Utility of material-specific fat images derived from rapid-kVp-switch dual-energy renal mass CT for diagnosis of renal angiomyolipoma
Bibliographic record
Abstract
Background Renal angiomyolipoma (AML) are benign masses that require detection of macroscopic fat for accurate diagnosis. Purpose To evaluate fat material-specific images derived from dual-energy computed tomography (DECT) to diagnose renal AML. Material and Methods This retrospective case-control study evaluated 25 renal AML and 44 solid renal masses (41 renal cell carcinomas, three other tumors) imaged with rapid-kVp-switch DECT (120 kVp non-contrast-enhanced [NECT], 70-keV corticomedullary [CM], and 120-kVp nephrographic [NG]-phase CECT) during 2017–2018. A radiologist measured attenuation (Hounsfield Units [HU]) on NECT, CM-CECT, NG-CECT, and fat concentration (mg/mL) using fat-water base-pair images. Results At NECT, 100% (44/44) non-AML and 4.0% (1/25) AML measured >–15 HU. At CM-CECT and NG-CECT, 24.0% (6/25) and 20.0% (5/25) AML measured >–15 HU (size 6–20 mm). To diagnose AML, area under receiver operating characteristic curve (AUC) using –15 HU was: 0.98 (95% confidence interval [CI] 0.98–1.00) NECT, 0.88 (95% CI 0.79–0.91) CM-CECT, and 0.90 (95% CI 0.82–0.98) NG-CECT. At DECT, fat concentration was higher in AML (163.7 ± 333.9 [–553.0 to 723.5] vs. –2858.1 ± 460.3 [–2421.2 to –206.0] mg/mL, P<0.001). AUC to diagnose AML using ≥–206.0 mg/mL threshold was 0.98 (95% CI 0.95–1.0) with sensitivity/specificity of 92.0%/96.7%. Of AML, 8.0% (2/25) were incorrectly classified; one of these was fat-poor. AUC was higher for fat concentration compared to HU measurements on CM-CECT and NG-CECT ( P=0.009–0.050) and similar to NECT ( P=0.98). Conclusion DECT material-specific fat images can help confirm the presence of macroscopic fat in renal AML which may be useful to establish a diagnosis if unenhanced CT is unavailable.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".