Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers

Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers

Abstract Objective This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer. Materials and Methods This retrospective study included women with breast can...

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Bibliographic Details
Main Authors: Mengwei Ma, Weimin Xu, Jun Yang, Bowen Zheng, Chanjuan Wen, Sina Wang, Zeyuan Xu, Genggeng Qin, Weiguo Chen
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Medical Imaging
Subjects:
Contrast-enhanced mammography
Interpretable machine learning model
SHapley additive exPlanation
Multinomial naive Bayes
Breast cancer
Molecular subtype
Online Access:https://doi.org/10.1186/s12880-025-01765-3
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https://doi.org/10.1186/s12880-025-01765-3

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