Radiographic predictors of muscle-invasive upper tract urothelial cancer
Bibliographic record
Abstract
INTRODUCTION: Accurate diagnostic staging of upper tract urothelial cancer (UTUC) is challenging. Endoscopic staging is limited by its ability to provide adequate sampling of deeper layers of the ureter and renal pelvis. Further ability to accurately predict invasive disease would aid in better selecting the appropriate treatment for patients. We aimed to analyze the ability of preoperative cross-sectional radiologic findings to predict pathologic outcomes, including tumor grade, muscle-invasive disease, and presence of lymphovascular invasion (LVI). METHODS: All patients diagnosed with localized UTUC (cN0M0) who underwent nephroureterectomy between February 2012 and December 2018 in Manitoba, Canada, were identified. Preoperative radiologic characteristics, including the presence and severity of hydronephrosis, as well as tumor location, were recorded. Patients' and pathologic characteristics were also recorded. Logistic regression analysis was used to assess the association between radiologic variables and pathologic outcomes at radical surgery. RESULTS: A total of 112 pathology reports of patients with UTUC were obtained. The median age was 70 years (range 50-87), and 58.8% of patients were men. On univariate analysis, ureteric location on computed tomography (odds ratio [OR] 2.240, 95% confidence interval [CI] 1.049-4.783, p=0.037) and presence of hydronephrosis (OR 2.455, 95% CI 1.094-5.506, p=0.0029) were each independently associated with locally invasive disease (>pT2). No radiologic variables were found to be predictors of adverse pathology on multivariable analysis. Only the presence of hydronephrosis was associated with high-grade disease on univariate analysis (OR 2.533, 95% CI 1.083-5.931, p=0.032). CONCLUSIONS: Our findings suggest a limited role for cross-sectional imaging in predicting the presence of high-grade disease, LVI, or locally advanced disease in UTUC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".