Diagnostic Accuracy of Qualitative and Quantitative Computed Tomography Analysis for Diagnosis of Pathological Grade and Stage in Upper Tract Urothelial Cell Carcinoma
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it