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Record W2164608774 · doi:10.1148/radiol.2015140907

Accuracy of Contrast-enhanced US for Differentiating Benign from Malignant Solid Small Renal Masses

2015· article· en· W2164608774 on OpenAlexaff
Mostafa Atri, Leila Tabatabaeifar, Hyun‐Jung Jang, Anthony Finelli, Hadas Moshonov, Michael A.S. Jewett

Bibliographic record

VenueRadiology · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineVascularityRadiologyMann–Whitney U testContrast (vision)Institutional review boardExact testNuclear medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To test the hypothesis that qualitative and quantitative features of contrast material-enhanced ultrasonography (US) can be used to differentiate benign from malignant small renal masses. MATERIALS AND METHODS: This is an institutional review board approved, HIPAA-compliant prospective study with written informed consent. Patients with histologically characterized solid small renal masses, excluding lipid-rich angiomyolipomas, underwent qualitative contrast-enhanced US with a combination of three different US machines. A subgroup of patients underwent quantitative contrast-enhanced US. Patients received a bolus injection of 0.2 mL of contrast material for qualitative and quantitative evaluations and were followed for 3 minutes. Two radiologists independently reviewed videotaped qualitative contrast-enhanced US examinations and were blinded to the final diagnoses. Features that were evaluated included lesion vascularity relative to the adjacent cortex in the arterial phase, the presence of a capsule, homogeneity, the pattern of vascularity, and washout. One radiologist separately reviewed a subset of contrast-enhanced US examinations that were performed with all three machines. Parameters of a first-pass time intensity curve were calculated for quantitative analysis. The Mann-Whitney test was used for quantitative parameters, the χ(2) or Fisher exact test was used for qualitative parameters, and κ statistics and Fleiss methodology were used to determine interobserver and intermachine agreement. RESULTS: The study population consisted of 91 patients (35 women and 56 men) with 94 lesions. The mean age was 62 years ± 14 (range, 21-91). Three patients had two lesions each, which were evaluated at two different sessions. There were 26 benign small renal masses (including 18 oncocytomas, seven lipid-poor angiomyolipomas, and one hemangioblastoma) and 68 malignant masses (including 41 clear cell, 20 papillary, and seven chromophobe renal cell carcinomas [RCCs[) that were 1.1-4.0 cm in diameter (mean, 2.7 cm ± 0.9). All patients underwent contrast-enhanced US on the same one machine, and 68 patients were imaged on all three machines. Vascularity was present in all lesions (n = 94) at contrast-enhanced US. Lesion hypovascularity relative to the adjacent cortex in the arterial phase was seen in only malignant lesions by both reviewers; reviewer 1 saw hypovascularity in 24 of 94 lesions (P = .0001), and reviewer 2 saw hypovascularity in 21 of 94 lesions (P = .0006), for a specificity of 100% (95% confidence interval [CI]: 84, 100). This feature had κ values of 0.91 (95%CI: 0.82, 1.00) between the two reviewers and 0.85 (95% CI: 0.72, 0.99) between the three machines. Eighteen of 20 papillary RCCs were hypovascular. Quantitative parameters of area under the receiver operating characteristics curve, peak intensity, wash-in slope of 10%-90% and 5%-45%, and washout slope of 100%-10% and 50%-10% were significantly higher in malignant renal masses (P = .018, P = .002, P = .036, P = .016, P = .001, and P = .005, respectively) than in benign lesions. CONCLUSION: Excluding lipid-rich angiomyolipoma, hypovascularity-which has high interobserver and intermachine agreement-of solid small renal masses relative to the cortex in the arterial phase has 100% specificity (95% CI: 84, 100) for detecting malignancy, most often papillary RCC.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.059
GPT teacher head0.301
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations54
Published2015
Admission routes1
Has abstractyes

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