Breast Cancer Research and Treatment | 2021
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients
Abstract
Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC. Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n\u2009=\u200937, pPR n\u2009=\u200921). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined. In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR\u2009=\u20090.14, p\u2009=\u20090.012), nuclear intensity (OR\u2009=\u20091.23, p\u2009=\u20090.018), and GLCM-COR (OR\u2009=\u20090.96, p\u2009=\u20090.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR). Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.