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Background <p>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.</p> Methods <p>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.</p> Results <p>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 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.</p> Conclusions <p>The 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Tianle Hong,
  • Wenjie Huang,
  • Wenqing Lu,
  • Lu Peng,
  • Cunke Miao,
  • Lixuan Chen,
  • Yunjun Yang,
  • Yezhi Lin,
  • Liqin Wu

摘要

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 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.

Conclusions

The 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.