The proposed feature ranking approach can be extended to other types of cancer datasets by following a similar methodology. First, the dataset specific to the particular type of cancer needs to be obtained, which may include clinical, genetic, radiomics, and demographic data. The feature selection methods used in the proposed approach, such as wrapper, filter, and embedded techniques, can be applied to rank the features in the new dataset. By considering the importance of features across different selection methods and using mutual information-based criteria, a final ranking of features can be obtained. This process can help identify the most relevant features for early diagnosis and treatment of other types of cancer, similar to how it was done for renal cancer in the study.