PloS one
Predicting lymph node metastasis in non-small cell lung cancer using clinical and PET/CT-derived morphological features: An interpretable machine-learning study
By Xu Y, Hao J, Dou Y and 1 others.
Abstract
Background
Accurate prediction of lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) is crucial for staging and treatment planning. This study aimed to establish a standardized machine learning workflow based on routinely available clinical, serological, and PET/CT-derived parameters to enhance preoperative LNM risk stratification.
Methods
This retrospective study included 255 patients with histologically confirmed NSCLC, all of whom underwent systematic lymph-node dissection. Patients were randomly divided, with stratification by pathological lymph-node status, into a training cohort (n = 178) and an internal held-out test test cohort (n = 77). Candidate predictors were screened using univariable and multivariable logistic regression in the training cohort. Six machine-learning algorithms were trained and optimized using repeated nested five-fold cross-validation. After model development was completed, all six models were applied without modification to the test cohort. Test-set AUCs were compared using paired DeLong tests with Holm correction. The primary model was further evaluated for calibration, clinical utility, and feature contribution using SHAP. A conventional multivariable logistic regression model using the same four predictors was added as a simple reference model, and its discrimination and calibration were compared with those of XGBoost.