Development of a nomogram for predicting the risk of lymph node metastasis in non-small cell lung cancer.

Background: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death worldwide. The accurate preoperative prediction of lymph node (LN) status is crucial for reducing NSCLC mortality rates and formulating treatment strategies. This study aimed to develop and validate a nomogram for the individualized prediction of lymph node metastasis (LNM) risk in NSCLC patients, thereby guiding therapeutic decision making. Methods: The complete clinical, computed tomography (CT) imaging, and pathological data of patients with pathologically confirmed NSCLC at three independent tertiary medical centers from January 2014 to October 2023 were retrospectively collected and analyzed. In total, 2,725 patients were enrolled from Centers 1 and 2, and 112 patients were enrolled from center 3. The patients were randomly divided into training and test datasets at an 8:2 ratio. A clinical CT imaging predictive model was established by binary logistic regression analysis, and a corresponding nomogram chart was constructed. The predictive ability, discriminative ability, accuracy, and clinical benefit of the model were evaluated by receiver operating characteristic (ROC) curve, calibration curve, decision curve, and clinical impact curve (CIC) analyses. Results: Mixed ground-glass opacity (mGGO), obstructive inflammation, emphysema or bullae, a short diameter (SD), and heterogeneous ventilation or perfusion (HVP) were identified as independent risk factors for LN positivity in NSCLC. The model had an area under the curve (AUC) of 0.893, an accuracy of 76.8%, a sensitivity of 91.6%, and a specificity of 74.2% on the training dataset. While it had an AUC of 0.907, an accuracy of 86.2%, a sensitivity of 89.3%, and a specificity of 80.9% on the test dataset. Conclusions: We developed and validated a robust clinical CT imaging nomogram model that effectively predicts LN positivity in NSCLC.