Longitudinal deep learning models for tracking disease progression in ovarian cancer using PET/CT imaging and...

Ovarian cancer is often diagnosed at advanced stages, with high-grade serous ovarian cancer (HGSOC) accounting for 70-80% of fatalities. Current predictive tools, limited by single-time-point data, fail to capture subtle temporal changes indicative of relapse. To evaluate the performance of OvarXNet, a novel deep learning framework integrating longitudinal PET/CT imaging and clinical data for early prediction of ovarian cancer relapse. This retrospective study included 58 advanced-stage HGSOC patients (mean age, 56 ± 10.4 years) who underwent [¹⁸F]FDG PET/CT scans from April 2019 to January 2025. Patients with uncontrolled diabetes or recent cancers were excluded. Each patient had a median of three PET/CT scans and associated clinical data. The OvarXNet framework combines 3D convolutional neural networks (CNNs) for volumetric feature extraction and bidirectional gated recurrent units for temporal analysis. Statistical analyses included area under the receiver operating characteristic curve (AUC), precision-recall (PR) metrics, and calibration plots. Fifty-eight patients (mean age 56 ± 10.4 years) contributed 1914 image sets post-augmentation. OvarXNet achieved an AUC of 0.92, outperforming single-time-point CNN (AUC: 0.84) and LSTM-based models (AUC: 0.89). PR analysis confirmed superior model performance (PR-AUC: OvarXNet > 0.90 vs. single-time-point CNN: 0.82). Calibration plots demonstrated robust probability estimates. Attention mechanisms highlighted time points with elevated CA-125 or progression-related clinical notes, enhancing interpretability. OvarXNet significantly improves early relapse prediction in advanced-stage HGSOC by leveraging longitudinal imaging and clinical data. The framework's accuracy and interpretability support its potential for guiding personalized treatment strategies.