Radiomics in Surgery: Preoperative Prediction of Cancer in the Lung and Other Areas

Radiomics, a burgeoning field in cancer research, leverages advanced quantitative analysis of medical images to predict cancer characteristics. This article explores its application in preoperative lung cancer prediction and surgery planning. Lung cancer, a leading global cause of cancer-related deaths, necessitates precise staging for effective treatment. Radiomics extracts intricate features from CT scans, offering noninvasive biomarkers indicative of tumor aggressiveness and prognosis. Four stages of radiomic analysis involve image acquisition, segmentation, feature generation, and outcome correlation. In surgery, radiomics aids in imaging-driven decision-making, predicting tumor behavior, and assessing therapy response. Challenges include standardization and small sample sizes, but artificial intelligence holds promise in overcoming these limitations, revolutionizing cancer diagnosis and management.