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

Published version
Peer-reviewed

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Authors

Rundo, Leonardo 
Beer, Lucian 
Escudero Sanchez, Lorena 
Crispin-Ortuzar, Mireia 
Reinius, Marika 

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.

Description

Keywords

chemotherapy response score, computed tomography, neoadjuvant chemotherapy, ovarian cancer, radiomics

Journal Title

Front Oncol

Conference Name

Journal ISSN

2234-943X
2234-943X

Volume Title

Publisher

Frontiers Media SA
Sponsorship
Engineering and Physical Sciences Research Council (EP/P020259/1)
Cancer Research UK (C96/A25177)
National Institute for Health and Care Research (IS-BRC-1215-20014)
Wellcome Trust (215733/Z/19/Z)
Cancer Research UK (22905)