Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer

Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT‐Based Radiomic Features in Non‐Small Cell Lung Cancer

ABSTRACT Background Non‐small‐cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques a...

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Bibliographic Details
Main Authors: Shrey S. Sukhadia, Christoph Sadée, Olivier Gevaert, Shivashankar H. Nagaraj
Format: Article
Language:English
Published: Wiley 2024-12-01
Series:Cancer Medicine
Subjects:
gene expression and non‐small cell lung cancer
machine learning
radiogenomics
radiomics
Online Access:https://doi.org/10.1002/cam4.70509
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https://doi.org/10.1002/cam4.70509

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