Frequency Ranking of Imaging Biomarkers for Lung Cancer Risk Stratification Using a Hybrid Elastic Net Method

Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for novel and robust biomarkers to improve prognostication and guide precision oncology. While traditional clinical variables such as tumor stage, age, and sex are routinely used for survival prediction, their prognostic performance is limited. Imaging biomarkers derived from radiomic analysis of advanced medical imaging have emerged as a promising class of noninvasive cancer biomarkers, enabling quantitative characterization of tumor phenotypes. In this study, we investigated the prognostic utility of radiomic imaging biomarkers, with a particular focus on the texture-based feature Busyness, and compared their performance against conventional clinical factors. Survival analyses demonstrated that Busyness achieved significantly stronger discrimination of survival outcomes than stage, age, or sex. Stratified analyses further showed that Busyness consistently remained a dominant predictor of survival across age and sex subgroups, whereas tumor stage alone provided limited prognostic separation. To address class imbalance and enhance model robustness, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, further supporting the stability of the imaging biomarker findings. These results highlight the potential of radiomic imaging biomarkers as powerful prognostic tools in lung cancer and support their integration into clinical workflows. This work contributes to the growing landscape of new cancer biomarkers and provides a foundation for future studies integrating imaging biomarkers with molecular and genomic markers to achieve improved prognostic accuracy.