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Record W2759289569 · doi:10.1097/rct.0000000000000664

Diagnostic Accuracy of Qualitative and Quantitative Computed Tomography Analysis for Diagnosis of Pathological Grade and Stage in Upper Tract Urothelial Cell Carcinoma

2017· article· en· W2759289569 on OpenAlexaff
Suraj Mammen, Satheesh Krishna, Matthew Quon, Wael Shabana, Shaheed W. Hakim, Trevor A. Flood, Nicola Schieda

Bibliographic record

VenueJournal of Computer Assisted Tomography · 2017
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineReceiver operating characteristicRadiologyConfidence intervalHydronephrosisStage (stratigraphy)CarcinomaNuclear medicineInstitutional review boardPathologySurgeryInternal medicineUrinary system

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to compare grade and stage of upper tract urothelial cell carcinoma (UCC) using computed tomography. MATERIALS AND METHODS: With institutional review board approval, 48 patients with 49 UCC (44 high grade and 5 low grade, 26 ≤ T1 and 23 ≥ T2) underwent nephroureterectomy and preoperative computed tomography between 2013 and 2015. Two blinded radiologists assessed for tumor appearance (filling defect/mass or wall thickening/stricture), margin (smooth or spiculated/irregular), texture (homogeneous, heterogeneous), hydronephrosis, and calcification. A third blinded radiologist established consensus. A fourth blinded radiologist measured size and first-order histogram texture features. Comparisons were performed using χ test, multivariable logistic regression, and receiver operator characteristic analysis. RESULTS: There was no difference in size of tumors compared by grade or stage (P = 0.80 and 0.13, respectively).Among subjective variables, only tumor texture was significantly different between low- and high-grade UCC (P = 0.03; κ = 0.45). Tumors characterized as spiculated/irregular margin (P = 0.003; 0.30) and heterogeneous (P < 0.001; κ = 0.45) were associated with T2 disease or higher.Entropy was greater in higher grade (6.23 ± 0.46 vs 5.72 ± 0.28) and T2 disease or higher (6.40 ± 0.33 vs 5.95 ± 0.48), (P = 0.03 and 0.02, respectively) with no differences in Kurtosis or Skewness (P > 0.05). Area under the receiver operator characteristic curve for entropy to diagnose high-grade and T2 tumors or higher was 0.83 (confidence interval, 0.64-1.0) and 0.79 (confidence interval 0.59-0.98), respectively. CONCLUSIONS: Heterogeneity, assessed qualitatively and quantitatively, is accurate for diagnosis of higher grade and stage of disease in upper tract UCC. Spiculated/irregular margins are also associated with T2 disease or higher.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.069
GPT teacher head0.369
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2017
Admission routes1
Has abstractyes

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