Diagnostic artificial intelligence model predicts lymph node status in non-small cell lung cancer using simplified examination.

Abstract

Background: Artificial intelligence (AI) technology was introduced in medical data area and applied disease prediction models. This study aimed to establish an AI model for predicting lymph node metastasis based on simple medical examinations in patients with non-small cell lung cancer (NSCLC).

Methods: We retrospectively analyzed 988 patients with NSCLC who underwent radical pulmonary resection with mediastinal lymph node dissection between January 2011 and October 2022. We collected clinical characteristics including age, sex, smoking history, tumor marker levels, tumor side, segment location, total tumor size, solid tumor size and consolidation-to-tumor ratio, obtainable from medical interview, blood tests and plain computed tomography (CT) of the chest. All patients were randomly classified into a training set (n=790) and a validation set (n=198). Six algorithms including Support Vector Classification (SVC), k-nearest neighbor algorithm (k-NN), logistic regression (LR), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP) were created to decide the lymph node metastasis.

Results: The GB model showed the best diagnostic performance, with 80.0% accuracy, 95.6% specificity and an area under the curve (AUC) of 0.75.

Conclusions: An AI model showed high specificity and accuracy for predicting lymph node metastasis. These models have potential to categorize suitable surgical procedures for NSCLC patients without needing contrast-enhanced CT or positron emission tomography.