Interpretable machine learning-based prediction of liver metastasis risk in elderly patients with small cell lung Cancer: A study based on the SEER database and external validation in a Chinese cohort
Abstract
PURPOSE: Small cell lung cancer (SCLC) is a highly aggressive malignancy with a high incidence of liver metastases, particularly among elderly patients, which significantly worsens survival outcomes. However, efficient predictive tools targeting this population remain scarce. This study aimed to develop and validate an interpretable machine learning-based model to re-stratify the risk of liver metastasis in elderly patients with SCLC after completion of routine staging evaluation at initial diagnosis.
METHODS: A total of 10,080 patients aged ≥60 years with histologically confirmed SCLC were included from the SEER database (2010-2017) and the Affiliated Hospital of North Sichuan Medical College, China (2010-2024). Patients from SEER were randomly assigned to a training set (n = 7719) and an internal validation set (n = 1930), while 431 patients from China comprised the external validation set. Feature selection was performed using the Boruta algorithm, identifying 11 key variables. Seven ML models, namely, Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, XGBoost, and LightGBM, were developed to compare their predictive performance. The optimal model was further interpreted using SHAP (SHapley Additive exPlanations).
RESULTS: The incidence of liver metastasis was approximately 32.89%, 35.39%, and 32.71% in the training, internal validation, and external validation sets, respectively. Comparative analysis across models demonstrated…
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 10,080 participants or records.
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