A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

ObjectivesAnaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement s...

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Bibliographic Details
Main Authors:	Cheng Chang, Xiaoyan Sun, Gang Wang, Hong Yu, Wenlu Zhao, Yaqiong Ge, Shaofeng Duan, Xiaohua Qian, Rui Wang, Bei Lei, Lihua Wang, Liu Liu, Maomei Ruan, Hui Yan, Ciyi Liu, Jie Chen, Wenhui Xie
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2021-03-01
Series:	Frontiers in Oncology
Subjects:	positron emission tomography/computed tomography (PET/CT) machine learning radiomics anaplastic lymphoma kinase (ALK) rearrangement lung adenocarcinoma
Online Access:	https://www.frontiersin.org/articles/10.3389/fonc.2021.603882/full

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