For example, Vanguri et al. [5] developed a dynamic deep attention-based multiple-instance learning model with masking (DyAM) from genomic, histological, and radiological data to predict the response to immunotherapies, using RECIST criteria as an indicator of response in NSCLC patients treated with PD-L1 inhibition. The gene discussed is CD274; the disease is non-small cell lung carcinoma.