Use of a risk model combining clinical information and CT findings to customize follow-up intervals in lung cancer screening

A. Schreuder, C.M. Schaefer-Prokop, E.T. Scholten, C. Jacobs, M. Prokop and B. van Ginneken

in: Annual Meeting of the Radiological Society of North America, 2017

Abstract

Purpose: The U.S. has launched an annual CT lung cancer screening program, irrespective of individual participants' malignancy risk. We developed a risk model based on information from the baseline CT and clinical information to calculate the trade-off between cost savings by omitting one year follow-up scans in low risk individuals and the number of delayed cancer diagnoses. Method and Materials: We used data from the National Lung Screening Trial. We selected all subjects who underwent a baseline scan and a one year follow up scan, those diagnosed with lung cancer after the baseline scan were excluded. Using baseline clinical data and baseline scan variables, various models were developed to estimate the risk of developing lung cancer after the one year follow-up scan, using backward stepwise regression. The full model included both clinical and scan variables. Additionally we tested a clinical-only model and a nodule-only model, the latter including the largest nodule diameter as the only variable. Furthermore, the published Brock and Patz models were validated on the same data set. Results: 174 of 24,542 participants were diagnosed with lung cancer in the year after the first annual follow up. Best predictors included in the full model were older age, higher smoking duration and intensity, shorter smoking quit time, previous COPD and cancer diagnosis, emphysema, longest and perpendicular diameter of the largest nodule, presence of subsolid nodules, presence of an upper lobe nodule, and presence of a spiculated nodule. Using our full model, 9,972, 16,298, 19,726, and 21,158 of the cancer-free persons could have safely avoided the one year follow-up scan, at the expense of delaying the diagnosis of 17, 44, 70, and 88 of the lung cancer patients, respectively. The area under the ROC curve ranged from 0.79 with our full model to 0.73 with the Brock model to 0.67 in the Patz model. Conclusion: Predictive models based on clinical and baseline scan information can be used to personalize follow up intervals in lung cancer screening, saving radiation and costs. Results differed substantially depending on the risk model used. Clinical Relevance/Application: Our model can be used to improve lung cancer screening efficiency by selecting a substantial proportion of participants for a two year follow-up interval, while delaying lung cancer diagnosis in only very few cases. This can greatly reduce costs, radiation burden and radiologist’s work-load.