Performance of Lung Nodule Management Algorithms for Lung-RADS Category 4 Lesions

Purpose: To test the performance of the American College of Chest Physicians (ACCP) and British Thoracic Society (BTS) algorithms to stratify high-risk nodules identified at lung cancer screening. Method and Materials: Patients with Lung-RADS category 4 nodules identified on lung cancer screening computed tomography (CT) between March 2014 and August 2018 were identified, and a subset of 150 were randomly selected. Nodule characteristics and, if available, fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET)-CT scan were recorded. Radiologists blinded to final diagnosis and downstream testing performed five-point visual assessment score for probability of nodule malignancy; their accuracies are averaged below. Probabilities of malignancy according to Brock and Herder models were calculated. ACCP and BTS algorithms were applied to the nodules. Results: Final diagnosis of malignancy was made in 65/150 (43%) of patients. The sensitivity, specificity and accuracy for nodule malignancy were: radiologist visual score (92%, 85%, 88%); BTS (76%, 91%, 85%); ACCP (63%, 89%, 78%); and Brock calculator (77%, 71%, 73%). The sensitivity, specificity, and accuracy for nodule malignancy in patients with FDG PET-CT scan (n = 78) were: FDG uptake (91%, 64%, 83%); Herder probability (91%, 68%, 83%); radiologist visual score (93%, 69%, 86%); BTS (84%, 64%, 78%); Brock probability (82%, 50%, 72%); and ACCP (68%, 59%, 65%). Conclusion: Thoracic radiologist visual analysis yielded the greatest accuracy for nodule triage in the entire cohort. BTS performed better than ACCP guidelines and both performed better than the Brock model alone.