Morphological Analysis of Tumor Microenvironment in HER2-Positive Breast Cancer: Predicting Response to Neoadjuvant Chemotherapy on Histopathological Images

Background Tumor microenvironment (TME) biomarkers derived from histopathological images of HER2+ breast cancer (HER2+BC) can effectively predict pathological complete response (pCR) following neoadjuvant chemotherapy (NAC), thereby enhancing patient prognosis. In this study, we quantitatively assessed the morphological information of critical regions in the TME and analyzed their predictive potential for pCR. Methods The retrospective analysis included 147 HER2+BC patients treated with NAC, comprising 85 from the Yale Response dataset for training and 62 from the IMPRESS HER2+ dataset for external validation. Initially, VGG-16 and Xception networks were utilized to segment hematoxylin and eosin-stained histopathology images, generating tissue segmentation images (TS-images). Tumor and non-tumor regions were identified based on the TS-images, from which tumor-infiltrating lymphocytes (TILs) and non-tumor-infiltrating lymphocytes (non-TILs) were extracted, respectively. Subsequently, the morphological information of these regions was quantified through the measurement of connected components. Feature selection was performed based on combined morphological and clinical information, employing the least absolute shrinkage and selection operator. Finally, selected features were input into a multilayer perceptron for training and validated on an external test cohort. Results In external validation, models derived from non-TILs achieved an area under the curve (AUC) of 0.873 in predicting pCR, with F1 score, PPV, recall, and NPV of 0.889, 0.821, 0.970, and 0.933, respectively. This performance significantly surpassed models trained on non-tumor (AUC = 0.779), tumor (AUC = 0.732), TILs (AUC = 0.594), and lymphocytes (AUC = 0.668). Furthermore, despite using 20% of the samples for training, the model trained on non-TILs maintained its high performance (AUC = 0.722). Univariate analyses of pCR revealed significant morphological features, such as the significance area filled mean for non-TILs (p value = 0.026) and the significance number for non-tumor (p value = 0.003). Conclusion The TME-based morphological information from histopathological images demonstrates accurate prediction of pCR, offering considerable potential for more precise patient stratification for NAC.