MA02.11 Validation of the Sybil Deep Learning Lung Cancer Risk Prediction Model in Three Independent Screening Studies
Bibliographic record
Abstract
Introduction: A 4-protein biomarker panel (4MP) has been shown to improve estimation of lung cancer risk and identify individuals who may benefit most from lung cancer screening.In the current study, we evaluated the performance of the 4MP for risk determination of lung cancer in the multicenter National Lung Screening Trial (NLST).Methods: The 4MP was assessed in two nested case-control cohorts of plasma samples from the NLST.The first cohort consisted of 675 samples from individuals with CT scans without suspicious findings, including 135 eventually diagnosed with lung cancer and 540 matched control samples.The second cohort consisted of 715 samples from individuals with screen-detected pulmonary nodules, including 143 diagnosed with cancer before the next screening timepoint and 572 matched controls.The 4MP was measured using a multiplex beadbased immunoassay using coefficients fixed from a previously developed logistic regression model.Performance was evaluated using receiver operating characteristic analysis, including computing the area under the curve (AUCs) as well as a net reclassification index (NRI) to estimate how well the 4MP improved existing risk prediction models such as the Brock nodule risk calculator.Results: In the cohort with negative CTs, for those individuals eventually diagnosed with stage II or higher lung cancer, the 4MP showed an AUC of 0.67 (95% CI 0.59-0.74).For all those with advanced (stage III and higher), AUC was 0.71 (95% CI 0.61-0.81).For individuals diagnosed within 1 year of blood draw, the AUC was 0.71 (95% CI 0.51-0.91).In those with indeterminate nodules, the 4MP showed similar performance, with an AUC of 0.64 (95% CI 0.59-0.70) in those eventually diagnosed with stage II or higher lung cancer.The 4MP, added to the Brock model, showed an NRI of 0.26 compared to the Brock model alone.Conclusions: The 4MP may be a useful adjunct to screening, especially in identifying those who will develop more advanced stage disease in the following year.This could help to identify individuals who may benefit from closer clinical follow-up.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".