Survival Analysis in Lung Cancer: A Comparative Study of Different Approaches Using NSCLC-Radiomics (Lung1) Data

Lung cancer is known as one of the most prevalent and dangerous types of cancer, and its survival rate is lower compared to many other types of cancer. Survival analyses are vital to accurately estimate the expected time to death of lung cancer patients. Since these analyses are generally based on the judgment of clinicians, accuracy is often a matter of debate. This highlights the importance of performing survival analysis using statistical or machine learning systems that use clinical data and/or images obtained from medical imaging devices. In this study, we applied various methods to analyze clinical records from a publicly available lung cancer database. We approached survival analysis as both a survival regression and a survival classification problem, conducting comparative analyses using multiple techniques. We conducted a detailed examination of numerous survival analysis studies related to lung cancer. Our findings revealed that incorporating the number of slices containing the tumor area in patients' CT images (GTV1-SliceNum) as an extra feature significantly enhanced survival analysis performance. Additionally, we observed that the presence of censored observations negatively impacted survival classification performance.