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Multimodal Deep Learning With Routine Clinical Data For Recurrence Risk Stratification In Hr+/her2- Early Breast Cancer.

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Multimodal Deep Learning With Routine Clinical Data For Recurrence Risk Stratification In Hr+/her2- Early Breast Cancer.

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Multimodal Deep Learning with Routine Clinical Data for Recurrence Risk Stratification in HR⁺/HER2^- Early Breast

Xiaoyan Wu^1,2, Hong Liu³, Jingyan Liu⁴

¹Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.

Research (Washington, D.C.)

|April 1, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

A new AI model integrates pathology images, ultrasound data, and clinical information to predict recurrence risk in early breast cancer patients. This multimodal approach offers improved accuracy for personalized treatment and surveillance strategies.

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer (EBC) patients have a persistent risk of recurrence.
Existing prognostic tools lack sufficient accuracy and accessibility for precise risk stratification.

Purpose of the Study:

To develop and validate a multimodal recurrence risk prediction (MRRP) model for HR+/HER2- EBC patients.
To integrate routinely available clinical data, including whole-slide images (WSIs), ultrasound (US) imaging, and diagnostic reports.

Main Methods:

Developed a hierarchical transformer-based fusion framework with cross-attention mechanisms to integrate diverse data modalities.
Utilized a cohort of 768 HR+/HER2- EBC patients with long-term follow-up for model training and validation.

Implemented a learnable compensation mechanism to handle missing modality data.

Main Results:

The MRRP model achieved superior prognostic performance (C-index = 0.840) compared to single-modality models.
Demonstrated robust time-dependent AUCs exceeding 0.85 at 3, 5, and 7 years post-treatment.
Pathology features were crucial, with complementary value from US and clinical data.

Conclusions:

The MRRP model provides a clinically practical, AI-driven tool for precise risk stratification in HR+/HER2- EBC.
Enables individualized treatment and surveillance decisions, reducing reliance on expensive multi-omics data.