ArticleBMC medical imaging2026
A dual-center study: multimodal fusion-based deep learning approach for pathological subtype prediction of type I and type II ovarian cancer.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
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Abstract
backgroundAccurate 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.
methodsA 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.
resultsThe 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 validation set (AUC = 0.850, ACC = 0.824). In the SHAP analysis, the contribution of imaging features is ranked from highest to lowest as follows: EC, US, NC. Meanwhile, compared to imaging fusion features, CA125 demonstrates a superior contribution.
conclusionsThe integration of preoperative multimodal data into a deep learning model enables accurate prediction of pathological subtypes in EOC patients. Interpretability analyses confirmed the critical diagnostic contributions of EC vascular features and CA-125 levels. Accurate preoperative type I/II prediction can effectively assist in the formulation of chemotherapy regimens and surgical plans for patients.
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