Uncovering the Diagnostic Power of Radiomic Feature Significance in Automated Lung Cancer Detection: An Integrative Analysis of Texture, Shape, and Intensity Contributions

Uncovering the Diagnostic Power of Radiomic Feature Significance in Automated Lung Cancer Detection: An Integrative Analysis of Texture, Shape, and Intensity Contributions

Background: Lung cancer still maintains the leading position among causes of death in the world; the process of early detection surely contributes to changes in the survival of patients. Standard diagnostic methods are grossly insensitive, especially in the early stages. In this paper, radiomic feat...

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Bibliographic Details
Main Authors: Sotiris Raptis, Christos Ilioudis, Kiki Theodorou
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:BioMedInformatics
Subjects:
lung cancer
radiomics
machine learning
feature importance
SHAP
CT imaging
Online Access:https://www.mdpi.com/2673-7426/4/4/129
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