Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features
Abstract Background In 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...
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| Language: | English |
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BMC
2025-03-01
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| Series: | Breast Cancer Research |
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| Online Access: | https://doi.org/10.1186/s13058-025-01968-0 |
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| author | Xiaoyan Wu Yiman Li Jilong Chen Jie Chen Wenchuan Zhang Xunxi Lu Xiaorong Zhong Min Zhu Yuhao Yi Hong Bu |
| author_facet | Xiaoyan Wu Yiman Li Jilong Chen Jie Chen Wenchuan Zhang Xunxi Lu Xiaorong Zhong Min Zhu Yuhao Yi Hong Bu |
| author_sort | Xiaoyan Wu |
| collection | DOAJ |
| description | Abstract Background In 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. Methods We 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. Results Both 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. Conclusion Our 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. |
| format | Article |
| id | doaj-art-57d49bf45cbb49ecb826b6f6be5d1c17 |
| institution | DOAJ |
| issn | 1465-542X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj-art-57d49bf45cbb49ecb826b6f6be5d1c172025-08-20T02:49:02ZengBMCBreast Cancer Research1465-542X2025-03-0127111310.1186/s13058-025-01968-0Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological featuresXiaoyan Wu0Yiman Li1Jilong Chen2Jie Chen3Wenchuan Zhang4Xunxi Lu5Xiaorong Zhong6Min Zhu7Yuhao Yi8Hong Bu9Department of Pathology, West China Hospital, Sichuan UniversityCollege of Computer Science, Sichuan UniversityCollege of Computer Science, Sichuan UniversityInstitute of Clinical Pathology, West China Hospital, Sichuan UniversityDepartment of Pathology, West China Hospital, Sichuan UniversityDepartment of Pathology, West China Hospital, Sichuan UniversityInstitute for Breast Health Medicine, Cancer Center, Breast Center, West China Hospital, Sichuan UniversityCollege of Computer Science, Sichuan UniversityInstitute of Clinical Pathology, West China Hospital, Sichuan UniversityDepartment of Pathology, West China Hospital, Sichuan UniversityAbstract Background In 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. Methods We 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. Results Both 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. Conclusion Our 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.https://doi.org/10.1186/s13058-025-01968-0HR+/HER2- early breast cancerRecurrence riskAdjuvant chemo-endocrine therapyDeep learning pipelinesPathology image |
| spellingShingle | Xiaoyan Wu Yiman Li Jilong Chen Jie Chen Wenchuan Zhang Xunxi Lu Xiaorong Zhong Min Zhu Yuhao Yi Hong Bu Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features Breast Cancer Research HR+/HER2- early breast cancer Recurrence risk Adjuvant chemo-endocrine therapy Deep learning pipelines Pathology image |
| title | Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features |
| title_full | Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features |
| title_fullStr | Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features |
| title_full_unstemmed | Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features |
| title_short | Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features |
| title_sort | multimodal recurrence risk prediction model for hr her2 early breast cancer following adjuvant chemo endocrine therapy integrating pathology image and clinicalpathological features |
| topic | HR+/HER2- early breast cancer Recurrence risk Adjuvant chemo-endocrine therapy Deep learning pipelines Pathology image |
| url | https://doi.org/10.1186/s13058-025-01968-0 |
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