Vancouver Risk Calculator Compared with ACR Lung-RADS in Predicting Malignancy: Analysis of the National Lung Screening Trial
Bibliographic record
Abstract
Purpose To compare the Vancouver risk calculator (VRC) with American College of Radiology (ACR) Lung Imaging Reporting and Data System (Lung-RADS) in predicting the risk of malignancy in the National Lung Screening Trial (NLST). Materials and Methods A total of 2813 patients with 4408 nodules (4078 solid, 330 subsolid) were available from the NLST for evaluation. Nodules were scored by using VRC with nine parameters (output was the percentage likelihood of malignancy; VRC threshold for malignancy likelihood set as greater than 5%) and Lung-RADS (output was category 2-4B; malignancy defined as category 4A or 4B; malignancy likelihood greater than 5%). Lung-RADS and VRC were compared for sensitivity, specificity, and accuracy for malignancy on a per-nodule and per-patient basis. Results Of 4408 total nodules, 100 of 4078 (2.5%) solid nodules were malignant and 10 of 330 (3%) subsolid nodules were malignant. On an overall per-nodule basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 90%, and 90% for VRC and 87%, 83%, and 83% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). On a per-patient basis, the sensitivity, specificity, and accuracy for VRC and Lung-RADS were 93%, 85%, and 85% for VRC and 87%, 76%, and 76% for Lung-RADS, respectively (P = .077, P < .001, and P < .001, respectively). Conclusion The Vancouver risk calculator had superior overall accuracy than the Lung Imaging Reporting and Data System in predicting malignancy in the National Lung Screening Trial for total nodules, as well as on a per-patient basis. © RSNA, 2019 See also the editorial by Black in this issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".