Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study
Open accessAbstract
INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. MATERIALS AND METHODS: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. RESULTS: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). CONCLUSION: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.
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WSI-based deep learning biomarkers show potential in pCR prediction for breast cancer.
“WSI-based deep learning biomarkers show potential in pCR prediction for breast cancer (15, 16, 26).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [26]
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Mao, N., Dai, Y., Zhou, H., Lin, F., Zheng, T., Li, Z., Yang, P., Zhao, F., Li, Q., Wang, W., et al. (2025) · Science Advances · doi:10.1126/sciadv.adr1576 · Cited by 51 · see citing works
“WSI-based deep learning biomarkers show potential in pCR prediction for breast cancer ( 15 , 16 , 26 ).”
- Authors
- Li, B., Li, F., Liu, Z., Xu, F., Ye, G., Li, W., Zhang, Y., Zhu, T., Shao, L., Chen, C., Sun, C., Qiu, B., Bu, H., Wang, K., & Tian, J.
- Affiliations
- University of Science and Technology of China (CN)
- Sichuan University (CN)
- Chinese Academy of Sciences (CN)
- Guangdong Academy of Medical Sciences (CN)
- First People's Hospital of Foshan (CN)
- Shantou Central Hospital (CN)
- Corresponding author
- Bu, Hong
- Wang, Kun
- Tian, Jie
- ORCID
- Venue
- The Breast
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 66
- Pages
- 183-190
- ISSN
- 0960-9776 (ISSN-L)
- PMC
- PMC9619175
- OpenAlex
- W4306825447
- Cited by
- 48
- References
- 37
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 14 · 2023: 5
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Breast (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer, neoadjuvant chemotherapy, pathological complete response, whole slide images, tumor-infiltrating lymphocytes, triple-negative breast cancer, treatment response prediction, histopathology
- MeSH terms
- Deep Learning, Antineoplastic Combined Chemotherapy Protocols, Biopsy, Breast Neoplasms, Female, Humans, Retrospective Studies, Lymphocytes, Tumor-Infiltrating, Neoadjuvant Therapy, Triple Negative Breast Neoplasms
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