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      Is Open Access

      Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma

      research-article
      1 , 2 , 1 , 2 , 3 , 1 , 2 , 2 , 4 , 5 , 2 , 4 , 6 , 1 , 2 , 1 , 2 , 7 , 1 , 2 , 8 , 8 , 9 , 10 , 1 , 1 , 6 , 6 , 2 , 4 , 11 , 12 , 13 , 2 , 4 , 6 , 2 , 6 , 2 , 4 , 2 , 4 , 6 , 1 , 2 , 6 , 1 , 2 , 3 ,
      Frontiers in Oncology
      Frontiers Media S.A.
      ovarian cancer, radiomics, computed tomography, chemotherapy response score, neoadjuvant chemotherapy

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          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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          Most cited references50

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          New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).

          Assessment of the change in tumour burden is an important feature of the clinical evaluation of cancer therapeutics: both tumour shrinkage (objective response) and disease progression are useful endpoints in clinical trials. Since RECIST was published in 2000, many investigators, cooperative groups, industry and government authorities have adopted these criteria in the assessment of treatment outcomes. However, a number of questions and issues have arisen which have led to the development of a revised RECIST guideline (version 1.1). Evidence for changes, summarised in separate papers in this special issue, has come from assessment of a large data warehouse (>6500 patients), simulation studies and literature reviews. HIGHLIGHTS OF REVISED RECIST 1.1: Major changes include: Number of lesions to be assessed: based on evidence from numerous trial databases merged into a data warehouse for analysis purposes, the number of lesions required to assess tumour burden for response determination has been reduced from a maximum of 10 to a maximum of five total (and from five to two per organ, maximum). Assessment of pathological lymph nodes is now incorporated: nodes with a short axis of 15 mm are considered measurable and assessable as target lesions. The short axis measurement should be included in the sum of lesions in calculation of tumour response. Nodes that shrink to <10mm short axis are considered normal. Confirmation of response is required for trials with response primary endpoint but is no longer required in randomised studies since the control arm serves as appropriate means of interpretation of data. Disease progression is clarified in several aspects: in addition to the previous definition of progression in target disease of 20% increase in sum, a 5mm absolute increase is now required as well to guard against over calling PD when the total sum is very small. Furthermore, there is guidance offered on what constitutes 'unequivocal progression' of non-measurable/non-target disease, a source of confusion in the original RECIST guideline. Finally, a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included. Imaging guidance: the revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions. A key question considered by the RECIST Working Group in developing RECIST 1.1 was whether it was appropriate to move from anatomic unidimensional assessment of tumour burden to either volumetric anatomical assessment or to functional assessment with PET or MRI. It was concluded that, at present, there is not sufficient standardisation or evidence to abandon anatomical assessment of tumour burden. The only exception to this is in the use of FDG-PET imaging as an adjunct to determination of progression. As is detailed in the final paper in this special issue, the use of these promising newer approaches requires appropriate clinical validation studies.
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            Radiomics: Images Are More than Pictures, They Are Data

            This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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              Regularization and variable selection via the elastic net

                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                16 June 2022
                2022
                : 12
                : 868265
                Affiliations
                [1] 1 Department of Radiology , Cambridge, United Kingdom
                [2] 2 Cancer Research UK Cambridge Centre, University of Cambridge , Cambridge, United Kingdom
                [3] 3 Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna , Vienna, Austria
                [4] 4 Cancer Research UK Cambridge Institute, University of Cambridge , Cambridge, United Kingdom
                [5] 5 Department of Oncology, University of Cambridge , Cambridge, United Kingdom
                [6] 6 Cambridge University Hospitals NHS Foundation Trust , Cambridge, United Kingdom
                [7] 7 Department of Radiology, Tepecik Training and Research Hospital , Izmir, Turkey
                [8] 8 Department of Radiology and Medical Imaging, County Clinical Emergency Hospital , Cluj-Napoca, Romania
                [9] 9 Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy , Cluj-Napoca, Romania
                [10] 10 Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome—Sant’Andrea University Hospital , Rome, Italy
                [11] 11 Department of Applied Mathematics and Theoretical Physics, University of Cambridge , Cambridge, United Kingdom
                [12] 12 Department of Clinical Pathology, Barts Health NHS Trust , London, United Kingdom
                [13] 13 Department of Radiology, Barts Health NHS Trust , London, United Kingdom
                Author notes

                Edited by: Rathan Subramaniam, University of Otago, New Zealand

                Reviewed by: Annamaria Ferrero, Mauriziano Hospital, Italy; Giulia Dondi, University of Bologna, Italy

                *Correspondence: Ramona Woitek, rw585@ 123456cam.ac.uk

                †These authors share senior authorship

                This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2022.868265
                9243357
                35785153
                81d334d7-783b-4a9d-b113-1d83f0c7cca6
                Copyright © 2022 Rundo, Beer, Escudero Sanchez, Crispin-Ortuzar, Reinius, McCague, Sahin, Bura, Pintican, Zerunian, Ursprung, Allajbeu, Addley, Martin-Gonzalez, Buddenkotte, Singh, Sahdev, Funingana, Jimenez-Linan, Markowetz, Brenton, Sala and Woitek

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 February 2022
                : 02 May 2022
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 50, Pages: 12, Words: 5839
                Funding
                Funded by: Mark Foundation For Cancer Research , doi 10.13039/100014599;
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Funded by: NIHR Cambridge Biomedical Research Centre , doi 10.13039/501100018956;
                Funded by: Austrian Science Fund , doi 10.13039/501100002428;
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                ovarian cancer,radiomics,computed tomography,chemotherapy response score,neoadjuvant chemotherapy

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