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Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.

Chaoyang Yan1, Linwei Li2,3,4, Xiaolong Qian3,4,5

  • 1Centre for Bioinformatics and Intelligent Medicine, College of Computer Science, Nankai University, Tianjin, 300350, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|November 8, 2025
PubMed
Summary
This summary is machine-generated.

A new AI model, CPMP, predicts breast cancer recurrence risk from pathology slides, complementing genomic tests. This approach offers insights into tumor morphology and spatial patterns, improving prognostic accuracy.

Keywords:
breast cancercomputational pathologyrecurrence risk assessmenttumor spatial morphologyweakly supervised learning

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Area of Science:

  • Computational pathology
  • Artificial intelligence in oncology
  • Genomic medicine

Background:

  • Recurrence in breast cancer (BC) significantly impacts mortality.
  • The MammaPrint (MP) genomic assay assesses recurrence risk and chemotherapy benefit for early-stage HR+/HER2- BC.
  • MP has limitations including high cost and inability to analyze tumor morphology.

Purpose of the Study:

  • To develop a weakly supervised agent-attention transformer model (CPMP) for predicting MP recurrence risk using histopathological slides.
  • To explore spatial and morphological patterns associated with MP risk groups.
  • To evaluate the prognostic capability of CPMP in an external cohort.

Main Methods:

  • Establishment of a breast cancer MammaPrint cohort.
  • Development of CPMP, a weakly supervised agent-attention transformer model.
  • Prediction of MP risk groups from annotation-free histopathological slides.
  • Spatial and morphological analyses using CPMP.
  • Prognostic evaluation in an external cohort.

Main Results:

  • CPMP achieved an AUROC of 0.824 ± 0.03 in predicting MP risk groups.
  • The model revealed tumor spatial localization and distinct intercellular interaction patterns for different MP risk groups.
  • CPMP characterized tumor morphology diversity, identifying unique phenotypes associated with MP risk.
  • Prognostic evaluation showed significant stratification of distant metastasis risk (HR: 3.14, p-value = 0.0014).

Conclusions:

  • CPMP effectively predicts MammaPrint recurrence risk from histopathological slides.
  • The model provides novel insights into spatial and morphological tumor characteristics related to recurrence risk.
  • CPMP demonstrates significant prognostic value, offering a cost-effective supplement to genomic risk assessment in early-stage breast cancer.