Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients.

Autor: Dodington DW; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada., Lagree A; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada., Tabbarah S; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada., Mohebpour M; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada., Sadeghi-Naini A; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.; Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada., Tran WT; Biological Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada.; Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.; Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto, Toronto, ON, Canada., Lu FI; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada. fangi.lu@sunnybrook.ca.; Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Rm E423a, Toronto, ON, M4N 3M5, Canada. fangi.lu@sunnybrook.ca.
Jazyk: angličtina
Zdroj: Breast cancer research and treatment [Breast Cancer Res Treat] 2021 Apr; Vol. 186 (2), pp. 379-389. Date of Electronic Publication: 2021 Jan 23.
DOI: 10.1007/s10549-020-06093-4
Abstrakt: 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.
Databáze: MEDLINE
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