Journal
BREAST CANCER RESEARCH AND TREATMENT
Volume 186, Issue 2, Pages 379-389Publisher
SPRINGER
DOI: 10.1007/s10549-020-06093-4
Keywords
Breast cancer; Neoadjuvant chemotherapy; Artificial intelligence; Digital pathology
Categories
Funding
- tri-council (Government of Canada) New Frontiers in Research Fund
- Terry Fox Research Institute
- Women's Health Golf Classic Foundation Fund
- Natural Sciences and Engineering Research Council of Canada
- York Research Chair in Quantitative Imaging and Smart Biomarkers
Ask authors/readers for more resources
The study aimed to predict response to neoadjuvant chemotherapy using artificial intelligence and found that tumor multifocality/multicentricity, nuclear intensity, and GLCM-COR were independently associated with likelihood of achieving a pathological complete response (pCR). The model was able to successfully classify 79% of cases.
Purpose 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. Methods Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). 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. Results 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 = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) 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). Conclusion Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available