Construction and validation of machine learning models for predicting lymph node metastasis in cutaneous malignant melanoma: a large population-based study

Abstract

Background: Lymph node status is essential for determining the prognosis of cutaneous malignant melanoma (CMM). This study aimed to develop a machine learning (ML) model for predicting lymph node metastases (LNM) in CMM. Methods: We gathered data on 6,196 patients from the Surveillance, Epidemiology, and End Results (SEER) database, including known clinicopathologic variables, using six ML algorithms, including logistic regression (LR), support vector machine (SVM), Complement Naive Bayes (CNB), Extreme Gradient Boosting (XGBoost), RandomForest (RF), and k-nearest neighbor algorithm (kNN), to predict the presence of LNM in CMM. Subsequently, we established prediction models. The utilization of the adaptive synthetic (ADASYN) method served to address the challenge posed by imbalanced data. We assessed prediction model performance in terms of average precision (AP), sensitivity, specificity, accuracy, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA). Furthermore, employing SHapley Additive exPlanation (SHAP) analysis resulted in the creation of visualized explanations tailored to individual patients. Results: Among the 6,196 CMM cases, 19.9% (n=1,234) presented with LNM. The XGBoost model showed the best predictive performance when compared with the other algorithms (AP of 0.805). XGBoost showed that age and Breslow thickness were the two most important factors related to LNM. Conclusions: The XGBoost model predicted LNM of CMM with a high level of precision. We hope that this model could assist surgeons in accurately evaluating surgical approaches and determining the extent of surgery, while also guiding the subsequent adjuvant therapies, thereby improving the prognosis of patients.