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Buchert R, Szabo B, Kovacs A, Buddenkotte T, Mathies F, Karimzadeh A, Lehnert W, Klutmann S, Forgacs A, Apostolova I. Dopamine Transporter SPECT with 12-Minute Scan Duration Using Multiple-Pinhole Collimators. J Nucl Med 2024; 65:446-452. [PMID: 38238040 DOI: 10.2967/jnumed.123.266276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/28/2023] [Indexed: 03/03/2024] Open
Abstract
This study evaluated the potential to reduce the scan duration in dopamine transporter (DAT) SPECT when using a second-generation multiple-pinhole (MPH) collimator designed for brain SPECT with improved count sensitivity and improved spatial resolution compared with parallel-hole and fanbeam collimators. Methods: The retrospective study included 640 consecutive clinical DAT SPECT studies that had been acquired in list mode with a triple-head SPECT system with MPH collimators and a 30-min net scan duration after injection of 181 ± 10 MBq of [123I]FP-CIT. Raw data corresponding to scan durations of 20, 15, 12, 8, 6, and 4 min were obtained by restricting the events to a proportionally reduced time interval of the list-mode data for each projection angle. SPECT images were reconstructed iteratively with the same parameter settings irrespective of scan duration. The resulting 5,120 SPECT images were assessed for a neurodegeneration-typical reduction in striatal signal by visual assessment, conventional specific binding ratio analysis, and a deep convolutional neural network trained on 30-min scans. Results: Regarding visual interpretation, image quality was considered diagnostic for all 640 patients down to a 12-min scan duration. The proportion of discrepant visual interpretations between 30 and 12 min (1.2%) was not larger than the proportion of discrepant visual interpretations between 2 reading sessions of the same reader at a 30-min scan duration (1.5%). Agreement with the putamen specific binding ratio from the 30-min images was better than expected for 5% test-retest variability down to a 10-min scan duration. A relevant change in convolutional neural network-based automatic classification was observed at a 6-min scan duration or less. Conclusion: The triple-head SPECT system with MPH collimators allows reliable DAT SPECT after administration of about 180 MBq of [123I]FP-CIT with a 12-min scan duration.
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Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Balazs Szabo
- Mediso Medical Imaging Systems, Budapest, Hungary
| | - Akos Kovacs
- Mediso Medical Imaging Systems, Budapest, Hungary
| | - Thomas Buddenkotte
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Franziska Mathies
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Amir Karimzadeh
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Wencke Lehnert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | - Susanne Klutmann
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
| | | | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; and
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2
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Buddenkotte T, Rundo L, Woitek R, Escudero Sanchez L, Beer L, Crispin-Ortuzar M, Etmann C, Mukherjee S, Bura V, McCague C, Sahin H, Pintican R, Zerunian M, Allajbeu I, Singh N, Sahdev A, Havrilesky L, Cohn DE, Bateman NW, Conrads TP, Darcy KM, Maxwell GL, Freymann JB, Öktem O, Brenton JD, Sala E, Schönlieb CB. Deep learning-based segmentation of multisite disease in ovarian cancer. Eur Radiol Exp 2023; 7:77. [PMID: 38057616 PMCID: PMC10700248 DOI: 10.1186/s41747-023-00388-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 09/21/2023] [Indexed: 12/08/2023] Open
Abstract
PURPOSE To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. METHODS A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established "no-new-Net" framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. RESULTS Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10-7, 3 × 10-4, 4 × 10-2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10-3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. CONCLUSION Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. RELEVANCE STATEMENT Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. KEY POINTS • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines.
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Affiliation(s)
- Thomas Buddenkotte
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany
- jung diagnostics GmbH, Hamburg, Germany
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, Italy
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Medicine, Danube Private University, Krems, Austria
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, Vienna, Austria
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Christian Etmann
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Subhadip Mukherjee
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
| | - Vlad Bura
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Hilal Sahin
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Roxana Pintican
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca-Napoca, Romania
- Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca-Napoca, Romania
| | - Marta Zerunian
- Department of Medical-Surgical and Translational Medicine-Radiology Unit, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy
| | - Iris Allajbeu
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
| | - Naveena Singh
- Department of Clinical Pathology, Barts Health NHS Trust, London, UK
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, UK
| | | | - David E Cohn
- Departmant of Obstetrics and Gynecology, Division of Gynecologic Oncology, Ohio State University Comprehensive Cancer Center, Ohio State University College of Medicine, Columbus, OH, USA
| | - Nicholas W Bateman
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
| | - Thomas P Conrads
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
- Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA
- Inova Center for Personalized Health, Inova Schar Cancer Institute, Falls Church, VA, USA
| | - Kathleen M Darcy
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
| | - G Larry Maxwell
- Department of Obstetrics and Gynecology, Gynecologic Cancer Center of Excellence, Walter Reed National Military Medical Center, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Uniformed Services University, Bethesda, MD, USA
- Department of Obstetrics and Gynecology, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - John B Freymann
- Cancer Imaging Informatics Lab, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ozan Öktem
- Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, University of Cambridge, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
- Dipartimento Di Scienze Radiologiche Ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy.
- Dipartimento Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Carola-Bibiane Schönlieb
- Department, of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK
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3
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Buddenkotte T, Escudero Sanchez L, Crispin-Ortuzar M, Woitek R, McCague C, Brenton JD, Öktem O, Sala E, Rundo L. Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. Comput Biol Med 2023; 163:107096. [PMID: 37302375 DOI: 10.1016/j.compbiomed.2023.107096] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 04/16/2023] [Accepted: 05/27/2023] [Indexed: 06/13/2023]
Abstract
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human-machine collaboration.
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Affiliation(s)
- Thomas Buddenkotte
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany.
| | - Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom; Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Medical Image Analysis & Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom; Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Ozan Öktem
- Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy; Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy
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4
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Escudero Sanchez L, Buddenkotte T, Al Sa’d M, McCague C, Darcy J, Rundo L, Samoshkin A, Graves MJ, Hollamby V, Browne P, Crispin-Ortuzar M, Woitek R, Sala E, Schönlieb CB, Doran SJ, Öktem O. Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case. Diagnostics (Basel) 2023; 13:2813. [PMID: 37685352 PMCID: PMC10486639 DOI: 10.3390/diagnostics13172813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/31/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.
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Affiliation(s)
- Lorena Escudero Sanchez
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
| | - Thomas Buddenkotte
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
- Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, 20246 Hamburg, Germany
- Jung Diagnostics GmbH, 22335 Hamburg, Germany
| | - Mohammad Al Sa’d
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - Cathal McCague
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - James Darcy
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW7 3RP, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
| | - Alex Samoshkin
- Office for Translational Research, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, UK
| | - Martin J. Graves
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Victoria Hollamby
- Research and Information Governance, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, UK
| | - Paul Browne
- High Performance Computing Department, University of Cambridge, Cambridge CB3 0RB, UK
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Department of Oncology, University of Cambridge, Cambridge CB2 0XZ, UK
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, 3500 Krems, Austria
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, 00168 Rome, Italy
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
| | - Carola-Bibiane Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK
| | - Simon J. Doran
- National Cancer Imaging Translational Accelerator (NCITA) Consortium, UK
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London SW7 3RP, UK
| | - Ozan Öktem
- Department of Mathematics, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
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5
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Rundo L, Beer L, Escudero Sanchez L, Crispin-Ortuzar M, Reinius M, McCague C, Sahin H, Bura V, Pintican R, Zerunian M, Ursprung S, Allajbeu I, Addley H, Martin-Gonzalez P, Buddenkotte T, Singh N, Sahdev A, Funingana IG, Jimenez-Linan M, Markowetz F, Brenton JD, Sala E, Woitek R. Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma. Front Oncol 2022; 12:868265. [PMID: 35785153 PMCID: PMC9243357 DOI: 10.3389/fonc.2022.868265] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/02/2022] [Indexed: 11/29/2022] Open
Abstract
Background Pathological response to neoadjuvant treatment for patients with high-grade serous ovarian carcinoma (HGSOC) is assessed using the chemotherapy response score (CRS) for omental tumor deposits. The main limitation of CRS is that it requires surgical sampling after initial neoadjuvant chemotherapy (NACT) treatment. Earlier and non-invasive response predictors could improve patient stratification. We developed computed tomography (CT) radiomic measures to predict neoadjuvant response before NACT using CRS as a gold standard. Methods Omental CT-based radiomics models, yielding a simplified fully interpretable radiomic signature, were developed using Elastic Net logistic regression and compared to predictions based on omental tumor volume alone. Models were developed on a single institution cohort of neoadjuvant-treated HGSOC (n = 61; 41% complete response to NCT) and tested on an external test cohort (n = 48; 21% complete response). Results The performance of the comprehensive radiomics models and the fully interpretable radiomics model was significantly higher than volume-based predictions of response in both the discovery and external test sets when assessed using G-mean (geometric mean of sensitivity and specificity) and NPV, indicating high generalizability and reliability in identifying non-responders when using radiomics. The performance of a fully interpretable model was similar to that of comprehensive radiomics models. Conclusions CT-based radiomics allows for predicting response to NACT in a timely manner and without the need for abdominal surgery. Adding pre-NACT radiomics to volumetry improved model performance for predictions of response to NACT in HGSOC and was robust to external testing. A radiomic signature based on five robust predictive features provides improved clinical interpretability and may thus facilitate clinical acceptance and application.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Lucian Beer
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lorena Escudero Sanchez
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Marika Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Cathal McCague
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
| | - Hilal Sahin
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology, Tepecik Training and Research Hospital, Izmir, Turkey
| | - Vlad Bura
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - Roxana Pintican
- Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
- Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Marta Zerunian
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome—Sant’Andrea University Hospital, Rome, Italy
| | | | - Iris Allajbeu
- Department of Radiology, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Helen Addley
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Buddenkotte
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Naveena Singh
- Department of Clinical Pathology, Barts Health NHS Trust, London, United Kingdom
| | - Anju Sahdev
- Department of Radiology, Barts Health NHS Trust, London, United Kingdom
| | - Ionut-Gabriel Funingana
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Mercedes Jimenez-Linan
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - James D. Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Evis Sala
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Ramona Woitek
- Department of Radiology, Cambridge, United Kingdom
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
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