Application of Interpretable Machine Learning Algorithm to Predict Lymph Node Metastasis in Cutaneous Malignant Melanoma.

INTRODUCTION

Cutaneous malignant melanoma (CMM) is the most lethal form of skin cancer worldwide. The precise prediction of lymph node metastasis is critical for personalized treatment and improved patient outcomes. However, no prior study has employed interpretable machine learning techniques to predict lymph node metastasis in CMM. This study aimed to utilize interpretable machine learning to integrate multidimensional data from the Surveillance, Epidemiology, and End Results (SEER) database - encompassing clinical characteristics, pathological information, and biomarkers of CMM - to construct various predictive models for lymph node metastasis.

METHODS

We constructed six machine learning models to predict lymph node metastasis using clinical, pathological, and biomarker data from 2,448 patients with CMM in the SEER database. These models comprise a support vector machine, random forest (RF), XGBoost, LightGBM, adaptive boosting, and gradient boosting decision tree. The primary influential factors were identified using Gaussian Naive Bayes and gradient boosting algorithms. Shapley additive explanations (SHAP) analysis facilitates visual interpretation in individual patients. Model performance was evaluated based on accuracy, sensitivity, specificity, Brier score, and area under the receiver operating characteristic curve (AUC).

RESULTS

The RF algorithm exhibited the highest predictive performance with an AUC of 0.897, accuracy of 0.821, sensitivity of 0.876, specificity of 0.765, and Brier score of 0.086. The primary influential variables were T stage, chemotherapy, ulceration, pretreatment lactate dehydrogenase (LDH) levels, and radiation therapy. SHAP analysis confirmed a significant association and highlighted the critical function of (LDH) as a predictive biomarker.

CONCLUSION

This study successfully established an accurate predictive model for lymph node metastasis in patients with CMM using machine learning techniques, offering a significant reference to aid clinician treatment decisions.