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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Jiadong Song, Tianyu Zhao, Meng Zhang, Jinzhi Yang, Aonan Zhu, Xin Qi, Chao Yang, Yang Dong
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	BMC 2025-12-01
Sarja:	BMC Medical Imaging
Aiheet:	Habitat analysis Topological data analysis Radiomics Pancreatic ductal adenocarcinoma Histological grade
Linkit:	https://doi.org/10.1186/s12880-025-02094-1
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