Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer
Open accessAbstract
Background: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. Methods: In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. Results: The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). Conclusion: The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.
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WSI-based deep learning can improve efficiency in predicting prognosis and treatment response.
“WSI-based deep learning can improve efficiency in predicting prognosis and treatment response fields (14–16).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [15]
The Background (page unavailable) reports positive results for deep learning with histological images in prognosis prediction, and the Discussion (page unavailable) reports that the authors’ WSI-based model predicted treatment response to chemotherapy. The paper describes an automated workflow but does not establish that it improves the efficiency of either prediction task. (LLM, conf=0.83)
Experimental — accuracy not yet measuredHistopathology whole slide image provides information that reflects molecular characteristics or genetic patterns.
“Emerging evidence has shown that histopathology whole slide image (WSI) provides information that reflects molecular characteristics or genetic patterns.”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [15]
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 [15]
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- Authors
- Li, F., Yang, Y., Wei, Y., He, P., Chen, J., Zheng, Z., & Bu, H.
- Affiliations
- Sichuan University (CN)
- Corresponding author
- Zheng, Zhongxi
- Bu, Hong
- Venue
- Journal of Translational Medicine
- Published
- 2021-08-16
- Volume
- 19
- Issue
- 1
- Pages
- 348-348
- ISSN
- 1479-5876 (ISSN-L)
- PMC
- PMC8365907
- OpenAlex
- W3211240094
- Cited by
- 113
- References
- 49
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 34 · 2023: 20 · 2022: 11 · 2021: 1
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Translational Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- 1·3·5 project for disciplines of excellence (ZYGD18012)
- the Technological Innovation Project of Chengdu New Industrial Technology Research Institute (2017-CY02–00026-GX)
- the Sichuan Science and Technology Program (2020YFS0088)
- the 1·3·5 project for disciplines of excellence Clinical Research Incubation Project (2019HXFH036)
- 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, predictive biomarkers, deep learning, stromal tumor-infiltrating lymphocytes, tumor subtypes, treatment response prediction, patient stratification
- MeSH terms
- Deep Learning, Antineoplastic Combined Chemotherapy Protocols, Breast Neoplasms, Female, Humans, Biomarkers, Lymphocytes, Tumor-Infiltrating, Treatment Outcome, Neoadjuvant Therapy
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