Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

Abstract Background Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). Th...

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Bibliographic Details
Main Authors: Timothy B. Fisher, Geetanjali Saini, T. S. Rekha, Jayashree Krishnamurthy, Shristi Bhattarai, Grace Callagy, Mark Webber, Emiel A. M. Janssen, Jun Kong, Ritu Aneja
Format: Article
Language:English
Published: BMC 2024-01-01
Series:Breast Cancer Research
Subjects:
Triple-negative breast cancer
Neoadjuvant chemotherapy
Machine learning
Digital image analysis
Feature engineering
Online Access:https://doi.org/10.1186/s13058-023-01752-y

Internet

https://doi.org/10.1186/s13058-023-01752-y

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