In recent years, statistical learning techniques have demonstrated significant utility in oncology. Cutting-edge methods from statistics, machine learning, and data science present novel opportunities to interpret complex patterns in diverse cancer datasets. Statistical learning enables researchers and clinicians to forecast patient outcomes (such as estimating cell types or individual patient prognosis and treatment response), detect biomarkers (discovering new biomarkers and therapeutic targets), optimize clinical trials (improving the efficiency and success of trials, speeding up drug development and enhancing trial success rates), and uncover hidden patterns (such as identifying subtle disease subtypes, spatial tumor heterogeneity, and temporal evolution patterns, yielding new insights into cancer biology).

This presentation underscores the significant role of statistical learning in identifying cancer cells through their biophysical characterization.