Diagnostic Accuracy of Qualitative and Quantitative Computed Tomography Analysis for Diagnosis of Pathological Grade and Stage in Upper Tract Urothelial Cell Carcinoma
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".