A dual-center study: multimodal fusion-based deep learning approach for pathological subtype prediction of type I and type II ovarian cancer

作者：Tianle Hong, Weicheng Huang, Wenqing Lu, Lu Peng, Cunke Miao, Lixuan Chen, Yunjun Yang, Yezhi Lin, Liqin Wu · 发表于：BMC Medical Imaging · 年份：2026 · DOI：10.1186/s12880-026-02231-4 · 被引用次数：1 · 研究领域：Ovarian cancer diagnosis and treatment、AI in cancer detection、Endometrial and Cervical Cancer Treatments

BACKGROUND: Accurate prediction of WHO Type I and Type II pathological classifications in epithelial ovarian cancer (EOC) patients is critical for developing effective personalized treatment strategies. This study aims to develop a multimodal deep learning framework integrating enhanced CT (EC), non-contrast CT (NC), transvaginal ultrasound (US), and clinical data to predict EOC subtypes. Furthermore, we investigate the differential contributions of distinct modalities to model predictions through interpretable analysis. METHODS: A retrospective analysis was conducted on 240 EOC patients who underwent postoperative histopathological subtyping alongside EC, NC and US. A total of 17 EOC patients from other centers were enrolled for external validation. The multimodal imaging data fusion deep learning model USECNC, integrating ResNet-50 with Cross-attention mechanisms, was designed to synergistically fuse multimodal image features. The integrated model USECNC + CL (USECNC+Clinical Data) combines clinical data and imaging features at the decision layer for final prediction. RESULTS: The USECNC + CL model demonstrated the best performance in predicting EOC pathological subtypes, achieving an AUC of 0.87 (5-fold cross-validation range: [0.82–0.92]). Baseline models based on EC, NC, US, and clinical data achieved AUC of 0.81, 0.69, 0.77 and 0.79. The optimal radiomics model achieved an AUC of 0.72. The USECNC + CL model also achieved good predictive performance in the external valid...