Lung-RADS versus the McWilliams nodule malignancy score for risk prediction: an evaluation using lesions from the DLCST Trial

PURPOSE: Lung-RADS published in 2014 by the American College of Radiology is based on results of the literature and expert opinion and uses nodule type, size, and growth to recommend nodule management adjusted to malignancy risk. The McWilliams model published in 2013 is based on baseline screen detected nodules from the Canadian screening trial and provides a nodule malignancy probability based on nodule size, type, morphology and subject characteristics. We compare the performance of both approaches on an independent data set. METHODS: We selected 60 cancers from the Danish Lung Cancer Screening Trial as presented in the first scan they were visible, and randomly added 120 benign nodules from baseline scans, all from different participants. Data had been acquired using a low-dose (16x0.75mm, 120kVp, 40mAs) protocol, and 1mm section thickness reconstruction. For each nodule, the malignancy probability was calculated using McWilliams model 2b. Parameters were available from the screening database or scored by an expert radiologist. For the McWilliams model completely calcified nodules and perifissural nodules were assigned a malignancy probability of 0, in accordance with the McWilliams model. All nodules were categorized into their Lung-RADS category based on nodule type and diameter. Perifissural nodules were treated as solid nodules, in accordance with Lung-RADS guidelines. For each Lung-RADS category cut-off sensitivity and specificity were calculated. Corresponding sensitivities and specificities using the McWilliams model were determined. RESULTS: McWilliams performed superiorly to Lung-RADS in selecting malignant nodules for more aggressive follow-up. Defining Lung-RADS category 2/3/4A/4B and higher as a positive screening result, specificities to exclude lung malignancy were 21%/65%/86%/99% and vice versa sensitivities to predict malignancy were 100%/85%/58%/32%. At the same sensitivity levels, McWilliams model yielded overall higher specificities with 2%/86%/98%/100%, respectively. Similarly, at the same specificities McWilliamsAC/a,!a,,C/s model achieved higher sensitivities with 100%/95%/85%/48%, respectively. CONCLUSION: For every cut-off level of Lung-RADS, the McWilliams model yields superior specificity to reduce unnecessary work-up for benign nodules, and higher sensitivity to predict malignancy. The McWilliams model seems to be a better tool than Lung-RADS to provide a malignancy risk, thus reducing unnecessary work-up and helping radiologists determine which subgroup of nodules detected in a screening setting need more invasive work-up.