Machine learning-based prediction of N2 lymph node metastasis in non-small cell lung cancer.

Abstract

Background: Lung cancer is a leading cause of cancer-related mortality worldwide. Accurate staging of mediastinal lymph nodes is a crucial step in determining appropriate treatment approaches. Current noninvasive diagnostic methods do not provide sufficient accuracy to confidently decide on surgery without histological confirmation. Our study aimed to develop a artificial intelligence model for the precise prediction of N2 lymph node metastasis.

Methods: We retrospectively analyzed 1489 patients who underwent standard cervical mediastinoscopy at our department, including 472 patients diagnosed with non-small cell lung cancer. We developed three distinct prediction models for N2 lymph node station metastasis: one using standard statistical analysis, another utilizing an image processing deep learning algorithm with thoracic CT, and the third employing various machine learning methods with clinicopathological and radiological data. We compared diagnostic accuracy, area under the curve (AUC), sensitivity, and specificity rates, as well as the F1-score of all models.

Results: Linear discriminant analysis, quadratic discriminant analysis, Gaussian naive Bayes, and artificial neural networks all surpassed 90% accuracy. The linear support vector machine demonstrated the highest performance, with an accuracy of 95.7%, an AUC of 93.5%, and an F1-score of 92%, respectively and outperformed the logistic regression-based statistical model, which reached an accuracy of 90.6% and an AUC of 85.7%.

Conclusion: Machine learning models outperformed standard statistical analysis models in predicting N2 lymph node metastasis. Implementing these machine learning prediction models might greatly improve the accuracy of mediastinal lymph node metastasis detection, thereby enhancing clinical decision making and patient outcomes.