Direct Comparison of Diagnostic Accuracy of Fast Kilovoltage Switching Dual-Energy Computed Tomography and Magnetic Resonance Imaging for Detection of Enhancement in Renal Masses
Bibliographic record
Abstract
PURPOSE: The aim of the study was to compare diagnostic accuracy of dual-energy computed tomography (DECT) and magnetic resonance imaging (MRI) to detect enhancement in renal masses. METHODS: Adults renal masses of 10 mm or greater with both fast kilovoltage potential switching DECT and contrast-enhanced MRI performed within 12 months were retrospectively included. Two blinded radiologists independently evaluated for enhancement subjectively (5-point Likert scales) and quantitatively (signal intensity ratio ≥15% for MRI, iodine concentration ≥1.2 or ≥2.0 mg/mL for DECT). Per-lesion diagnostic accuracy, with histologic reference standard for solid masses, was expressed as the area under the receiver operator curve (AUC) for each index test. Differences were evaluated for statistical significance using the DeLong test. RESULTS: We included 24 patients with 41 masses: 17 solid renal masses and 24 Bosniak 1 or 2 cysts. There was no significant difference in diagnostic accuracy comparing subjective enhancement by MRI and using iodine overlay DECT for reader 1 (AUC 0.99 vs 0.99, P = 0.38) or reader 2 (AUC 1.00 vs 0.94, P = 0.12) Interobserver agreement was κ = 0.61 for DECT and κ = 0.71 for MRI. There was no significant difference either in accuracy between quantitative assessment using signal intensity ratio or iodine concentration for reader 1 (AUC 0.94 vs 0.94, P = 0.88) or reader 2 (AUC 0.97 vs 0.92, P = 0.16). False-negative results in both subjective and quantitative assessment were nearly exclusively seen in papillary renal cell carcinoma, occurring with both DECT and MRI. CONCLUSIONS: We detected no significant differences in accuracy for detecting enhancement in renal masses comparing MRI and DECT. Our results require further investigation in larger sample sizes, but suggest that DECT may be comparable to MRI for detection of enhancement in renal masses.
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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 | 0.005 | 0.026 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".