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Integration of histopathological characteristics by machine learning improves the prediction of neoadjuvant immunochemotherapy response in triple-negative breast cancer
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DOI：10.1002/path.70022.png)
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Abstract
En 中文
Neoadjuvant immunochemotherapy (NAIC) is a standard treatment for triple-negative breast cancer (TNBC), but there is no reliable biomarker to identify potential responders and optimize patient care. In this study, we developed a model named Immunotherapy Prediction based on Pathological Images (IPPI) by machine learning. The IPPI model performed well in the discovery cohort and two validation cohorts, which included a total of 209 patients, and its predictive power was significantly improved compared to clinical factors and the combined positive score for programmed death-ligand 1. TNBC patients predicted to achieve a pathological complete response had a better prognosis than those predicted to have residual disease. Moreover, we elucidated the relationship between histopathological features and biological characteristics, thereby improving the interpretability of the IPPI model. This study proposes a novel and efficient model to facilitate the prediction of NAIC response in TNBC patients, highlights key histopathological features associated with treatment response, and presents new evidence for precision immuno-oncology through the integration of machine learning and digital pathology.
Keywords:
Triple-negative breast cancer
Immunotherapy
Pathologic complete response
Histopathology
Machine learning
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