The Application of Radiomics in the Prognosis of Non-small Cell Lung Cancer

Abstract

Lung cancer remains a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent type. Accurately predicting NSCLC prognosis is crucial for optimizing patient survival. However, traditional assessment tools often lack the comprehensive and precise capability to effectively stratify patient risk. Recent research has focused on exploring the potential of imaging histology technology for NSCLC prognosis. This article delves into the core principles of imaging histology and reviews the current state of research on its application in predicting NSCLC outcomes.

Keywords

• machine learning
• radiomics
• non-small cell lung cancer