Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.

Journal: PeerJ
PMID: 39026540

Abstract

BACKGROUND: Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers.

Authors

  • Xue Li
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Chunmei Li
    Radiology, Beijing Hospital, Beijing, China.
  • Hong Wang
    Department of Cardiology, Liuzhou Workers' Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
  • Lei Jiang
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
  • Min Chen
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.

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