Performance of the Vancouver Risk Calculator Compared with Lung-RADS in an Urban, Diverse Clinical Lung Cancer Screening Cohort
Bibliographic record
Abstract
Purpose To compare the performance of the Vancouver risk calculator (VRC) with the American College of Radiology’s Lung CT Screening Reporting and Data System (Lung-RADS) for a lung cancer screening cohort in an urban, diverse clinical setting. Materials and Methods This study included a total of 486 patients with lung nodules (63 years ± 5.2 [standard deviation], 261 female patients), 448 of whom had lung nodules that were subsequently classified as benign and 38 of whom had those that were classified as malignant. The mean follow-up time was 40.0 months ± 14. Institutional review board approval was obtained for this Health Insurance Portability and Accountability Act–compliant retrospective study, and a waiver of informed consent was received. All patients undergoing lung cancer screening who underwent an initial baseline screening CT between December 2012 and June 2016 that demonstrated a nodule and had at least 1 year of follow-up comprised the study population. Each examination was assigned a Lung-RADS score between 2 and 4B, with 4A and 4B considered as showing positive results. The VRC calculates the risk of cancer at different thresholds using nine variables related to patient and imaging characteristics. Analysis was performed per patient based on the largest nodule. Lung-RADS and VRC using the 5% threshold were compared to assess diagnostic performance in determining the risk of developing lung cancer in a patient with a nodule found at screening CT. The McNemar test was used to compare differences in performance between Lung-RADS and VRC. Results Lung-RADS resulted in nine false-positive and 16 false-negative findings, whereas VRC with a 5% threshold resulted in 29 false-positive and 10 false-negative findings. Overall sensitivity and specificity for Lung-RADS was 58.0% and 98.0%, and for VRC with a 5% threshold was 73.7% and 93.5%, respectively (P = .313, P < .001, respectively). Conclusion The VRC performs well in an urban, diverse lung cancer screening program. Further studies may be directed at determining whether its use in conjunction with Lung-RADS leads to improved lung cancer detection. Keywords: CT, Lung, Thorax © RSNA, 2020
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".