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Record W4301186660 · doi:10.1097/rct.0000000000001361

Direct Comparison of Diagnostic Accuracy of Fast Kilovoltage Switching Dual-Energy Computed Tomography and Magnetic Resonance Imaging for Detection of Enhancement in Renal Masses

2022· article· en· W4301186660 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineNuclear medicineMagnetic resonance imagingReceiver operating characteristicDiagnostic accuracyRadiologyRenal cell carcinomaArea under the curvePathology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it