DPU - Symposium 2022

Medica l Image Ana lysis & Ar t i f icia l Intel l igence Symposium 2022 Nr. 13: Prof. DDr. Ramona Woitek: Radiomics-based predictors of response to neoadjuvant chemotherapy in patients with high grade serous ovarian cancer Curriculum Vitae: Ramona Woitek completed her medical studies at the Medical University of Vienna in 2008, followed by a PhD in Clinical Neuroscience and a radiology residency at the Department of Biomedical Imaging and Image-guided Therapy at the Medical University of Vienna. Between 2017 and 2022, Univ.-Prof. Ramona Woitek worked scientifically and clinically at the Department of Radiology at the University of Cambridge and Addenbrookes Hospital in Cambridge, UK, where she researched hyperpolarised carbon-13 MRI and radiomics and multiomics in breast and ovarian cancer. Abstract: Radiomics-based predictors of response to neoadjuvant chemotherapy in patients with high grade serous ovarian cancer 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. Omental CT-based radiomics models 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). 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 generalisability and reliability in identifying non-responders when using radiomics. 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 facilitate clinical acceptance and application.