CT habitat radiomics and topological data analysis based on interpretable machine learning for prediction of pancreatic ductal adenocarcinoma pathological grading

Abstract Background This study explores the feasibility and effectiveness of an interpretable machine learning model for assessing the pathological grading of pancreatic ductal adenocarcinoma (PDAC) using radiomics and topological features derived from contrast-enhanced CT habitat subregions. Method...

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Detalhes bibliográficos

Principais autores:	Jiadong Song, Tianyu Zhao, Meng Zhang, Jinzhi Yang, Aonan Zhu, Xin Qi, Chao Yang, Yang Dong
Formato:	Artigo
Idioma:	Inglês
Publicado em:	BMC 2025-12-01
coleção:	BMC Medical Imaging
Assuntos:	Habitat analysis Topological data analysis Radiomics Pancreatic ductal adenocarcinoma Histological grade
Acesso em linha:	https://doi.org/10.1186/s12880-025-02094-1
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