Introduction: Despite the reduction in mortality shown by Low dose computer tomography (LDCT) lung cancer screening, the uptake is still low. Patients undergo chest imaging for several other medical reasons, and this is a unique opportunity to detect lung nodules.
Methods: In a cohort of NSCLC patients from the Surveillance, Epidemiology and End Results (SEER)-Medicare linked data, tumor size at previous imaging was calculated as: VDT = [(T2-T1)·ln2]/ln(V2/V1), solving for diameter of V1. V1 and V2 are tumor volume at times T1 (previous imaging) and T2 (diagnostic procedure) according to three different growth models. 10-year lung cancer-specific mortality was calculated as: lung cancer survival rate = (-0.0098 × maximum tumor diameter) + 1.
Results: 1,007 patients who had a chest imaging performed up to one year prior to lung cancer diagnosis. The median size of the tumor at diagnosis was 25 mm, the predicted median tumor size at previous imaging was 12.16 mm, 17.3 mm, and 20.42 mm under the fast, medium and slow growth model. Under the fast growth model, a detection of the nodule at previous imaging would have yield a decrease in mortality of 7.79%; the corresponding values for the medium growth model is 4.5%, for the slow growth model 2.45%.
Conclusions: Identifying malignant lung nodules in imaging performed for other clinical reasons can help decreasing the burden of NSCLC, especially for non-LDCT eligible patients and the medically vulnerable. We show here that clinical benefits, especially among patients with aggressive disease, can be considerable.
Keywords: early detection; lung cancer screening; nodule modeling.
Copyright © 2024 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.