Machine learning and artificial intelligence are transforming cancer research and precision medicine. Computational pathology now uses deep learning models like convolutional neural networks (CNNs) to analyze histopathological images and predict molecular alterations in cancer. These models have shown success in predicting gene mutations, molecular subtypes, and biomarkers like microsatellite instability and somatic mutations such as TP53 and PTEN. Novel architectures like Panoptes classify complex cancers like endometrial cancer from image data. However, most existing studies rely heavily on genomic and transcriptomic data, often neglecting the tumor proteome and lacking pathology expert input in model design. This limits the biological interpretability and real-world clinical application of these models. To address this, the study uses data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), offering comprehensive genomics-to-proteomics profiles linked with matched histopathology images and patient outcomes.

This study applied machine learning to assess lifestyle and occupational risk factors for bladder cancer. A balanced case-control study of 692 bladder cancer patients and 692 healthy controls was conducted using data from the Iranian Cancer Registry. Random Forest achieved the highest predictive accuracy. Recurrent urinary tract infections, bladder stones, and lifestyle factors such as smoking and diet were key predictors. The study provides a valuable tool for early screening, risk assessment, and the development of preventive strategies in public health.