Individual risk and prognostic value prediction by interpretable machine learning for distant metastasis in neuroblastoma: A population-based study and an external validation

Abstract

Purpose

Neuroblastoma (NB) is a childhood malignancy with a poor prognosis and a propensity for distant metastasis (DM). We aimed to establish machine learning (ML) based model to accurately predict risk of DM and prognosis of NB patients with DM.

Methods

We analyzed NB patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2020. Univariate and multivariate logistic analysis were employed to select meaning variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were evaluated by area under the operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, cross-entropy, Brier scores, Balanced Accuracy and F-beta score. An optimal ML model was constructed to predict DM, and the predictive results were explained by SHapley Additive exPlanations (SHAP) framework. Meanwhile, 101 ML algorithm combinations were developed to select the best model with highest C-index to predict prognosis of NB patients with DM.

Results

A total of 1,668 NB patients from SEER database was consecutively enrolled. We identified that tumor primary site, grade, surgery type, regional lymph nodes, radiotherapy and chemotherapy are significant risk factors for DM. CatBoost model was selected as the best prediction model, and AUC was 0.846 (95 %CI: [0.804,0.899]), 0.834 (95 %CI: [0.796,0.873]) and 0.813 (95 %CI: [0.776,0.852]) in training, internal test and external test sets, with 0.777 accuracy, 0.839 sensitivity, 0.72 specificity and 0.731 precision in training set. Grade, chemotherapy and radiotherapy had the greatest effects on DM according to SHAP results. For prognosis prediction, “RSF + GBM” algorithm was the best prognostic model with C-index of 0.656, 0.611 and 0.629 in training, internal test and external test sets.

Conclusions

Our ML models demonstrate excellent accuracy and reliability, offering more precise personalized metastasis diagnosis and prognostic prediction to NB patients.