Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma

Abstract Objective This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). Methods A retrospective analysis was conducted on a total...

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Bibliographic Details
Main Authors: Hongfan Liao, Jiang Yuan, Chunhua Liu, Jiao Zhang, Yaying Yang, Hongwei Liang, Song Jiang, Shanxiong Chen, Yongmei Li, Yanbing Liu
Format: Article
Language:English
Published: SpringerOpen 2023-12-01
Series:Insights into Imaging
Subjects:
Machine learning
Radiomics
Deep learning
Tumor stroma ratio
Pancreatic ductal adenocarcinoma
Online Access:https://doi.org/10.1186/s13244-023-01553-z

Internet

https://doi.org/10.1186/s13244-023-01553-z

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