Evaluation of the accuracy of the PLCO<sub>m2012</sub>6-year lung cancer risk prediction model among smokers in the CARTaGENE population-based cohort
Bibliographic record
Abstract
<h3>Background:</h3> The PLCO<sub>m2012</sub> prediction tool for risk of lung cancer has been proposed for a pilot program for lung cancer screening in Quebec, but has not been validated in this population. We sought to validate PLCO<sub>m2012</sub> in a cohort of Quebec residents, and to determine the hypothetical performance of different screening strategies. <h3>Methods:</h3> We included smokers without a history of lung cancer from the population-based CARTaGENE cohort. To assess PLCO<sub>m2012</sub> calibration and discrimination, we determined the ratio of expected to observed number of cases, as well as the sensitivity, specificity and positive predictive values of different risk thresholds. To assess the performance of screening strategies if applied between Jan. 1, 1998, and Dec. 31, 2015, we tested different thresholds of the PLCO<sub>m2012</sub> detection of lung cancer over 6 years (1.51%, 1.70% and 2.00%), the criteria of Quebec’s pilot program (for people aged 55–74 yr and 50–74 yr) and recommendations from 2021 United States and 2016 Canada guidelines. We assessed shift and serial scenarios of screening, whereby eligibility was assessed annually or every 6 years, respectively. <h3>Results:</h3> Among 11 652 participants, 176 (1.51%) lung cancers were diagnosed in 6 years. The PLCO<sub>m2012</sub> tool underestimated the number of cases (expected-to-observed ratio 0.68, 95% confidence interval [CI] 0.59–0.79), but the discrimination was good (C-statistic 0.727, 95% CI 0.679–0.770). From a threshold of 1.51% to 2.00%, sensitivities ranged from 52.3% (95% CI 44.6%–59.8%) to 44.9% (95% CI 37.4%–52.6%), specificities ranged from 81.6% (95% CI 80.8%–82.3%) to 87.7% (95% CI 87.0%–88.3%) and positive predictive values ranged from 4.2% (95% CI 3.4%–5.1%) to 5.3% (95% CI 4.2%–6.5%). Overall, 8938 participants had sufficient data to test performance of screening strategies. If eligibility was estimated annually, Quebec pilot criteria would have detected fewer cancers than PLCO<sub>m2012</sub> at a 2.00% threshold (48.3% v. 50.2%) for a similar number of scans per detected cancer. If eligibility was estimated every 6 years, up to 26 fewer lung cancers would have been detected; however, this scenario led to higher positive predictive values (highest for PLCO<sub>m2012</sub> with a 2.00% threshold at 6.0%, 95% CI 4.8%–7.3%). <h3>Interpretation:</h3> In a cohort of Quebec smokers, the PLCO<sub>m2012</sub> risk prediction tool had good discrimination in detecting lung cancer, but it may be helpful to adjust the intercept to improve calibration. The implementation of risk prediction models in some of the provinces of Canada should be done with caution.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".