Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Clinical, Conventional CT and Radiomic Feature-Based Machine Learning Models for Predicting ALK Rearrangement Status in Lung Adenocarcinoma Patients

Objectives: To predict the anaplastic lymphoma kinase (ALK) mutations in lung adenocarcinoma patients non-invasively with machine learning models that combine clinical, conventional CT and radiomic features.Methods: This retrospective study included 335 lung adenocarcinoma patients who were randomly...

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Bibliographic Details
Main Authors: Lan Song, Zhenchen Zhu, Li Mao, Xiuli Li, Wei Han, Huayang Du, Huanwen Wu, Wei Song, Zhengyu Jin
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Oncology
Subjects:
lung neoplasms
radiomics
tomography
X-ray computed
anaplastic lymphoma kinase
gene mutation
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.00369/full

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https://www.frontiersin.org/article/10.3389/fonc.2020.00369/full

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