Precision Surgical Oncology through Artificial Intelligence Molecular Imaging and Biomarker Guided Therapeutic Strategies

Abstract

Surgery remains a major component of curative cancer treatment. Conventional surgical decision making is mainly based on anatomical imaging, histopathology, and clinicopathological features. These approaches have improved considerably but it cannot always identify microscopic disease. Recent advances in artificial intelligence, molecular imaging, and biomarker research are changing this situation. Molecular imaging provides surgeons with information about tumor biology during surgery through the use of targeted fluorescent probes and other imaging agents. Biomarkers such as circulating tumor DNA provide additional information about residual disease and the risk of recurrence after surgery. The combination of these technologies creates a new model of precision surgical oncology in which treatment decisions can be guided by both anatomical and molecular information. Artificial intelligence assisted hyperspectral imaging and Raman spectroscopy have demonstrated potential for intraoperative margin assessment in cancers. Circulating tumor DNA has further shown value in identifying patients at increased risk of recurrence after surgery and in selecting patients for adjuvant treatment. Despite these advances several limitations remain and most AI models require external validation. Molecular imaging agents remain target dependent. Biomarker assays require standardized methods and careful clinical interpretation. The integration of these technologies into routine surgical practice will therefore require prospective multicenter trials and appropriate regulatory frameworks. This review discusses the development of precision surgical oncology with particular emphasis on AI based decision to support biomarker guided treatment and future clinical translation.