Deep learning-based tumor resectability prediction model in patients with Ovarian Cancer: a preliminary evaluation

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide, characterized by aggressive behavior, high relapse rate, and rapid progression. The cornerstone of OC treatment is cytoreductive surgery, targeting the removal of all detectable tumor lesions wherever feasible. In instances of widespread disease or significant perioperative morbidity risk, patients may initially receive neoadjuvant chemotherapy aimed at reducing the tumor’s volume prior to surgical intervention. The pivotal decision between surgery and chemotherapy poses a significant therapeutic challenge in OC management. Our contribution is to develop an artificial intelligence-based model to support this critical decision by predicting Tumor Resectability (TR) from preoperative Computed Tomography (CT) images at the time of diagnosis. Our study aims to develop a 3D Convolutional Neural Network capable of predicting TR in a cohort of 650 with advanced stage epithelial patients with OC who underwent surgery at the European Institute of Oncology (IEO, Milan, Italy). The model processes preoperative CT scans of the Thorax, Abdomen, and Pelvis to deliver a binary prediction: TR=0 indicates a tumor completely resected, while TR=1 indicates the presence of residual tumor after cytoreductive surgery. ... mehrWe design and train our model from the ground up, achieving as preliminary results an accuracy of 65%. As far as we are aware, this is the first attempt to leverage deep learning for assessing TR in OC patients based on preoperative CT scans. Our model represents a non-invasive and preoperative tool with the potential to facilitate clinical decision making in the era of individualized and precision medicine.