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F. Ciompi
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ORAL 09 - CT Screening - New Data and Risk Assessment (ID 95)
- Event: WCLC 2015
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:J. Mulshine, J.K. Field
- Coordinates: 9/07/2015, 10:45 - 12:15, Mile High Ballroom 2a-3b
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ORAL09.05 - Lung-RADS versus the McWilliams Nodule Malignancy Score for Risk Prediction: Evaluation on the Danish Lung Cancer Screening Trial (ID 356)
11:28 - 11:39 | Author(s): F. Ciompi
- Abstract
Background:
Lung-RADS published in 2014 by the American College of Radiology is based on literature review and expert opinion and uses nodule type, size, and growth to recommend nodule management adjusted to malignancy risk. The McWilliams model (N Engl J Med 2013;369:910-9) is a multivariate logistic regression model derived from the Pan-Canadian Early Detection of Lung Cancer Study 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. Completely calcified nodules and perifissural nodules were assigned a malignancy probability of 0, in accordance with model guidelines. 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:
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 as Lung-RADS, McWilliams model yielded overall higher specificities with 2%/86%/98%/100%, respectively (red arrows in Figure 1). Similarly, at the same specificities McWilliams’s model achieved higher sensitivities with 100%/95%/85%/48%, respectively (green arrows in Figure 1). Figure 1
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.
