A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer

A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer

Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC). Methods: Ninety-one...

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Bibliographic Details
Main Authors: Hyunjong Lee, Seung Hwan Moon, Jung Yong Hong, Jeeyun Lee, Seung Hyup Hyun
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Cancers
Subjects:
colorectal cancer
FDG PET/CT
radiomics
tumor mutational burden
prognosis
Online Access:https://www.mdpi.com/2072-6694/15/15/3841

Internet

https://www.mdpi.com/2072-6694/15/15/3841

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