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DataSheet_1_Clinically Interpretable Radiomics-Based Prediction of Histopathologic Response to Neoadjuvant Chemotherapy in High-Grade Serous Ovarian Carcinoma.pdf

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posted on 2022-06-16, 04:25 authored by Leonardo Rundo, Lucian Beer, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Marika Reinius, Cathal McCague, Hilal Sahin, Vlad Bura, Roxana Pintican, Marta Zerunian, Stephan Ursprung, Iris Allajbeu, Helen Addley, Paula Martin-Gonzalez, Thomas Buddenkotte, Naveena Singh, Anju Sahdev, Ionut-Gabriel Funingana, Mercedes Jimenez-Linan, Florian Markowetz, James D. Brenton, Evis Sala, Ramona Woitek
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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