Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography

Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography

Patient outcomes of non-small-cell lung cancer (NSCLC) vary because of tumor heterogeneity and treatment strategies. This study aimed to construct a deep learning model combining both radiomic and clinical features to predict the overall survival of patients with NSCLC. To improve the reliability of...

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Bibliographic Details
Main Authors: Kuei-Yuan Hou, Jyun-Ru Chen, Yung-Chen Wang, Ming-Huang Chiu, Sen-Ping Lin, Yuan-Heng Mo, Shih-Chieh Peng, Chia-Feng Lu
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Cancers
Subjects:
radiomics
non-small-cell lung cancer
computed tomography
survival
deep learning
Online Access:https://www.mdpi.com/2072-6694/14/15/3798

Internet

https://www.mdpi.com/2072-6694/14/15/3798

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