DOI: 10.3390/cancers14030637] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/22/2022] [Accepted: 01/23/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary The aim of this study was to evaluate PET/CT parameters to determine different prognostic groups in TNBC, in order to select patients with a high risk of relapse, for whom therapeutic escalation can be considered. We have demonstrated that the MTV, TLG and entropy of the primary breast lesion could be of interest to predict the prognostic outcome of TNBC patients. Abstract (1) Background: triple-negative breast cancer (TNBC) remains a clinical and therapeutic challenge primarily affecting young women with poor prognosis. TNBC is currently treated as a single entity but presents a very diverse profile in terms of prognosis and response to treatment. Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose ([18F]FDG) is gaining importance for the staging of breast cancers. TNBCs often show high [18F]FDG uptake and some studies have suggested a prognostic value for metabolic and volumetric parameters, but no study to our knowledge has examined textural features in TNBC. The objective of this study was to evaluate the association between metabolic, volumetric and textural parameters measured at the initial [18F]FDG PET/CT and disease-free survival (DFS) and overall survival (OS) in patients with nonmetastatic TBNC. (2) Methods: all consecutive nonmetastatic TNBC patients who underwent a [18F]FDG PET/CT examination upon diagnosis between 2012 and 2018 were retrospectively included. The metabolic and volumetric parameters (SUVmax, SUVmean, SUVpeak, MTV, and TLG) and the textural features (entropy, homogeneity, SRE, LRE, LGZE, and HGZE) of the primary tumor were collected. (3) Results: 111 patients were enrolled (median follow-up: 53.6 months). In the univariate analysis, high TLG, MTV and entropy values of the primary tumor were associated with lower DFS (p = 0.008, p = 0.006 and p = 0.025, respectively) and lower OS (p = 0.002, p = 0.001 and p = 0.046, respectively). The discriminating thresholds for two-year DFS were calculated as 7.5 for MTV, 55.8 for TLG and 2.6 for entropy. The discriminating thresholds for two-year OS were calculated as 9.3 for MTV, 57.4 for TLG and 2.67 for entropy. In the multivariate analysis, lymph node involvement in PET/CT was associated with lower DFS (p = 0.036), and the high MTV of the primary tumor was correlated with lower OS (p = 0.014). (4) Conclusions: textural features associated with metabolic and volumetric parameters of baseline [18F]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.
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Affiliation(s)
- Clément Bouron
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- Correspondence:
| | - Clara Mathie
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
| | - Valérie Seegers
- Research and Statistics Department, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France;
| | - Olivier Morel
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Pascal Jézéquel
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
- CRCINA, UMR 1232 INSERM, Université de Nantes, Université d’Angers, Institut de Recherche en Santé, 8 Quai Moncousu—BP 70721, CEDEX 1, 44007 Nantes, France
| | - Hamza Lasla
- Omics Data Science Unit, ICO Pays de la Loire, Bd Jacques Monod, CEDEX, 44805 Saint-Herblain, France; (P.J.); (H.L.)
| | - Camille Guillerminet
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
- Department of Medical Physics, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France
| | - Sylvie Girault
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Marie Lacombe
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Avigaelle Sher
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
| | - Franck Lacoeuille
- Department of Nuclear Medicine, University Hospital of Angers, 4 rue Larrey, 49100 Angers, France;
- CRCINA, University of Nantes and Angers, INSERM UMR1232 équipe 17, 49055 Angers, France
| | - Anne Patsouris
- Department of Medical Oncology, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (C.M.); (A.P.)
- INSERM UMR1232 équipe 12, 49055 Angers, France
| | - Aude Testard
- Department of Nuclear Medicine, ICO Pays de la Loire, 15 rue André Boquel, 49055 Angers, France; (O.M.); (C.G.); (S.G.); (M.L.); (A.S.); (A.T.)
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11
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Yang Y, Zhang T, Zhou Z, Liang J, Chen D, Feng Q, Xiao Z, Hui Z, Lv J, Deng L, Wang X, Wang W, Wang J, Liu W, Zhai Y, Wang J, Bi N, Wang L. Development and validation of a prediction model using molecular marker for long-term survival in unresectable stage III non-small cell lung cancer treated with chemoradiotherapy. Thorac Cancer 2021; 13:296-307. [PMID: 34927371 PMCID: PMC8807329 DOI: 10.1111/1759-7714.14218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 12/28/2022] Open
Abstract
Background This study aimed to establish a predictive nomogram integrating epidermal growth factor receptor (EGFR) mutation status for 3‐ and 5‐year overall survival (OS) in unresectable/inoperable stage III non‐small cell lung cancer (NSCLC) treated with definitive chemoradiotherapy. Methods A total of 533 stage III NSCLC patients receiving chemoradiotherapy from 2013 to 2017 in our institution were included and divided into training and testing sets (2:1). Significant factors impacting OS were identified in the training set and integrated into the nomogram based on Cox proportional hazards regression. The model was subject to bootstrap internal validation and external validation within the testing set and an independent cohort from a phase III trial. The accuracy and discriminative capacity of the model were examined by calibration plots, C‐indexes and risk stratifications. Results The final multivariate model incorporated sex, smoking history, histology (including EGFR mutation status), TNM stage, planning target volume, chemotherapy sequence and radiation pneumonitis grade. The bootstrapped C‐indexes in the training set were 0.688, 0.710 for the 3‐ and 5‐year OS. For external validation, C‐indexes for 3‐ and 5‐year OS were 0.717, 0.720 in the testing set and 0.744, 0.699 in the external testing cohort, respectively. The calibration plots presented satisfying accuracy. The derivative risk stratification strategy classified patients into distinct survival subgroups successfully and performed better than the traditional TNM staging. Conclusions The nomogram incorporating EGFR mutation status could facilitate survival prediction and risk stratification for individual stage III NSCLC, providing information for enhanced immunotherapy decision and future trial design.
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Affiliation(s)
- Yufan Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zongmei Zhou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jun Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongfu Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qinfu Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zefen Xiao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhouguang Hui
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jima Lv
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Deng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenqing Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianyang Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenyang Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yirui Zhai
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Wang
- State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Nan Bi
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Luhua Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Radiation Oncology, National Cancer Center/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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12
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Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021; 34:e107-e122. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/24/2021] [Accepted: 10/14/2021] [Indexed: 12/13/2022]
Abstract
Lung cancer's radiomic phenotype may potentially inform clinical decision-making with respect to radical radiotherapy. At present there are no validated biomarkers available for the individualisation of radical radiotherapy in lung cancer and the mortality rate of this disease remains the highest of all other solid tumours. MEDLINE was searched using the terms 'radiomics' and 'lung cancer' according to the Preferred Reporting Items for Systematic Reviews and Met-Analyses (PRISMA) guidance. Radiomics studies were defined as those manuscripts describing the extraction and analysis of at least 10 quantifiable imaging features. Only those studies assessing disease control, survival or toxicity outcomes for patients with lung cancer following radical radiotherapy ± chemotherapy were included. Study titles and abstracts were reviewed by two independent reviewers. The Radiomics Quality Score was applied to the full text of included papers. Of 244 returned results, 44 studies met the eligibility criteria for inclusion. End points frequently reported were local (17%), regional (17%) and distant control (31%), overall survival (79%) and pulmonary toxicity (4%). Imaging features strongly associated with clinical outcomes include texture features belonging to the subclasses Gray level run length matrix, Gray level co-occurrence matrix and kurtosis. The median cohort size for model development was 100 (15-645); in the 11 studies with external validation in a separate independent population, the median cohort size was 84 (21-295). The median number of imaging features extracted was 184 (10-6538). The median Radiomics Quality Score was 11% (0-47). Patient-reported outcomes were not incorporated within any studies identified. No studies externally validated a radiomics signature in a registered prospective study. Imaging-derived indices attained through radiomic analyses could equip thoracic oncologists with biomarkers for treatment response, patterns of failure, normal tissue toxicity and survival in lung cancer. Based on routine scans, their non-invasive nature and cost-effectiveness are major advantages over conventional pathological assessment. Improved tools are required for the appraisal of radiomics studies, as significant barriers to clinical implementation remain, such as standardisation of input scan data, quality of reporting and external validation of signatures in randomised, interventional clinical trials.
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13
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Kong J, Zhu S, Shi G, Liu Z, Zhang J, Ren J. Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model. Front Oncol 2021; 11:739933. [PMID: 34631575 PMCID: PMC8499696 DOI: 10.3389/fonc.2021.739933] [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: 07/12/2021] [Accepted: 09/06/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND PURPOSE Chemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy. MATERIALS AND METHODS In total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model. RESULTS Of the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645-0.787) in the training group and 0.718 (95% CI: 0.612-0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674-0.810) in the training group and 0.715 (95% CI: 0.609-0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy. CONCLUSIONS A radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.
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Affiliation(s)
- Jie Kong
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shuchai Zhu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Gaofeng Shi
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Zhikun Liu
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jun Zhang
- Department of Radiation Oncology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jialiang Ren
- Pharmaceutical Diagnosis, GE Healthcare, Beijing, China
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Avanzo M, Gagliardi V, Stancanello J, Blanck O, Pirrone G, El Naqa I, Revelant A, Sartor G. Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy. Med Phys 2021; 48:6257-6269. [PMID: 34415574 DOI: 10.1002/mp.15178] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 07/20/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023] Open
Abstract
PURPOSE The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND METHODS Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, were calculated from pre-treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT ), radiomics CT and BED (RadCT,BED ), deep learning (DL) CT (DLCT ), and DL CT and BED (DLCT,BED ). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. RESULTS Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75-0.85)) and AUC of 0.773 (0.688-0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649-0.829) and AUC of 0.812 (0.755-0.867). CONCLUSION According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.
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Affiliation(s)
- Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Vito Gagliardi
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | | | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Giovanni Pirrone
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Issam El Naqa
- Department of Machine Learning, Moffitt University, Tampa, Florida, USA
| | - Alberto Revelant
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
| | - Giovanna Sartor
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, Italy
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15
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Oliveira C, Amstutz F, Vuong D, Bogowicz M, Hüllner M, Foerster R, Basler L, Schröder C, Eboulet EI, Pless M, Thierstein S, Peters S, Hillinger S, Tanadini-Lang S, Guckenberger M. Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging. EJNMMI Res 2021; 11:79. [PMID: 34417899 PMCID: PMC8380219 DOI: 10.1186/s13550-021-00809-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). Methods A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). Results Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). Conclusions A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-021-00809-3.
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Affiliation(s)
- Carol Oliveira
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Division of Radiation Oncology, Cancer Center of Southeastern Ontario, Queen's University, Kingston, ON, Canada.
| | - Florian Amstutz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martin Hüllner
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Robert Foerster
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Lucas Basler
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Christina Schröder
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Eric I Eboulet
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Sandra Thierstein
- Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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16
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Shi L, Shi W, Peng X, Zhan Y, Zhou L, Wang Y, Feng M, Zhao J, Shan F, Liu L. Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter. Front Oncol 2021; 11:618677. [PMID: 33968722 PMCID: PMC8096901 DOI: 10.3389/fonc.2021.618677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/25/2021] [Indexed: 12/09/2022] Open
Abstract
Purpose To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter. Materials and Methods This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts. Results Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusion This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.
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Affiliation(s)
- Lili Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Medical School, Nantong University, Nantong, China
| | - Weiya Shi
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xueqing Peng
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yi Zhan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Linxiao Zhou
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yunpeng Wang
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mingxiang Feng
- Chest Surgery Department, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jinli Zhao
- Radiology Department, Affiliated Hospital of Nantong University, Nantong, China
| | - Fei Shan
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Lei Liu
- Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,School of Basic Medical Sciences, and Academy of Engineering and Technology, Fudan University, Shanghai, China
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Moran A, Wang Y, Dyer BA, Yip SSF, Daly ME, Yamamoto T. Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer. Clin Lung Cancer 2021; 22:461-468. [PMID: 33931316 DOI: 10.1016/j.cllc.2021.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 01/26/2023]
Abstract
INTRODUCTION We investigated whether adding computed tomography (CT) and/or 18F-fluorodeoxyglucose (18F-FDG) PET radiomics features to conventional prognostic factors (CPFs) improves prognostic value in locally advanced non-small cell lung cancer (NSCLC). MATERIALS AND METHODS We retrospectively identified 39 cases with stage III NSCLC who received chemoradiotherapy and underwent planning CT and staging 18F-FDG PET scans. Seven CPFs were recorded. Feature selection was performed on 48 CT and 49 PET extracted radiomics features. A penalized multivariate Cox proportional hazards model was used to generate models for overall survival based on CPFs alone, CPFs with CT features, CPFs with PET features, and CPFs with CT and PET features. Linear predictors generated and categorized into 2 risk groups for which Kaplan-Meier survival curves were calculated. A log-rank test was performed to quantify the discrimination between the groups and calculated the Harrell's C-index to quantify the discriminatory power. A likelihood ratio test was performed to determine whether adding CT and/or PET features to CPFs improved model performance. RESULTS All 4 models significantly discriminated between the 2 risk groups. The discriminatory power was significantly increased when CPFs were combined with PET features (C-index 0.82; likelihood ratio test P < .01) or with both CT and PET features (0.83; P < .01) compared with CPFs alone (0.68). There was no significant improvement when CPFs were combined with CT features (0.68). CONCLUSION Adding PET radiomics features to CPFs yielded a significant improvement in the prognostic value in locally advanced NSCLC; adding CT features did not.
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Affiliation(s)
- Angel Moran
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA
| | - Yichuan Wang
- Department of Statistics, University of California Davis, Davis, CA
| | - Brandon A Dyer
- Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA
| | | | - Megan E Daly
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA
| | - Tokihiro Yamamoto
- Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, CA.
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Ji Y, Qiu Q, Fu J, Cui K, Chen X, Xing L, Sun X. Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer. Cancer Manag Res 2021; 13:307-317. [PMID: 33469373 PMCID: PMC7811450 DOI: 10.2147/cmar.s287128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 12/28/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose To investigate the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) textural analysis and to develop a stage-specific PET radiomic prediction model to distinguish lung ADC from SCC. Patients and Methods Patients who were histologically diagnosed with lung ADC or SCC and underwent pretreatment 18F-FDG PET/CT scans were retrospectively identified. Radiomic features were extracted from a semiautomatically outlined tumor region in the Chang-Gung Image Texture Analysis (CGITA) software package. The differences in radiomic parameters between lung ADC and SCC were compared stage-by-stage in 253 consecutive NSCLC patients with stages I to III disease. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection. A radiomic signature for each stage was subsequently constructed and evaluated. Then, an individual nomogram incorporating the radiomic signature and clinical risk factors was established and evaluated. The performance of the constructed models was assessed by receiver operating characteristic (ROC) curve analysis, and the nomogram was further validated by calibration curve analysis. Results The performance of the radiomic signature for distinguishing lung ADC and SCC in both the training and validation cohorts was good, with AUCs of 0.883, 0.854, and 0.895 in the training cohort and 0.932, 0.944, and 0.886 in the validation cohort for stages I, II, and III NSCLC, respectively. The radiomic-clinical nomogram integrating radiomic features with independent clinical predictors exhibited more favorable discriminative performance, with AUCs of 0.982, 0.963, and 0.979 in the training cohort and 0.989, 0.984, and 0.978 in the validation cohort for stages I, II, and III, respectively. Conclusion Differences in PET radiomic features between lung ADC and SCC varied in different stages. Stage-specific PET radiomic prediction models provided more favorable performance for discriminating the histological subtype of NSCLC.
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Affiliation(s)
- Yanlei Ji
- Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People's Republic of China.,Department of Ultrasound Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Jing Fu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Shandong First Medical University, Jinan, Shandong 250014, People's Republic of China
| | - Kai Cui
- Department of PET/CT, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, People's Republic of China
| | - Xia Chen
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, People's Republic of China
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Zhang Y, Li X, Lv Y, Gu X. Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma. Tomography 2020; 6:325-332. [PMID: 33364422 PMCID: PMC7744193 DOI: 10.18383/j.tom.2020.00039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The diagnosis of patients with suspected angiomyolipoma relies on the detection of abundant macroscopic intralesional fat, which is always of no use to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma and diagnosis of fp-AML excessively depends on individual experience. Texture analysis was proven to be a potentially useful biomarker for distinguishing between benign and malignant tumors because of its capability of providing objective and quantitative assessment of lesions by analyzing features that are not visible to the human eye. This review aimed to summarize the literature on the use of texture analysis to diagnose patients with fat-poor angiomyolipoma vs those with renal cell carcinoma and to evaluate its current application, limitations, and future challenges in order to avoid unnecessary surgical resection.
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Affiliation(s)
- Yuhan Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Xu Li
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
| | - Yang Lv
- Department of Anesthesia, The Second Hospital of Jilin University, Changchun, China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China; and
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Kothari G, Korte J, Lehrer EJ, Zaorsky NG, Lazarakis S, Kron T, Hardcastle N, Siva S. A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy. Radiother Oncol 2020; 155:188-203. [PMID: 33096167 DOI: 10.1016/j.radonc.2020.10.023] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND AND PURPOSE Radiomics allows extraction of quantifiable features from imaging. This study performs a systematic review and meta-analysis of the performance of radiomics based prognostic models in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS A literature review was performed following PRISMA guidelines. Medline, EMBASE and Cochrane databases were searched for articles investigating radiomics features predictive of overall survival (OS) in NSCLC treated with curative intent radiotherapy. A random-effects meta-analysis of Harrell's Concordance Index (C-index) was performed on the performance of radiomics models. RESULTS Of the 2746 articles retrieved, 40 studies of 55 datasets and 6223 patients were eligible for inclusion in the systematic review. There was significant heterogeneity in the methodology for feature selection and model development. Twelve datasets reported the C-index of radiomics based models in predicting OS and were included in the meta-analysis. The C-index random effects estimate was 0.57 (95% CI 0.53-0.62). There was significant heterogeneity (I2 = 70.3%). CONCLUSIONS Based on this review, radiomics based models for lung cancer have to date demonstrated modest prognostic capabilities. Future research should consider using standardised radiomics features, robust feature selection and model development, and deep learning techniques, absolving the need for pre-defined features, to improve imaging-based models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia.
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Biomedical Engineering, School of Engineering, University of Melbourne, Melbourne, Australia
| | - Eric J Lehrer
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Nicholas G Zaorsky
- Department of Radiation Oncology, Penn State Cancer Institute, Hershey, USA; Department of Public Health Sciences, Penn State College of Medicine, Hershey, USA
| | - Smaro Lazarakis
- Health Sciences Library, Peter MacCallum Cancer Centre, Parkville, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia; Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Peter MacCallum Cancer Centre, Melbourne, Australia
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Zhang N, Liang R, Gensheimer MF, Guo M, Zhu H, Yu J, Diehn M, Loo BW, Li R, Wu J. Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Am J Cancer Res 2020; 10:11707-11718. [PMID: 33052242 PMCID: PMC7546006 DOI: 10.7150/thno.50565] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/08/2020] [Indexed: 12/25/2022] Open
Abstract
Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.
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Qiu Q, Duan J, Deng H, Han Z, Gu J, Yue NJ, Yin Y. Development and Validation of a Radiomics Nomogram Model for Predicting Postoperative Recurrence in Patients With Esophageal Squamous Cell Cancer Who Achieved pCR After Neoadjuvant Chemoradiotherapy Followed by Surgery. Front Oncol 2020; 10:1398. [PMID: 32850451 PMCID: PMC7431604 DOI: 10.3389/fonc.2020.01398] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 07/02/2020] [Indexed: 12/24/2022] Open
Abstract
Background and purpose: Although patients with esophageal squamous cell carcinoma (ESCC) can achieve a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) followed by surgery, one-third of these patients with a pCR may still experience recurrence. The aim of this study is to develop and validate a predictive model to estimate recurrence-free survival (RFS) in those patients who achieved pCR. Materials and methods: Two hundred six patients with ESCC were enrolled and divided into a training cohort (n = 146) and a validation cohort (n = 60). Radiomic features were extracted from contrast-enhanced computed tomography (CT) images of each patient. Feature reduction was then implemented in two steps, including a multiple segmentation test and least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression method. A radiomics signature was subsequently constructed and evaluated. For better prediction performance, a clinical nomogram based on clinical risk factors and a nomogram incorporating the radiomics signature and clinical risk factors was built. Finally, the prediction models were further validated by calibration and the clinical usefulness was examined in the validation cohort to determine the optimal prediction model. Results: The radiomics signature was constructed using eight radiomic features and displayed a significant correlation with RFS. The nomogram incorporating the radiomics signature with clinical risk factors achieved optimal performance compared with the radiomics signature (P < 0.001) and clinical nomogram (P < 0.001) in both the training cohort [C-index (95% confidence interval [CI]), 0.746 (0.680-0.812) vs. 0.685 (0.620-0.750) vs. 0.614 (0.538-0.690), respectively] and validation cohort [C-index (95% CI), 0.724 (0.696-0.752) vs. 0.671 (0.624-0.718) vs. 0.629 (0.597-0.661), respectively]. The calibration curve and decision curve analysis revealed that the radiomics nomogram outperformed the other two models. Conclusions: A radiomics nomogram model incorporating radiomics features and clinical factors has been developed and has the improved ability to predict the postoperative recurrence risk in patients with ESCC who achieved pCR after nCRT followed by surgery.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Hongbin Deng
- Department of Medical Imaging Ultrasonography, Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhujun Han
- Department of Radiation Oncology, Yantai Yuhuangding Hospital, Yantai, China
| | - Jiabing Gu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Ning J Yue
- Department of Radiation Oncology, The Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Prognostic factors for overall survival of stage III non-small cell lung cancer patients on computed tomography: A systematic review and meta-analysis. Radiother Oncol 2020; 151:152-175. [PMID: 32710990 DOI: 10.1016/j.radonc.2020.07.030] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 07/15/2020] [Accepted: 07/15/2020] [Indexed: 12/15/2022]
Abstract
INTRODUCTION Prognosis prediction is central in treatment decision making and quality of life for non-small cell lung cancer (NSCLC) patients. However, conventional computed tomography (CT) related prognostic factors may not apply to the challenging stage III NSCLC group. The aim of this systematic review was therefore to identify and evaluate CT-related prognostic factors for overall survival (OS) of stage III NSCLC. METHODS The Medline, Embase, and Cochrane electronic databases were searched. After study selection, risk of bias was estimated for the included studies. Meta-analysis of univariate results was performed when sufficient data were available. RESULTS 1595 of the 11,996 retrieved records were selected for full text review, leading to inclusion of 65 studies that reported data of 144,513 stage III NSCLC patients andcompromising 26 unique CT-related prognostic factors. Relevance and validity varied substantially, few studies had low relevance and validity. Only four studies evaluated the added value of new prognostic factors compared with recognized clinical factors. Included studies suggested gross tumor volume (meta-analysis: HR = 1.22, 95%CI: 1.05-1.42), tumor diameter, nodal volume, and pleural effusion, are prognostic in patients treated with chemoradiation. Clinical T-stage and location (right/left) were likely not prognostic within stage III NSCLC. Inconclusive are several radiomic features, tumor volume, atelectasis, location (pulmonary lobes, central/peripheral), interstitial lung abnormalities, great vessel invasion, pit-fall sign, and cavitation. CONCLUSIONS Tumor-size and nodal size-related factors are prognostic for OS in stage III NSCLC. Future studies should carefully report study characteristics and contrast factors with guideline recognized factors to improve evidence evaluation and validation.
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2-[ 18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease. Methods 2020; 188:84-97. [PMID: 32497604 DOI: 10.1016/j.ymeth.2020.05.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[18F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[18F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.
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Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020; 196:879-887. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 04/15/2020] [Indexed: 02/07/2023]
Abstract
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy.
| | | | - Giovanni Pirrone
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
| | - Giovanna Sartor
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081, Aviano, PN, Italy
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Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol 2020; 72:238-250. [PMID: 32371013 DOI: 10.1016/j.semcancer.2020.04.002] [Citation(s) in RCA: 153] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 12/15/2022]
Abstract
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are usually performed through different imaging modalities such as mammography, magnetic resonance imaging and ultrasound. However, mammography and ultrasound-imaging techniques have limited sensitivity and specificity both in identifying lesions and in differentiating malign from benign lesions, especially in presence of dense breast parenchyma. Due to the higher resolution of magnetic resonance images, MRI represents the method with the higher specificity and sensitivity among all the available tools, in both lesions' identification and diagnosis. However, especially for diagnosis, even MRI has limitations that are only partially solved if combined with mammography. Unfortunately, due to the limits of all these imaging tools, in order to have a certain diagnosis, patients often receive painful and costly bioptics procedures. In this context, several computational approaches have been developed to increase sensitivity, while maintaining the same specificity, in BC diagnosis and screening. Amongst these, radiomics has been increasingly gaining ground in oncology to improve cancer diagnosis, prognosis and treatment. Radiomics derives multiple quantitative features from single or multiple medical imaging modalities, highlighting image traits which are not visible to the naked eye and hence significantly augmenting the discriminatory and predictive potential of medical imaging. This review article aims to summarize the state of the art in radiomics-based BC research. The dominating evidence extracted from the literature points towards a high potential of radiomics in disentangling malignant from benign breast lesions, classifying BC types and grades and also in predicting treatment response and recurrence risk. In the era of personalized medicine, radiomics has the potential to improve diagnosis, prognosis, prediction, monitoring, image-based intervention, and assessment of therapeutic response in BC.
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Affiliation(s)
- Allegra Conti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Iole Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179, Rome, Italy; Department of Medicine and Surgery, Saint Camillus International University of Health and Medical Sciences, Rome, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States.
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Hu J, Zhao Y, Li M, Liu Y, Wang F, Weng Q, You R, Cao D. Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours. Eur J Radiol 2020; 126:108929. [PMID: 32169748 DOI: 10.1016/j.ejrad.2020.108929] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 02/01/2020] [Accepted: 02/26/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate the performance of machine-learning-based computed tomography (CT) radiomic analysis to differentiate high-risk thymic epithelial tumours (TETs) from low-risk TETs according to the WHO classification. METHOD This retrospective study included 155 patients with a histologic diagnosis of high-risk TET (n = 72) and low-risk TET (n = 83) who underwent unenhanced CT (UECT) and contrast-enhanced CT (CECT). The radiomic features were extracted from the UECT and CECT of each patient at the largest cross-section of the lesion. The classification performance was evaluated with a nested leave-one-out cross-validation approach combining the least absolute shrinkage and selection operator feature selection and four classifiers: generalised linear model (GLM), k-nearest neighbor (KNN), support vector machine (SVM) and random forest (RF). The receiver-operating characteristic curve (ROC) and the area under the curve (AUC) were used to evaluate the performance of the classifiers. RESULTS The combination of UECT and CECT radiomic features demonstrated the best performance to differentiate high-risk TETs from low-risk TETs for all four classifiers. Among these classifiers, the RF had the highest AUC of 0.87, followed by GLM (AUC = 0.86), KNN (AUC = 0.86) and SVM (AUC = 0.84). CONCLUSIONS Machine learning-based CT radiomic analysis allows for the differentiation of high-risk TETs and low-risk TETs with excellent performance, representing a promising tool to assist clinical decision making in patients with TETs.
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Affiliation(s)
- Jianping Hu
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Yijing Zhao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Mengcheng Li
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Yin Liu
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Feng Wang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Qiang Weng
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Ruixiong You
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
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Krarup MMK, Nygård L, Vogelius IR, Andersen FL, Cook G, Goh V, Fischer BM. Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool. Radiother Oncol 2020; 144:72-78. [PMID: 31733491 DOI: 10.1016/j.radonc.2019.10.012] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/01/2019] [Accepted: 10/17/2019] [Indexed: 02/06/2023]
Abstract
AIM The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy. METHODS 18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant. RESULTS Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively). CONCLUSION The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.
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Affiliation(s)
- Marie Manon Krebs Krarup
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Lotte Nygård
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Ivan Richter Vogelius
- Department of Oncology, Rigshospitalet, Copenhagen University Hospital, Denmark; Faculty of Health and Medical Sciences, Copenhagen University, Denmark.
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
| | - Gary Cook
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Vicky Goh
- PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
| | - Barbara Malene Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen University Hospital, Denmark; PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, United Kingdom.
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Zhang R, Wang C, Cui K, Chen Y, Sun F, Sun X, Xing L. Prognostic Role Of Computed Tomography Textural Features In Early-Stage Non-Small Cell Lung Cancer Patients Receiving Stereotactic Body Radiotherapy. Cancer Manag Res 2019; 11:9921-9930. [PMID: 31819630 PMCID: PMC6883938 DOI: 10.2147/cmar.s220587] [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: 07/04/2019] [Accepted: 10/29/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose The imaging features of patients with early-stage non-small cell lung cancer (NSCLC) receiving stereotactic body radiotherapy (SBRT) are crucial for the decision-making process to establish a treatment plan. The purpose of this study was to predict the clinical outcomes of SBRT from the textural features of pretreatment computed tomography (CT) images. Patients and methods Forty-one early-stage NSCLC patients who received SBRT were included in this retrospective study. In total, 72 textural features were extracted from the pretreatment contrast-enhanced CT images. Survival analysis was used to identify high-risk groups for progression-free survival (PFS) and disease-specific survival (DSS). Receiver operating characteristic (ROC) curve analysis was utilized to estimate the diagnostic abilities of the textural parameters. Univariable and multivariable Cox regression analyses were performed to evaluate the predictors of PFS and DSS. Results Four parameters, including entropy (P=0.003), second angular moment (SAM) (P=0.04), high-intensity long-run emphasis (HILRE) (P=0.046) and long-run emphasis (LRE) (P=0.042), were significant prognostic features for PFS. In addition, contrast (P=0.008), coarseness (P=0.017), low-intensity zone emphasis (LIZE) (P=0.01) and large number emphasis (LNE) (P=0.046) were significant prognostic factors for DSS. In the ROC analysis, the area under the curve (AUC) of coarseness for local recurrence (LR) was 0.722 (0.528–0.916), and the AUC of entropy for lymph node metastasis (LNM) was 0.771 (0.556–0.987). The four highest AUCs for distant metastasis (DM) were 0.885 (0.784–0.985) for LNE, 0.846 (0.733–0.959) for SAM, 0.731 (0.500–0.961) for LRE and 0.731 (0.585–0.876) for contrast. In the multivariable analysis, smoking and entropy were independent prognostic factors for PFS. Conclusion This exploratory study reveals that textual features derived from pretreatment CT scans have prognostic value in early-stage NSCLC patients treated with SBRT.
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Affiliation(s)
- Ran Zhang
- Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.,Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Changbin Wang
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.,Department of Clinical Medicine, Jinan University, Jinan, Shandong, People's Republic of China
| | - Kai Cui
- Department of Clinical Medicine, Jinan University, Jinan, Shandong, People's Republic of China.,Department of Nuclear Medicine, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Yicong Chen
- Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.,Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Fenghao Sun
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.,Department of Clinical Medicine, Weifang Medical University, Weifang, Shandong, People's Republic of China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
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Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L, Sun Z, Wu J, Diehn M, Loo BW, Plichta KA, Seyedin SN, Gannon M, Cabel KR, Kim Y, Wu X. Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep 2019; 9:17286. [PMID: 31754135 PMCID: PMC6872742 DOI: 10.1038/s41598-019-53461-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 10/31/2019] [Indexed: 12/11/2022] Open
Abstract
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/computed tomography (PET/CT) images have predictive power for NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive power compared to hand-crafted radiomics features. Here we show that CNNs trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on pre-treatment PET-CT images of 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET and CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-Net algorithm has not seen any other clinical information (e.g. survival, age, smoking history, etc.) than the images and the corresponding tumor contours provided by physicians. In addition, we observed the same trend by validating the U-Net features against an extramural data set provided by Stanford Cancer Institute. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of metastasis and recurrence appear to match with the regions where the U-Net features identified patterns that predicted higher likelihoods of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination. For example, the deep learned PET/CT features can not only predict survival but also visualize high-risk regions within or adjacent to the primary tumor and hence potentially impact therapeutic outcomes by optimal selection of therapeutic strategy or first-line therapy adjustment.
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Affiliation(s)
- Stephen Baek
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
- University of Iowa, Department of Electrical and Computer Engineering, Iowa City, IA, 52242, United States
| | - Yusen He
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
| | - Bryan G Allen
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - John M Buatti
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Brian J Smith
- University of Iowa, Department of Biostatistics, Iowa City, IA, 52242, United States
| | - Ling Tong
- University of Iowa, Department of Business Analytics, Iowa City, IA, 52242, United States
| | - Zhiyu Sun
- University of Iowa, Department of Industrial and Systems Engineering, Iowa City, IA, 52242, United States
| | - Jia Wu
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Maximilian Diehn
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Billy W Loo
- Stanford University, Stanford Cancer Institute, Palo Alto, CA, 94304, United States
| | - Kristin A Plichta
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Steven N Seyedin
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Maggie Gannon
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Katherine R Cabel
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States
| | - Yusung Kim
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States.
| | - Xiaodong Wu
- University of Iowa, Department of Radiation Oncology, Iowa City, IA, 52242, United States.
- University of Iowa, Department of Electrical and Computer Engineering, Iowa City, IA, 52242, United States.
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Ger RB, Zhou S, Elgohari B, Elhalawani H, Mackin DM, Meier JG, Nguyen CM, Anderson BM, Gay C, Ning J, Fuller CD, Li H, Howell RM, Layman RR, Mawlawi O, Stafford RJ, Aerts H, Court LE. Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients. PLoS One 2019; 14:e0222509. [PMID: 31536526 PMCID: PMC6752873 DOI: 10.1371/journal.pone.0222509] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 08/31/2019] [Indexed: 12/22/2022] Open
Abstract
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables-HPV status and tumor volume-were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
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Affiliation(s)
- Rachel B. Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Shouhao Zhou
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Baher Elgohari
- Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Hesham Elhalawani
- Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Dennis M. Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Joseph G. Meier
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Callistus M. Nguyen
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Brian M. Anderson
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Casey Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Clifton D. Fuller
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rebecca M. Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rick R. Layman
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Osama Mawlawi
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - R. Jason Stafford
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Hugo Aerts
- Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, Shrager JB, Napel S, Nair VS. Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology 2019; 293:451-459. [PMID: 31526257 DOI: 10.1148/radiol.2019190357] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Primary tumor maximum standardized uptake value is a prognostic marker for non-small cell lung cancer. In the setting of malignancy, bone marrow activity from fluorine 18-fluorodeoxyglucose (FDG) PET may be informative for clinical risk stratification. Purpose To determine whether integrating FDG PET radiomic features of the primary tumor, tumor penumbra, and bone marrow identifies lung cancer disease-free survival more accurately than clinical features alone. Materials and Methods Patients were retrospectively analyzed from two distinct cohorts collected between 2008 and 2016. Each tumor, its surrounding penumbra, and bone marrow from the L3-L5 vertebral bodies was contoured on pretreatment FDG PET/CT images. There were 156 bone marrow and 512 tumor and penumbra radiomic features computed from the PET series. Randomized sparse Cox regression by least absolute shrinkage and selection operator identified features that predicted disease-free survival in the training cohort. Cox proportional hazards models were built and locked in the training cohort, then evaluated in an independent cohort for temporal validation. Results There were 227 patients analyzed; 136 for training (mean age, 69 years ± 9 [standard deviation]; 101 men) and 91 for temporal validation (mean age, 72 years ± 10; 91 men). The top clinical model included stage; adding tumor region features alone improved outcome prediction (log likelihood, -158 vs -152; P = .007). Adding bone marrow features continued to improve performance (log likelihood, -158 vs -145; P = .001). The top model integrated stage, two bone marrow texture features, one tumor with penumbra texture feature, and two penumbra texture features (concordance, 0.78; 95% confidence interval: 0.70, 0.85; P < .001). This fully integrated model was a predictor of poor outcome in the independent cohort (concordance, 0.72; 95% confidence interval: 0.64, 0.80; P < .001) and a binary score stratified patients into high and low risk of poor outcome (P < .001). Conclusion A model that includes pretreatment fluorine 18-fluorodeoxyglucose PET texture features from the primary tumor, tumor penumbra, and bone marrow predicts disease-free survival of patients with non-small cell lung cancer more accurately than clinical features alone. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Sarah A Mattonen
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Guido A Davidzon
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Jalen Benson
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Ann N C Leung
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Minal Vasanawala
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - George Horng
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Joseph B Shrager
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Sandy Napel
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
| | - Viswam S Nair
- From the Department of Radiology, James H. Clark Center (S.A.M., A.N.C.L., S.N., V.S.N.), Division of Nuclear Medicine, Department of Radiology (G.A.D.), and Division of Thoracic Surgery, Department of Cardiothoracic Surgery (J.B., J.B.S.), Stanford University, 318 Campus Dr, Room S355, Stanford, CA 94305; Palo Alto VA Health Care System, Palo Alto, Calif (M.V.); California Pacific Medical Center, San Francisco, Calif (G.H.); and Section of Pulmonary & Critical Care Medicine, Moffitt Cancer Center & Research Institute; Morsani College of Medicine, University of South Florida, Tampa, Fla (V.S.N.)
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Ger RB, Meier JG, Pahlka RB, Gay S, Mumme R, Fuller CD, Li H, Howell RM, Layman RR, Stafford RJ, Zhou S, Mawlawi O, Court LE. Effects of alterations in positron emission tomography imaging parameters on radiomics features. PLoS One 2019; 14:e0221877. [PMID: 31487307 PMCID: PMC6728031 DOI: 10.1371/journal.pone.0221877] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 08/16/2019] [Indexed: 01/11/2023] Open
Abstract
Radiomics studies require large patient cohorts, which often include patients imaged using different imaging protocols. We aimed to determine the impact of variability in imaging protocol parameters and interscanner variability using a phantom that produced feature values similar to those of patients. Positron emission tomography (PET) scans of a Hoffman brain phantom were acquired on GE Discovery 710, Siemens mCT, and Philips Vereos scanners. A standard-protocol scan was acquired on each machine, and then each parameter that could be changed was altered individually. The phantom was contoured with 10 regions of interest (ROIs). Values for 45 features with 2 different preprocessing techniques were extracted for each image. To determine the impact of each parameter on the reliability of each radiomics feature, the intraclass correlation coefficient (ICC) was calculated with the ROIs as the subjects and the parameter values as the raters. For interscanner comparisons, we compared the standard deviation of each radiomics feature value from the standard-protocol images to the standard deviation of the same radiomics feature from PET scans of 224 patients with non-small cell lung cancer. When the pixel size was resampled prior to feature extraction, all features had good reliability (ICC > 0.75) for the field of view and matrix size. The time per bed position had excellent reliability (ICC > 0.9) on all features. When the filter cutoff was restricted to values below 6 mm, all features had good reliability. Similarly, when subsets and iterations were restricted to reasonable values used in clinics, almost all features had good reliability. The average ratio of the standard deviation of features on the phantom scans to that of the NSCLC patient scans was 0.73 using fixed-bin-width preprocessing and 0.92 using 64-level preprocessing. Most radiomics feature values had at least good reliability when imaging protocol parameters were within clinically used ranges. However, interscanner variability was about equal to interpatient variability; therefore, caution must be used when combining patients scanned on equipment from different vendors in radiomics data sets.
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Affiliation(s)
- Rachel B. Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- * E-mail:
| | - Joseph G. Meier
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Raymond B. Pahlka
- Department of Radiology, Texas Children’s Hospital, Houston, Texas, United States of America
| | - Skylar Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Raymond Mumme
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Clifton D. Fuller
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rebecca M. Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
| | - Rick R. Layman
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - R. Jason Stafford
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Shouhao Zhou
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Osama Mawlawi
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Laurence E. Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
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van Timmeren JE, Carvalho S, Leijenaar RTH, Troost EGC, van Elmpt W, de Ruysscher D, Muratet JP, Denis F, Schimek-Jasch T, Nestle U, Jochems A, Woodruff HC, Oberije C, Lambin P. Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics. PLoS One 2019; 14:e0217536. [PMID: 31158263 PMCID: PMC6546238 DOI: 10.1371/journal.pone.0217536] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 05/11/2019] [Indexed: 12/22/2022] Open
Abstract
Background Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.
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Affiliation(s)
- Janna E. van Timmeren
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
- * E-mail:
| | - Sara Carvalho
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Ralph T. H. Leijenaar
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Esther G. C. Troost
- OncoRay–National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Cal Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology—OncoRay, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT) Partner Site Dresden, Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Helmholtz Association / Helmholtz-Zentrum Dresden–Rossendorf (HZDR), Dresden, Germany
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (MAASTRO clinic), GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Fabrice Denis
- Centre Jean Bernard, Clinique Victor Hugo, Le Mans, France
| | - Tanja Schimek-Jasch
- Department for Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany
| | - Ursula Nestle
- Department for Radiation Oncology, University Medical Center Freiburg, Freiburg, Germany
| | - Arthur Jochems
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Henry C. Woodruff
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Cary Oberije
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab: Decision Support for Precision Medicine, GROW—School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
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Milgrom SA, Elhalawani H, Lee J, Wang Q, Mohamed ASR, Dabaja BS, Pinnix CC, Gunther JR, Court L, Rao A, Fuller CD, Akhtari M, Aristophanous M, Mawlawi O, Chuang HH, Sulman EP, Lee HJ, Hagemeister FB, Oki Y, Fanale M, Smith GL. A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma. Sci Rep 2019; 9:1322. [PMID: 30718585 PMCID: PMC6361903 DOI: 10.1038/s41598-018-37197-z] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 12/03/2018] [Indexed: 12/14/2022] Open
Abstract
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUVmax). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.
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Affiliation(s)
- Sarah A Milgrom
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.
| | - Hesham Elhalawani
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Joonsang Lee
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Qianghu Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Bouthaina S Dabaja
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Chelsea C Pinnix
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Jillian R Gunther
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence Court
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.,Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Mani Akhtari
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Michalis Aristophanous
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Osama Mawlawi
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hubert H Chuang
- Department of Nuclear Medicine, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Erik P Sulman
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hun J Lee
- Department of Lymphoma/Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Frederick B Hagemeister
- Department of Lymphoma/Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yasuhiro Oki
- Department of Lymphoma/Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michelle Fanale
- Department of Lymphoma/Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Grace L Smith
- Department of Radiation Oncology, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, USA
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Gu J, Zhu J, Qiu Q, Wang Y, Bai T, Duan J, Yin Y. The Feasibility Study of Megavoltage Computed Tomographic (MVCT) Image for Texture Feature Analysis. Front Oncol 2018; 8:586. [PMID: 30568918 PMCID: PMC6290333 DOI: 10.3389/fonc.2018.00586] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 11/21/2018] [Indexed: 11/13/2022] Open
Abstract
Purpose: To determine whether radiomics texture features can be reproducibly obtained from megavoltage computed tomographic (MVCT) images acquired by Helical TomoTherapy (HT) with different imaging conditions. Methods: For each of the 195 textures enrolled, the mean intrapatient difference, which is considered to be the benchmark for reproducibility, was calculated from the MVCT images of 22 patients with early-stage non-small-cell lung cancer. Test–retest MVCT images of an in-house designed phantom were acquired to determine the concordance correlation coefficient (CCC) for these 195 texture features. Features with high reproducibility (CCC > 0.9) in the phantom test–retest set were investigated for sensitivities to different imaging protocols, scatter levels, and motion frequencies using a wood phantom and in-vitro animal tissues. Results: Of the 195 features, 165 (85%) features had CCC > 0.9. For the wood phantom, 124 features were reproducible in two kinds of scatter materials, and further investigations were performed on these features. For animal tissues, 108 features passed the criteria for reproducibility when one layer of scatter was covered, while 106 and 108 features of in-vitro liver and bone passed with two layers of scatter, respectively. Considering the effect of differing acquisition pitch (AcP), 97 features extracted from wood passed, while 103 and 59 features extracted from in-vitro liver and bone passed, respectively. Different reconstruction intervals (RI) had a small effect on the stability of the feature value. When AcP and RI were held consistent without motion, all 124 features calculated from wood passed, and a majority (122 of 124) of the features passed when imaging with a “fine” AcP with different RIs. However, only 55 and 40 features passed with motion frequencies of 20 and 25 beats per minute, respectively. Conclusion: Motion frequency has a significant impact on MVCT texture features, and features from MVCT were more reproducibility in different scatter conditions than those from CBCT. Considering the effects of AcP and RI, the scanning protocols should be kept consistent when MVCT images are used for feature analysis. Some radiomics features from HT MVCT images are reproducible and could be used for creating clinical prediction models in the future.
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Affiliation(s)
- Jiabing Gu
- School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, China.,Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Jian Zhu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Qingtao Qiu
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yungang Wang
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Tong Bai
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Jinghao Duan
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital Affiliated to Shandong University, Shandong Academy of Medical Sciences, Jinan, China
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Ger RB, Craft DF, Mackin DS, Zhou S, Layman RR, Jones AK, Elhalawani H, Fuller CD, Howell RM, Li H, Stafford RJ, Court LE. Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis. Comput Med Imaging Graph 2018; 69:134-139. [PMID: 30268005 PMCID: PMC6217839 DOI: 10.1016/j.compmedimag.2018.09.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 08/14/2018] [Accepted: 09/06/2018] [Indexed: 12/28/2022]
Abstract
Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed.
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Affiliation(s)
- Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States.
| | - Daniel F Craft
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Shouhao Zhou
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1411, Houston, Texas 77030, United States
| | - Rick R Layman
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - A Kyle Jones
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 0097, Houston, Texas 77030, United States
| | - Clifton D Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 0097, Houston, Texas 77030, United States
| | - Rebecca M Howell
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States
| | - R Jason Stafford
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1420, Houston, Texas 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, 6767 Bertner Ave., Houston, Texas 77030, United States; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, Texas 77030, United States
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Ercelep O, Alan O, Sahin D, Telli TA, Salva H, Tuylu TB, Babacan NA, Kaya S, Dane F, Ones T, Alkis H, Adli M, Yumuk F. Effect of PET/CT standardized uptake values on complete response to treatment before definitive chemoradiotherapy in stage III non-small cell lung cancer. Clin Transl Oncol 2018; 21:499-504. [PMID: 30229391 DOI: 10.1007/s12094-018-1949-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Accepted: 09/07/2018] [Indexed: 11/29/2022]
Abstract
PURPOSE The standard treatment for patients with stage III non-small cell lung cancer (NSCLC), unsuitable for resection and with good performance, is definitive radiotherapy with cisplatin-based chemotherapy. Our aim is to evaluate the effect of the maximum value of standardized uptake values (SUVmax) of the primary tumor in positron emission tomography-computed tomography (PET/CT) before treatment on complete response (CR) and overall survival. METHODS The data of 73 stage III NSCLC patients treated with concurrent definitive chemoradiotherapy (CRT) between 2008 and 2017 and had PET/CT staging in the pretreatment period were evaluated. ROC curve analysis was performed to determine the ideal cut-off value of pretreatment SUVmax to predict CR. RESULTS Median age was 58 years (range 27-83 years) and 66 patients were male (90.4%). Median follow-up time was 18 months (range 3-98 months); median survival was 23 months. 1-year overall survival (OS) rate and 5-year OS rate were 72 and 19%, respectively. Median progression-free survival (PFS) was 9 months; 1-year PFS rate and 5-year PFS rate were 38 and 19%, respectively. The ideal cut-off value of pretreatment SUVmax that predicted the complete response of CRT was 12 in the ROC analysis [AUC 0.699 (0.550-0.833)/P < 0.01] with a sensitivity of 83%, and specificity of 55%. In patients with SUVmax < 12, CR rate was 60%, while, in patients with SUV ≥ 12, it was only 19% (P = 0.002). Median OS was 26 months in patients with pretreatment SUVmax < 12, and 21 months in patients with SUVmax ≥ 12 (HR = 2.93; 95% CI 17.24-28.75; P = 0.087). CR rate of the whole patient population was 26%, and it was the only factor that showed a significant benefit on survival in both univariate and multivariate analyses. CONCLUSION Pretreatment SUVmax of the primary tumor in PET/CT may predict CR in stage III NSCLC patients who were treated with definitive CRT. Having clinical CR is the only positive predictive factor for prolonged survival.
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Affiliation(s)
- O Ercelep
- Department of Medical Oncology, Pendik Education and Research Hospital, Marmara University, Istanbul, Turkey.
| | - O Alan
- Department of Medical Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - D Sahin
- Department of Internal Medicine, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - T A Telli
- Department of Medical Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - H Salva
- Department of Internal Medicine, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - T B Tuylu
- Department of Medical Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - N A Babacan
- Department of Medical Oncology, Pendik Education and Research Hospital, Marmara University, Istanbul, Turkey
| | - S Kaya
- Department of Medical Oncology, Pendik Education and Research Hospital, Marmara University, Istanbul, Turkey
| | - F Dane
- Department of Medical Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - T Ones
- Department of Nuclear Medicine, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - H Alkis
- Department of Radiation Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - M Adli
- Department of Radiation Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
| | - F Yumuk
- Department of Medical Oncology, Faculty of Medicine, Marmara University, Istanbul, Turkey
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Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 2018. [DOI: 10.1007/s40336-018-0299-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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40
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Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies. Sci Rep 2018; 8:13047. [PMID: 30158540 PMCID: PMC6115360 DOI: 10.1038/s41598-018-31509-z] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 08/16/2018] [Indexed: 12/19/2022] Open
Abstract
Radiomics has shown promise in improving models for predicting patient outcomes. However, to maximize the information gain of the radiomics features, especially in larger patient cohorts, the variability in radiomics features owing to differences between scanners and scanning protocols must be accounted for. To this aim, the imaging variability of radiomics feature values was evaluated on 100 computed tomography scanners at 35 clinics by imaging a radiomics phantom using a controlled protocol and the commonly used chest and head protocols of the local clinic. We used a linear mixed-effects model to determine the degree to which the manufacturer and individual scanners contribute to the overall variability. Using a controlled protocol reduced the overall variability by 57% and 52% compared to the local chest and head protocols respectively. The controlled protocol also reduced the relative contribution of the manufacturer to the total variability. For almost all variabilities (manufacturer, scanner, and residual with different preprocesssing), the controlled protocol scans had a significantly smaller variability than the local protocol scans did. For most radiomics features, the imaging variability was small relative to the inter-patient feature variability in non-small cell lung cancer and head and neck squamous cell carcinoma patient cohorts. From this study, we conclude that using controlled scans can reduce the variability in radiomics features, and our results demonstrate the importance of using controlled protocols in prospective radiomics studies.
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Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer. Nucl Med Commun 2018; 39:732-740. [DOI: 10.1097/mnm.0000000000000860] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy. Sci Rep 2018; 8:9902. [PMID: 29967326 PMCID: PMC6028651 DOI: 10.1038/s41598-018-28243-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 06/19/2018] [Indexed: 01/09/2023] Open
Abstract
This study was designed to evaluate the predictive performance of 18F-fluorodeoxyglucose positron emission tomography (PET)-based radiomic features for local control of esophageal cancer treated with concurrent chemoradiotherapy (CRT). For each of the 30 patients enrolled, 440 radiomic features were extracted from both pre-CRT and mid-CRT PET images. The top 25 features with the highest areas under the receiver operating characteristic curve for identifying local control status were selected as discriminative features. Four machine-learning methods, random forest (RF), support vector machine, logistic regression, and extreme learning machine, were used to build predictive models with clinical features, radiomic features or a combination of both. An RF model incorporating both clinical and radiomic features achieved the best predictive performance, with an accuracy of 93.3%, a specificity of 95.7%, and a sensitivity of 85.7%. Based on risk scores of local failure predicted by this model, the 2-year local control rate and PFS rate were 100.0% (95% CI 100.0–100.0%) and 52.2% (31.8–72.6%) in the low-risk group and 14.3% (0.0–40.2%) and 0.0% (0.0–40.2%) in the high-risk group, respectively. This model may have the potential to stratify patients with different risks of local failure after CRT for esophageal cancer, which may facilitate the delivery of personalized treatment.
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Grootjans W, de Geus-Oei LF, Bussink J. Image-guided adaptive radiotherapy in patients with locally advanced non-small cell lung cancer: the art of PET. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF RADIOPHARMACEUTICAL CHEMISTRY AND BIOLOGY 2018; 62:369-384. [PMID: 29869486 DOI: 10.23736/s1824-4785.18.03084-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
With a worldwide annual incidence of 1.8 million cases, lung cancer is the most diagnosed form of cancer in men and the third most diagnosed form of cancer in women. Histologically, 80-85% of all lung cancers can be categorized as non-small cell lung cancer (NSCLC). For patients with locally advanced NSCLC, standard of care is fractionated radiotherapy combined with chemotherapy. With the aim of improving clinical outcome of patients with locally advanced NSCLC, combined and intensified treatment approaches are increasingly being used. However, given the heterogeneity of this patient group with respect to tumor biology and subsequent treatment response, a personalized treatment approach is required to optimize therapeutic effect and minimize treatment induced toxicity. Medical imaging, in particular positron emission tomography (PET), before and during the course radiotherapy is increasingly being used to personalize radiotherapy. In this setting, PET imaging can be used to improve delineation of target volumes, employ molecularly-guided dose painting strategies, early response monitoring, prediction and monitoring of treatment-related toxicity. The concept of PET image-guided adaptive radiotherapy (IGART) is an interesting approach to personalize radiotherapy for patients with locally advanced NSCLC, which might ultimately contribute to improved clinical outcomes and reductions in frequency of treatment-related adverse events in this patient group. In this review, we provide a comprehensive overview of available clinical data supporting the use of PET imaging for IGART in patients with locally advanced NSCLC.
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Affiliation(s)
- Willem Grootjans
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands -
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
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Lee JW, Lee SM. Radiomics in Oncological PET/CT: Clinical Applications. Nucl Med Mol Imaging 2018; 52:170-189. [PMID: 29942396 PMCID: PMC5995782 DOI: 10.1007/s13139-017-0500-y] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 09/22/2017] [Accepted: 09/29/2017] [Indexed: 12/11/2022] Open
Abstract
18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, 25, Simgok-ro 100 Gil 25, Seo-gu, Incheon, 22711 South Korea
- Institute for Integrative Medicine, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon, South Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, South Korea
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Hou Z, Li S, Ren W, Liu J, Yan J, Wan S. Radiomic analysis in T2W and SPAIR T2W MRI: predict treatment response to chemoradiotherapy in esophageal squamous cell carcinoma. J Thorac Dis 2018; 10:2256-2267. [PMID: 29850130 DOI: 10.21037/jtd.2018.03.123] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background To investigate the capability of radiomic analysis using T2-weighted (T2W) and spectral attenuated inversion-recovery T2-weighted (SPAIR T2W) magnetic resonance imaging (MRI) for predicting the therapeutic response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Methods Pretreatment T2W- and SPAIR T2W-MRI of 68 ESCC patients (37 responders, 31 nonresponders) were analyzed. A number of 138 radiomic features were extracted from each image sequence respectively. Kruskal-Wallis test were performed to evaluate the capability of each feature on treatment response classification. Sensitivity and specificity for each of the studied features were derived using receiver operating characteristic (ROC) analysis. Support vector machine (SVM) and artificial neural network (ANN) models were constructed based on the training set (23 responders, 20 nonresponders) for the prediction of treatment response, and then the testing set (14 responders, 11 nonresponders) validated the reliability of the models. Comparison between the performances of the models was performed by using McNemar's test. Results Radiomic analysis showed significance in the prediction of treatment response. The analyses showed that complete responses (CRs) versus stable diseases (SDs), partial responses (PRs) versus SDs, and responders (CRs and PRs) versus nonresponders (SDs) could be differentiated by 26, 17, and 33 features (T2W: 11/11/15, SPAIR T2W: 15/6/18), respectively. The prediction models (ANN and SVM) based on features extracted from SPAIR T2W sequence (SVM: 0.929, ANN: 0.883) showed higher accuracy than those derived from T2W (SVM: 0.893, ANN: 0.861). No statistical difference was observed in the performance of the two classifiers (P=0.999). Conclusions Radiomic analysis based on pretreatment T2W- and SPAIR T2W-MRI can be served as imaging biomarkers to predict treatment response to CRT in ESCC patients.
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Affiliation(s)
- Zhen Hou
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
| | - Shuangshuang Li
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Wei Ren
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Juan Liu
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Jing Yan
- The Comprehensive Cancer Centre of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210000, China
| | - Suiren Wan
- State Key Laboratory of Bioelectronics, Laboratory for Medical Electronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China
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Oikonomou A, Khalvati F, Tyrrell PN, Haider MA, Tarique U, Jimenez-Juan L, Tjong MC, Poon I, Eilaghi A, Ehrlich L, Cheung P. Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep 2018; 8:4003. [PMID: 29507399 PMCID: PMC5838232 DOI: 10.1038/s41598-018-22357-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/19/2018] [Indexed: 02/08/2023] Open
Abstract
We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 patients with 172 lung cancers, who underwent SBRT were retrospectively included. Radiomics were applied on PET/CT. Principal components (PC) for 42 CT and PET-derived features were examined to determine which ones accounted for most of variability. Survival analysis quantified ability of radiomics and SUVmax to predict outcome. PCs including homogeneity, size, maximum intensity, mean and median gray level, standard deviation, entropy, kurtosis, skewness, morphology and asymmetry were included in prediction models for regional control (RC) [PC4-HR:0.38, p = 0.02], distant control (DC) [PC4-HR:0.51, p = 0.02 and PC1-HR:1.12, p = 0.01], recurrence free probability (RFP) [PC1-HR:1.08, p = 0.04], disease specific survival (DSS) [PC2-HR:1.34, p = 0.03 and PC3-HR:0.64, p = 0.02] and overall survival (OS) [PC4-HR:0.45, p = 0.004 and PC3-HR:0.74, p = 0.02]. In combined analysis with SUVmax, PC1 lost predictive ability over SUVmax for RFP [HR:1.1, p = 0.04] and DC [HR:1.13, p = 0.002], while PC4 remained predictive of DC independent of SUVmax [HR:0.5, p = 0.02]. Radiomics remained the only predictors of OS, DSS and RC. Neither SUVmax nor radiomics predicted recurrence free survival. Radiomics on PET/CT provided complementary information for prediction of control and survival in SBRT-treated lung cancer patients.
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Affiliation(s)
- Anastasia Oikonomou
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
| | - Farzad Khalvati
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Pascal N Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Masoom A Haider
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Usman Tarique
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Laura Jimenez-Juan
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Michael C Tjong
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Ian Poon
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Armin Eilaghi
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Lisa Ehrlich
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
| | - Patrick Cheung
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada
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Effect of tube current on computed tomography radiomic features. Sci Rep 2018; 8:2354. [PMID: 29403060 PMCID: PMC5799381 DOI: 10.1038/s41598-018-20713-6] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 01/10/2018] [Indexed: 12/24/2022] Open
Abstract
Variability in the x-ray tube current used in computed tomography may affect quantitative features extracted from the images. To investigate these effects, we scanned the Credence Cartridge Radiomics phantom 12 times, varying the tube current from 25 to 300 mA∙s while keeping the other acquisition parameters constant. For each of the scans, we extracted 48 radiomic features from the categories of intensity histogram (n = 10), gray-level run length matrix (n = 11), gray-level co-occurrence matrix (n = 22), and neighborhood gray tone difference matrix (n = 5). To gauge the size of the tube current effects, we scaled the features by the coefficient of variation of the corresponding features extracted from images of non-small cell lung cancer tumors. Variations in the tube current had more effect on features extracted from homogeneous materials (acrylic, sycamore wood) than from materials with more tissue-like textures (cork, rubber particles). Thirty-eight of the 48 features extracted from acrylic were affected by current reductions compared with only 2 of the 48 features extracted from rubber particles. These results indicate that variable x-ray tube current is unlikely to have a large effect on radiomic features extracted from computed tomography images of textured objects such as tumors.
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Jensen GL, Yost CM, Mackin DS, Fried DV, Zhou S, Court LE, Gomez DR. Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer. Radiother Oncol 2018; 126:362-367. [DOI: 10.1016/j.radonc.2017.11.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 07/31/2017] [Accepted: 11/13/2017] [Indexed: 01/24/2023]
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49
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Tang C, Hobbs B, Amer A, Li X, Behrens C, Canales JR, Cuentas EP, Villalobos P, Fried D, Chang JY, Hong DS, Welsh JW, Sepesi B, Court L, Wistuba II, Koay EJ. Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. Sci Rep 2018; 8:1922. [PMID: 29386574 PMCID: PMC5792427 DOI: 10.1038/s41598-018-20471-5] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Accepted: 01/09/2018] [Indexed: 12/17/2022] Open
Abstract
With increasing use of immunotherapy agents, pretreatment strategies for identifying responders and non-responders is useful for appropriate treatment assignment. We hypothesize that the local immune micro-environment of NSCLC is associated with patient outcomes and that these local immune features exhibit distinct radiologic characteristics discernible by quantitative imaging metrics. We assembled two cohorts of NSCLC patients treated with definitive surgical resection and extracted quantitative parameters from pretreatment CT imaging. The excised primary tumors were then quantified for percent tumor PDL1 expression and density of tumor-infiltrating lymphocyte (via CD3 count) utilizing immunohistochemistry and automated cell counting. Associating these pretreatment radiomics parameters with tumor immune parameters, we developed an immune pathology-informed model (IPIM) that separated patients into 4 clusters (designated A-D) utilizing 4 radiomics features. The IPIM designation was significantly associated with overall survival in both training (5 year OS: 61%, 41%, 50%, and 91%, for clusters A-D, respectively, P = 0.04) and validation (5 year OS: 55%, 72%, 75%, and 86%, for clusters A-D, respectively, P = 0.002) cohorts and immune pathology (all P < 0.05). Specifically, we identified a favorable outcome group characterized by low CT intensity and high heterogeneity that exhibited low PDL1 and high CD3 infiltration, suggestive of a favorable immune activated state. We have developed a NSCLC radiomics signature based on the immune micro-environment and patient outcomes. This manuscript demonstrates model creation and validation in independent cohorts.
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Affiliation(s)
- Chad Tang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Brian Hobbs
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ahmed Amer
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiao Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carmen Behrens
- Department of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jaime Rodriguez Canales
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Edwin Parra Cuentas
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pamela Villalobos
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David Fried
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joe Y Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James W Welsh
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Boris Sepesi
- Department of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Ger RB, Cardenas CE, Anderson BM, Yang J, Mackin DS, Zhang L, Court LE. Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. J Vis Exp 2018. [PMID: 29364284 DOI: 10.3791/57132] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's graphical user interface (GUI) and to demonstrate how IBEX calculated features have been used in clinical studies. IBEX allows for the import of DICOM images with DICOM radiation therapy structure files or Pinnacle files. Once the images are imported, IBEX has tools within the Data Selection GUI to manipulate the viewing of the images, measure voxel values and distances, and create and edit contours. IBEX comes with 27 preprocessing and 132 feature choices to design feature sets. Each preprocessing and feature category has parameters that can be altered. The output from IBEX is a spreadsheet that contains: 1) each feature from the feature set calculated for each contour in a data set, 2) image information about each contour in a data set, and 3) a summary of the preprocessing and features used with their selected parameters. Features calculated from IBEX have been used in studies to test the variability of features under different imaging conditions and in survival models to improve current clinical models.
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Affiliation(s)
- Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Brian M Anderson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Lifei Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center; UTHealth Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center;
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