A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study

A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study

Abstract Objective To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to preoperatively predict human epidermal growth factor receptor 2 (HER2) status in bladder cancer (BCa) with multicenter validation. Methods In this retrospective study, 207 patients w...

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Bibliographic Details
Main Authors: Zongjie Wei, Xuesong Bai, Yingjie Xv, Shao-Hao Chen, Siwen Yin, Yang Li, Fajin Lv, Mingzhao Xiao, Yongpeng Xie
Format: Article
Language:English
Published: SpringerOpen 2024-10-01
Series:Insights into Imaging
Subjects:
Radiomics
Bladder cancer
Computed tomography
HER2
Machine learning
Online Access:https://doi.org/10.1186/s13244-024-01840-3
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