ArticleBreast cancer research : BCR2025
Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features.
Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cutting-edge advances in endocrine therapy for breast cancer (Review).Oncology letters · 2026Review
- Multimodal Deep Learning with Routine Clinical Data for Recurrence Risk Stratification in HRResearch (Washington, D.C.) · 2026Article
- Resilient Sinkhorn-Based Optimal Transport Late Fusion Framework for Breast Cancer Diagnosis.Cancer informatics · 2026Article
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- A macro-micro-macro radiogenomic framework identifies FIBCD1 as a key immune-modulating biomarker in breast cancer.Journal of translational medicine · 2025Article
- Multimodal deep learning model for prediction of breast cancer recurrence risk and correlation with oncotype DX.Breast cancer research : BCR · 2025Article
- Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records.Scientific reports · 2025Article
- Artificial Intelligence-Based Pathology to Assist Prediction of Neoadjuvant Therapy Responses for Breast Cancer.Cancer medicine · 2025Review
- Immunohistochemical predictors of local recurrence in breast carcinoma: development and sensitivity validation of an IHC-based risk score.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
- Multimodal machine learning for predicting postoperative functional outcomes in surgically treated supratentorial deep intracerebral hemorrhage: a prospective multicenter study.Frontiers in neurologyArticle
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Abstract
backgroundIn HR+/HER2- early breast cancer (EBC) patients, approximately one-third of stage II and 50% of stage III patients experience recurrence, with poor outcomes after recurrence. Given that these patients commonly undergo adjuvant chemo-endocrine therapy (C-ET), accurately predicting the recurrence risk is crucial for optimizing treatment strategies and improving patient outcomes.
methodsWe collected postoperative histopathological slides from 1095 HR+/HER2- EBC who received C-ET and were followed for more than five years at West China Hospital, Sichuan University. Two deep learning pipelines were developed and validated: ACMIL-based and CLAM-based. Both pipelines, designed to predict recurrence risk post-treatment, were based on pretrained feature encoders and multi-instance learning with attention mechanisms. Model performance was evaluated using a five-fold cross-validation approach and externally validated on HR+/HER2- EBC patients from the TCGA cohort.
resultsBoth ACMIL-based and CLAM-based pipelines performed well in predicting recurrence risk, with UNI-ACMIL demonstrating superior performance across multiple metrics. The average area under the curve (AUC) for the UNI-ACMIL pipeline in the five-fold cross-validation test set was 0.86 ± 0.02, and 0.80 ± 0.04 in the TCGA cohort. In the five-fold cross-validation test sets, effectively stratified patients into high-risk and low-risk groups, demonstrating significant prognostic differences. Hazard ratios for recurrence-free survival (RFS) ranged from 5.32 (95% CI 1.86-15.12) to 15.16 (95% CI 3.61-63.56). Moreover, among six different multimodal recurrence risk models, the WSI-based risk score was identified as the most significant contributor.
conclusionOur multimodal recurrence risk prediction model is a practical and reliable tool that enhances the predictive power of existing systems relying solely on clinicopathological parameters. It offers improved recurrence risk prediction for HR+/HER2- EBC patients following adjuvant C-ET, supporting personalized treatment and better patient outcomes.
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