Lung Nodule Enhancement at CT: Multicenter Study
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that absence of statistically significant lung nodule enhancement (< or =15 HU) at computed tomography (CT) is strongly predictive of benignity. MATERIALS AND METHODS: Five hundred fifty lung nodules were studied. Of these, 356 met all entrance criteria and had a diagnosis. On nonenhanced, thin-section CT scans, the nodules were solid, 5-40 mm in diameter, relatively spherical, homogeneous, and without calcification or fat. All patients were examined with 3-mm-collimation CT before and after intravenous injection of contrast material. CT scans through the nodule were obtained at 1, 2, 3, and 4 minutes after the onset of injection. Peak net nodule enhancement and time-attenuation curves were analyzed. Seven centers participated. RESULTS: The prevalence of malignancy was 48% (171 of 356 nodules). Malignant neoplasms enhanced (median, 38.1 HU; range, 14.0-165.3 HU) significantly more than granulomas and benign neoplasms (median, 10.0 HU; range, -20.0 to 96.0 HU; P < .001). With 15 HU as the threshold, the sensitivity was 98% (167 of 171 malignant nodules), the specificity was 58% (107 of 185 benign nodules), and the accuracy was 77% (274 of 356 nodules). CONCLUSION: Absence of significant lung nodule enhancement (< or = 15 HU) at CT is strongly predictive of benignity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".