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23.01.2021 | Preclinical study

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

verfasst von: David W. Dodington, Andrew Lagree, Sami Tabbarah, Majid Mohebpour, Ali Sadeghi-Naini, William T. Tran, Fang-I Lu

Erschienen in: Breast Cancer Research and Treatment | Ausgabe 2/2021

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Abstract

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.
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Metadaten
Titel
Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients
verfasst von
David W. Dodington
Andrew Lagree
Sami Tabbarah
Majid Mohebpour
Ali Sadeghi-Naini
William T. Tran
Fang-I Lu
Publikationsdatum
23.01.2021
Verlag
Springer US
Erschienen in
Breast Cancer Research and Treatment / Ausgabe 2/2021
Print ISSN: 0167-6806
Elektronische ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-020-06093-4

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