Computed Tomographic Histogram Analysis in the Diagnosis of Lipid-Poor Adenomas
Bibliographic record
Abstract
OBJECTIVE: To evaluate the ability of computed tomographic histogram analysis to diagnose lipid poor adenoma in comparison with adrenal washout computed tomography (CT). MATERIALS AND METHODS: Adrenal CT washout examinations performed during a period from January 2000 to July 2005 were reviewed. Computed tomographic histogram analysis was performed on the unenhanced component of the study, and sensitivity was assessed at thresholds of more than 5% and 10% negative pixels. Liver and spleen were used to represent the control/nonadenoma group. Computed tomographic noise was measured recording standard deviation (SD) of mean CT attenuation in adrenal, liver, and spleen. RESULTS: Twenty-four lipid-poor adenomas included exhibited more than 60% absolute enhancement washout (range, 60%-79%, mean, 69%) and remained stable for a period greater than 6 months. At threshold of more than 5% or 10% negative pixels CT histogram analysis yielded sensitivities of 91.6% and 70.8%, respectively, with 100% specificity. The mean SDs of adrenal, liver, and spleen were 18.2, 16.4 and 15, respectively. These differences in the mean SD were much smaller compared with the differences in the percentage of negative pixels in adrenal, liver, and spleen of 12.75%, 0.75%, and 0.25%, respectively. CONCLUSIONS: Computed tomographic histogram analysis has good potential in the diagnosis of lipid-poor adenoma and can reduce the need to perform adrenal washout CT.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".