Lung Nodule Enhancement at CT: Multicenter Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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) | 0.009 | 0.001 |
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