Lung Cancers Diagnosed at Annual CT Screening: Volume Doubling Times
Bibliographic record
Abstract
PURPOSE: To empirically address the distribution of the volume doubling time (VDT) of lung cancers diagnosed in repeat annual rounds of computed tomographic (CT) screening in the International Early Lung Cancer Action Program (I-ELCAP), first and foremost with respect to rates of tumor growth but also in terms of cell types. MATERIALS AND METHODS: All CT screenings in I-ELCAP from 1993 to 2009 were performed according to HIPAA-compliant protocols approved by the institutional review boards of the collaborating institutions. All instances of first diagnosis of primary lung cancer after a negative screening result 7-18 months earlier were identified, with symptom-prompted diagnoses included. Lesion diameter was calculated by using the measured length and width of each cancer at the time when the nodule was first identified for further work-up and at the time of the most recent prior screening, 7-18 months earlier. The length and width were measured a second time for each cancer, and the geometric mean of the two calculated diameters was used to calculate the VDT. The χ(2) statistic was used to compare the VDT distributions. RESULTS: The median VDT for 111 cancers was 98 days (interquartile range, 108). For 56 (50%) cancers it was less than 100 days, and for three (3%) cancers it was more than 400 days. Adenocarcinoma was the most frequent cell type (50%), followed by squamous cell carcinoma (19%), small cell carcinoma (19%), and others (12%). Lung cancers manifesting as subsolid nodules had significantly longer VDTs than those manifesting as solid nodules (P < .0001). CONCLUSION: Lung cancers diagnosed in annual repeat rounds of CT screening, as manifest by the VDT and cell-type distributions, are similar to those diagnosed in the absence of screening.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".