Preliminary deep learning model to predict residual tumor in advanced epithelial ovarian cancer.

Abstract

e13701 Background: The optimal therapeutic strategy for advanced epithelial ovarian cancer (EOC) — primary debulking surgery (PDS) followed by adjuvant chemotherapy vs. neoadjuvant chemotherapy (NACT) followed by interval debulking surgery — remains debated. NACT is often chosen for unresectable disease, typically identified only during surgery. This study aims to develop a deep learning (DL) model to predict tumor resectability from pre-operative computed tomography (CT) scans. Methods: We retrospectively included EOC patients, FIGO stage III-IV, who underwent PDS between 01/ 2016 and 12/2023 at the European Institute of Oncology, Milan, Italy. The dataset included anonymized portal venous phase contrast-enhanced CT scans. Residual tumor (RT) annotations were extracted from operative reports and categorized as optimal RT (< 1cm) and suboptimal RT (≥1cm). A DL segmentation model (based on open-source TotalSegmentator) was used to identify pelvic/abdominal organ volumes and to remove couch artifacts. The dataset was split into training/validation (80%) and testing (20%) sets. A pre-trained Vision Transformer (ViT) encoder was adapted for 3D imaging, converting CT volumes into features refined by an attention mechanism and classified as optimal RT or suboptimal RT . We trained two ViT configurations (vit-base-patch16-224 and dinov2-small-imagenet1k-1-layer) and compared them with DenseNet121, a widely used Convolutional Neural Network (CNN) for image classification. Performance metrics included accuracy and F1 score. Results: Out of 521 patients, 416 (80%) had optimal RT and 105 (20%) suboptimal RT , the proportion being respected througouth the training/validation and testing groups. The median age at diagnosis was 60.0 years (IQR, 53.0-67.0). ViT models showed better performance compared to CNN in terms of accuracy (0.80 and 0.77 vs. 0.62) and F1 score (0.43 and 0.33 vs. 0.12). Conclusions: A pioneering ViT-based model was capable to predict tumor resectability from pre-operative CT scans in patients with advanced EOC. This improvement in performance, compared to CNN, might be due to the ViTs' ability to capture long-range dependencies and global context, which are crucial for predicting tumor resectability. In contrast, CNNs are limited by localized feature extraction and inductive bias, hindering their ability to capture the complex spatial relationships. By addressing the limitations of current radiological assessments, the model has the potential to serve as a clinical decision-support tool, enabling personalized management strategies for patients. Future work will focus on further improving the model performance.