Detection Sensitivity of a Commercial Lung Nodule CAD System in a Series of Pathologically Proven Lung Cancers
Bibliographic record
Abstract
PURPOSE: To evaluate the performance of a commercially available computer-aided detection (CAD) system in a series of pathologically proven lung cancers. MATERIALS AND METHODS: Sixty-nine chest computed tomography (CT) scans obtained in 12 subjects (8 females, 4 males, age 51 to 75 y, mean 63 y) with 15 pathologically proven lung cancers were retrospectively selected from 2156 entry and follow-up CT scans from a lung cancer screening program. CT scans were retrospectively analyzed using a commercially available CAD system for detecting lung nodules. RESULTS: When first detectable proven lung cancer nodules ranged in maximum diameter from 3 to 38 mm (10.4+/-9.2 mm) with CAD detection sensitivity stratified by size: 0/2 (0%) < or =3 mm, 5/8 (62.5%) 4 to 10 mm, 2/3 (66.7%) 11 to 15 mm, 0/0 16 to 20 mm, 2/2 (100%) >20 mm, and overall sensitivity 9/15 (60%). The sensitivity for all CT scans (first detectable and follow-up), stratified by nodule size as above, was, respectively, 0/2, 18/25, 24/28, 6/9, 5/5, and overall 53/69 (76.8%). Excluding nodules <4 mm and pure ground-glass nodules, the sensitivity for all CT scans by size was 18/24 (75%) 4 to 10 mm, 21/22 (95.4%) 11 to 15 mm, 6/6 (100%) 16 to 20 mm, 5/5 (100%) >20 mm, and overall 50/57 (87.7%). At resection (13) or biopsy (2) nodules were: adenocarcinoma (10), squamous cell carcinoma (3), and small cell carcinoma (2). CONCLUSIONS: The CAD system showed good sensitivity for solid and semisolid cancers > or =4 mm (sensitivity 87.7%) and excellent for those > or =11 mm (sensitivity >95.4%).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".