Beyond Routine Surveillance Imaging for Melanoma: Emerging Technologies for Detecting Early Recurrence and Guiding Clinical Management

Melanoma recurrence risk is highest within the first 2 years after diagnosis and progressively declines thereafter. Current surveillance strategies remain largely guided by clinicopathologic risk stratification, with the comprehensive medical history, physical examination, and complete skin assessment forming the cornerstone of follow-up. Although cross-sectional imaging and lymph node ultrasound are used in selected higher-risk patients, routine intensive imaging has not consistently demonstrated survival benefit and may increase costs and false-positive findings. Emerging technologies are reshaping melanoma surveillance and clinical management. Circulating tumor DNA (ctDNA) has shown promise as a minimally invasive biomarker capable of detecting molecular residual disease and anticipating clinical recurrence. Persistent or newly positive ctDNA after surgery is consistently associated with inferior recurrence-free survival. However, ctDNA does not reliably detect all recurrence patterns and its sensitivity varies according to disease burden and metastatic site. Prospective validation and clarification of how ctDNA should guide adjuvant therapy or imaging strategies remain necessary. In parallel, CD8-targeted positron emission tomography (CD8 PET) has emerged as a novel functional imaging modality capable of noninvasively visualizing whole-body T-cell dynamics. By differentiating tumor burden from immune infiltration and capturing early T-cell recruitment, CD8 PET offers predictive insights into immunotherapy response. Nevertheless, limitations of this technique include dependence on optimal imaging timing, limited tracer availability, cost, and an inability to directly assess T-cell functionality. Together, ctDNA and immune-focused imaging approaches represent promising steps toward precision surveillance and management of melanoma. Further robust prospective studies are required to define their integration into clinical decision making and optimize patient outcomes.