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Updated: Feb 28, 2026

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MMHC-OCPR: Prediction of Platinum Response and Recurrence Risk in Ovarian Cancer with Multimodal Deep Learning.

Enyu Tang1, Haoming Xia2, Zhenlong Yuan1

  • 1Department of Gynecology Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.

Biomedicines
|February 27, 2026
PubMed
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A new multimodal model (MMHC-OCPR) accurately predicts ovarian cancer platinum response and recurrence risk. This tool aids in early detection of platinum resistance and personalizes treatment, improving patient outcomes and quality of life.

Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Ovarian cancer is a leading cause of gynecological cancer mortality.
  • Platinum resistance significantly worsens prognosis in ovarian cancer patients.
  • Personalized treatment strategies are crucial for improving patient outcomes.

Purpose of the Study:

  • To develop a multimodal model (MMHC-OCPR) for predicting platinum response in ovarian cancer.
  • To create a model for stratifying ovarian cancer patients based on recurrence risk.
  • To enable earlier detection of platinum resistance and guide personalized treatment decisions.

Main Methods:

  • A multicenter retrospective study involving 431 ovarian cancer patients and 1182 whole slide images (WSIs).
  • Utilized a weakly supervised multiple instance learning framework integrating histopathology (WSIs) with clinicopathological data.
Keywords:
MMHC-OCPRUNI2-hclustering-constrained attention multiple instance learningovarian cancerplatinum responserecurrence risk

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  • Incorporated the transformer-based pretrained encoder UNI2-h to enhance predictive performance.

    • Main Results:

    • The platinum response classifier achieved an AUC of 0.896 (internal) and 0.876 (external).
    • Multimodal integration (metastatic WSIs and clinical data) improved AUC to 0.914.
    • Recurrence risk model showed a C-index of 0.801, increasing to 0.838 with multimodal enhancement, stratifying patients into distinct risk groups.

    Conclusions:

    • The MMHC-OCPR model facilitates early identification of platinum resistance, enabling timely treatment escalation.
    • Recurrence risk stratification supports personalized management, potentially sparing low-risk patients from unnecessary maintenance therapy.
    • The model aims to reduce treatment toxicity and cost while enhancing patients' quality of life.