Decoding Lung Cancer Radiogenomics: A Custom Clustering/Classification Methodology to Simultaneously Identify Important Imaging Features and Relevant Genes

Decoding Lung Cancer Radiogenomics: A Custom Clustering/Classification Methodology to Simultaneously Identify Important Imaging Features and Relevant Genes

Background: This study evaluated a custom algorithm that sought to perform a radiogenomic analysis on lung cancer genetic and imaging data, specifically by using machine learning to see whether a custom clustering/classification method could simultaneously identify features from imaging data that co...

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Bibliographic Details
Main Authors: Destie Provenzano, John P. Lichtenberger, Sharad Goyal, Yuan James Rao
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
lung cancer
machine learning
radiogenomics
deep clustering
deep learning
ResNet
Online Access:https://www.mdpi.com/2076-3417/15/7/4053
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