A prediction model for platinum-resistant recurrence of ovarian cancer was established using multimodal artificial intelligence machine learning methods

Abstract

Aim To develop and validate a multimodal artificial intelligence (AI)-based prediction model for platinum-resistant recurrence in ovarian cancer by integrating clinical data, medical imaging, and medical knowledge resources, with the goal of improving early risk stratification and supporting individualized treatment decisions. This exploratory proof-of-concept study aims to assess the feasibility of multimodal fusion for this task; no external validation has been performed. Methods This study collected multimodal data from ovarian cancer patients, including clinical records from 214 patients treated at Renji Hospital affiliated to Shanghai Jiao Tong University School of Medicine between June 2020 and January 2025, imaging data from 218 patients comprising 5,053 CT and MRI images, and 1,000 high-quality medical literature sources published between 2023 and 2025. Patients were classified into a platinum-resistant recurrence group (PROC, n = 87, 40.7%) and a non-platinum-resistant recurrence group (NPROC, n = 127, 59.3%) according to whether recurrence occurred within 6 months after the last platinum-based chemotherapy. The platinum-free interval (PFI) was used only for outcome definition, not as a predictor. A multimodal prediction framework based on a Mixture of Experts (MoE) architecture was constructed, incorporating a clinical expert model, an imaging expert model, and a medical knowledge expert model. Model performance was evaluated using accuracy, recall, F1 score, and the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (bootstrap with 1,000 iterations, stratified by cross-validation folds), calibration (Brier score, Hosmer-Lemeshow test, calibration curve), and decision curve analysis (DCA). Results The proposed multimodal model demonstrated excellent predictive performance for platinum-resistant recurrence, achieving an accuracy of 0.98 (95% CI: 0.96–1.00), a recall of 0.95 (95% CI: 0.92–0.98), an F1 score of 0.98 (95% CI: 0.97–0.99), and an AUC of 0.96 (95% CI: 0.95–0.97). The model showed good calibration with a Brier score of 0.042 (95% CI: 0.031–0.058) and a Hosmer-Lemeshow test p-value of 0.31 (χ2 = 11.8, df = 10), indicating no statistically significant lack of fit. These results were superior to those of single-expert and conventional benchmark models. In the clinical expert evaluation, the model achieved an accuracy of 0.83, a recall of 0.81, an F1 score of 0.82, and an AUC of 0.83, showing competitive performance compared with random forest, support vector machine, gradient boosting machine, and Transformer-based models. Conclusion This exploratory study demonstrates that multimodal AI integrating clinical, imaging, and knowledge graph data can achieve strong internal predictive performance for platinum-resistant recurrence of ovarian cancer in a single-center retrospective cohort. However, the model is preliminary, has not been externally validated, and is not ready for clinical use. Independent multicenter validation is required before any clinical translation can be considered. This article should be viewed as a hypothesis-generating tool and a methodological proof-of-concept only.