ArticleFrontiers in medicine2026
A prediction model for platinum-resistant recurrence of ovarian cancer was established using multimodal artificial intelligence machine learning methods.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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, 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 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.
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