Nomograms versus artificial intelligence platforms: which one can better predict sentinel node positivity in melanoma patients?

Abstract

Nomograms are commonly used in oncology to assist clinicians in individualized decision-making processes, such as considering sentinel node biopsy (SNB) for melanoma patients. Concurrently, artificial intelligence (AI) is increasingly being utilized in medical predictions. This study aims to compare the predictive accuracy of nomograms and AI platforms for SNB positivity in a real-world cohort of melanoma patients. A retrospective analysis of melanoma patients who underwent SNB from 2020 to 2024 in a single institution was performed. Three open-access nomograms and three public AI platforms were employed to assess SNB positivity based on comprehensive clinical and pathological characteristics. Our cohort comprised 62 melanoma patients who have undergone SNB, of whom 12 (19.4%) were positive. There was no concordance among the three nomograms, nor among AI platforms ( P  < 0.001). Only the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram scored statistically different between positive and negative SNB ( P  = 0.04), and ChatGPT was the only AI platform that was also statistically significant ( P  = 0.02). Only ChatGPT score was statistically significant for SNB positivity after univariate logistic regression (odds ratio: 1.05; 95% confidence interval: 1.004-1.108; P = 0.03). A receiver operating characteristic curve based on ChatGPT predictions generated a model with an area under the curve (AUC) of 0.702. Integrating MSKCC predictions marginally improved the model's predictive performance, enhancing the AUC to 0.715. In conclusion, SNB positivity could be better performed by an AI platform in this cohort of patients. Enhancing AI platforms could provide better populations for nomogram validation, which would lead to better predictive models.