Work

A nomogram predicting the risk of extrathoracic metastasis at initial diagnosis of T≤3cmN0 lung cancer

Year: 2024

Type: article

Abstract: Background: lung cancer patients are not fully understood. We aimed to develop a model to predict the risk of extrathoracic metastasis in those patients. Methods: Clinicopathological data of patients were collected from Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable analyses using logistic regression were conducted to identify risk factors. A predictive model and corresponding nomogram were developed based on the risk factors. The model was assessed using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and decision curve. Results: patients were enrolled, of whom 251 (1.25%) were diagnosed with extrathoracic metastasis at the initial diagnosis. Aged ≤50 [odds ratio (OR): 2.05, 95% confidence interval (CI): 1.19-3.53, P=0.01] and aged ≥81 [1.65 (1.05-2.58), P=0.03], Hispanic [1.81 (1.20-2.71), P=0.004], location of bronchus [3.18 (1.08-9.35), P=0.04], larger tumor size, pleural invasion, and a history of colorectal cancer [2.01 (1.01-4.00), P=0.046] were independent risk factors. In the training cohort and validation cohort, the AUCs of the developed model were 0.727, 0.728 respectively, and the results of Hosmer-Lemeshow test were P=0.47, P=0.61 respectively. The decision curve showed good clinical meaning of the model. Conclusions: lung cancer patients was not rare. The model based on the risk factors showed good performance in predicting the risk of extrathoracic metastasis.

Source: Translational Lung Cancer Research

DOI: 10.21037/tlcr-24-338