Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

Abstract Backgrounds Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomar...

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Bibliographic Details
Main Authors: Xiaorui Han, Yuan Guo, Huifen Ye, Zhihong Chen, Qingru Hu, Xinhua Wei, Zaiyi Liu, Changhong Liang
Format: Article
Language:English
Published: BMC 2024-01-01
Series:Breast Cancer Research
Subjects:
Machine learning
Radiomics signature
Breast cancer
Tumor microenvironment
Online Access:https://doi.org/10.1186/s13058-024-01776-y

Internet

https://doi.org/10.1186/s13058-024-01776-y

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