Detection Sensitivity of a Commercial Lung Nodule CAD System in a Series of Pathologically Proven Lung Cancers
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it