Objective To evaluate the predictive performance of the geometric characteristics, centerline (CL) of pulmonary nodules for prognosis in patients with surgically treatment in the National Lung Screening Trial (NLST). MethodsCT images of 178 patients who underwent surgical treatment and were diagnosed with non-small cell lung cancer (NSCLC) in the low-dose CT (LDCT) cohort from the NLST image database were selected, including 99 males and 79 females, with a median age of 64 (59, 68) years. CT images were processed using commercial software Mimics 21.0 to record the volume, surface area, CL and the area perpendicular to the centerline of pulmonary nodules. Receiver operating characteristic (ROC) curve was used to compare the predictive performance of LD, AD and CL on prognosis. Univariate Cox regression was used to explore the influencing factors for postoperative disease-free survival (DFS) and overall survival (OS), and meaningful independent variables were included in the multivariate Cox regression to construct the prediction model. ResultsThe area under the curve (AUC) of CL for postoperative recurrence and death were 0.650 and 0.719, better than LD (0.596, 0.623) and AD (0.600, 0.631). Multivariate Cox proportional risk regression analysis showed that pulmonary nodule volume (P=0.010), the maximum area perpendicular to the centerline (MApc) (P=0.028) and lymph node metastasis (P<0.001) were independent risk factors for DFS. Meanwhile, age (P=0.010), CL (P=0.043), lymph node metastasis (P<0.001), MApc (P=0.022) and the average area perpendicular to the centerline (AApc) (P=0.016) were independently associated with OS. ConclusionFor the postoperative outcomes of NSCLC patients in the LDCT cohort of the NLST, the CL of the pulmonary nodule prediction performance for prognosis is superior to the LD and AD, CL can effectively predict the risk stratification and prognosis of lung cancer, and spheroid tumors have a better prognosis.