# Application of Radiomics in Melanoma: A Systematic Review and Meta-Analysis

## TL;DR

This paper reviews how radiomics is being used in melanoma, especially for predicting treatment response and in advanced disease settings.

## Contribution

The study provides a systematic review and meta-analysis of radiomic applications in melanoma, highlighting current trends and limitations.

## Key findings

- Radiomic models show strong discriminative performance in predicting treatment response with a pooled AUC of 0.83.
- Computed tomography and three-dimensional analysis are the most commonly used imaging and analysis methods in melanoma radiomics.
- Most studies focus on metastatic disease and immunotherapy, but molecular integration and validation remain limited.

## Abstract

Radiomics is being increasingly investigated as a tool to support clinical decisions in different solid tumors. The aim of our review was to analyze the state of the art of radiomic applications in melanoma. We found that metastatic disease and immunotherapy were the settings most explored through computed tomography imaging and three-dimensional analysis. The prediction of treatment response was the most investigated outcome. Radiomic models can achieve strong discriminative performance with low to moderate heterogeneity in methodologies. Limited validation strategies need to be overcome through greater standardization and transparency, and further verified in large prospective cohorts.

Background/Objectives: Radiomics is a powerful and emerging tool in oncology, with many potential applications in predicting therapy response and prognosis. To assess the current state of radiomics in melanoma, we conducted a systematic review of its various clinical uses. Methods: We searched three databases: PubMed, Web of Science and Scopus. Each study was classified based on multiple variables, including patient number, metastasis number, therapy, imaging modality, clinical endpoints and analysis methods. The risk of bias in the systematic review was assessed with QUADAS-2, and the certainty of evidence in the meta-analysis with GRADE. Results: Forty studies involving 4673 patients and 24,561 lesions were included in the analysis. Metastatic disease was the most frequently studied clinical setting (85%). Immunotherapy was the most commonly investigated treatment, featured in half of the studies. Computed tomography (CT) was the preferred imaging modality, appearing in 17 studies (42.5%). Radiomic features were most often extracted using three-dimensional (3D) analysis (72.5%). Across 24 studies investigating the prediction of treatment response and survival, only 9 provided sufficient data (Area Under the Curve, AUC, and standard error, SE) for inclusion. A random-effects model estimated a pooled AUC of 0.83 (95% CI: 0.74 to 0.92), indicating strong discriminative performance of the radiomic models included. Low to moderate heterogeneity was observed (I2 = 28.6%, p = 0.4741). No evidence of publication bias was detected (p = 0.470). Conclusions: Radiomics is increasingly being explored in the context of melanoma, particularly in advanced disease settings and in relation to immunotherapy. Most studies rely on CT imaging and 3D feature extraction, while molecular integration remains limited. Despite promising findings with strong discriminative performance in predicting therapy response, further prospective, standardized studies with higher methodological rigor are needed to validate radiomic biomarkers and integrate them into clinical decision-making.