Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy.

Although neoadjuvant chemotherapy (NACT) is commonly used for advanced ovarian cancer, patient outcomes vary substantially. We developed a graph convolutional network (GCN) that integrates patient-specific baseline clinical variables and computed tomography-derived radiomic features while modeling inter-patient relationships to improve outcome prediction beyond standard models. The GCN operates without reliance on high-performance computing resources and predicts long-term overall survival (OS) while stratifying short-term surgical outcomes (R0 resection). The GCN was compared with the CA-125 ELIMination rate constant K (KELIM) score and three Cox-based comparator models. Model performance was evaluated using the concordance index (C-index) for OS, area under the receiver operating characteristic curve for 3-year OS, Kaplan-Meier survival analysis, and R0 resection stratification. The GCN demonstrated strong OS prognosis performance (C-index = 0.73, 0.72, and 0.70 across the training and two external test datasets), stratified surgical outcomes, and identified 16.30% of patients with low KELIM scores but favorable survival.