Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics

Improving resectable gastric cancer prognosis prediction: A machine learning analysis combining clinical features and body composition radiomics

We evaluate the significance of body composition radiomics in predicting outcomes for resectable gastric cancer (GC) patients, as these parameters can enhance optimal surveillance strategies and risk-stratification models. Automated segmentation using deep learning algorithms was employed on CT imag...

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Bibliographic Details
Main Authors: Gianni S.S. Liveraro, Maria E.S. Takahashi, Fabiana Lascala, Luiz R. Lopes, Nelson A. Andreollo, Maria C.S. Mendes, Jun Takahashi, José B.C. Carvalheira
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Informatics in Medicine Unlocked
Subjects:
Body composition
Machine learning
Stomach neoplasms
Abdominal visceral fat
Online Access:http://www.sciencedirect.com/science/article/pii/S2352914824001655
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http://www.sciencedirect.com/science/article/pii/S2352914824001655

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