Chemical Shift MR Imaging of Hyperattenuating (>10 HU) Adrenal Masses: Does It Still Have a Role?
Bibliographic record
Abstract
PURPOSE: To evaluate chemical shift magnetic resonance (MR) imaging for the characterization of hyperattenuating adrenal masses. MATERIALS AND METHODS: Adrenal MR images obtained from January 1998 to February 2003 were reviewed. Patients were excluded if they did not undergo unenhanced computed tomography or did not have an adrenal mass with attenuation higher than 10 HU, adequate follow-up, or pathologic diagnosis for use as a reference standard. A diagnosis of adenoma required at least 24 weeks of stability on images. Thirty-eight masses in 36 patients were identified (27 adenomas, nine metastases, one adrenocortical oncocytoma, and one pheochromocytoma). Signal intensity (SI) decrease between in-phase and opposed-phase MR images was measured for the entire mass and normalized to the renal parenchymal SI. In 21 of 36 (58%) patients, dual-echo single-breath-hold MR imaging was used to eliminate misregistration. RESULTS: The attenuation of 61% (23 of 38) of all masses and 70% (19 of 27) of adenomas was 10-30 HU. With a threshold of 20% SI decrease, the sensitivity of chemical shift MR imaging for hyperattenuating adenoma was 67% (18 of 27 masses). When considering masses with attenuation of 10-30 HU, the sensitivity for adenoma was 89% (17 of 19 masses) and remained reasonable at 75% (six of eight masses) for adenomas with attenuation of 20-30 HU. Only one adenoma with attenuation higher than 30 HU had SI decrease of more than 20%. Specificity for diagnosis of adenoma was 100% (11 of 11). CONCLUSION: In certain circumstances, chemical shift MR imaging is a reasonable second imaging test for further characterization of a hyperattenuating adrenal mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".