Tumour architecture is an independent predictor of outcomes after nephroureterectomy: a multi‐institutional analysis of 1363 patients
Bibliographic record
Abstract
OBJECTIVE: To assess whether tumour architecture can help to refine the prognosis of patients treated with nephroureterectomy (NU) for urothelial carcinoma (UC) of the upper urinary tract (UT), as the prognostic value of tumour architecture (papillary vs sessile) in UTUC remains elusive. PATIENTS AND METHODS: The study included 1363 patients with UTUC and treated with radical NU at 12 centres worldwide. All slides were re-reviewed according to strict criteria by genitourinary pathologists who were unaware of the findings of the original pathology slides and clinical outcomes. Gross tumour architecture was categorized as sessile vs papillary. RESULTS: Papillary growth was identified in 983 patients (72.2%) and sessile growth in 380 (27.8%). The sessile growth pattern was associated with higher tumour grade, more advanced stage, lymphovascular invasion, and metastasis to lymph nodes (all P < 0.001). In multivariable Cox regression analyses that adjusted for the effects of pathological stage, grade and lymph node status, tumour architecture (sessile or papillary) was an independent predictor of cancer recurrence (hazard ratio 1.5, P = 0.002) and cancer-specific mortality (1.6, P = 0.001). Adding tumour architecture increased the predictive accuracy of a model that comprised pathological stage, grade and lymph node status for predicting cancer recurrence and cancer-specific death by a minimal but statistically significant margin (gain in predictive accuracy 1% and 0.5%, both P < 0.001). CONCLUSION: The tumour architecture of UTUC is associated with established features of biologically aggressive disease, and more importantly, with prognosis after radical NU. Including tumour architecture in predictive models for disease progression should be considered, aiming to identify patients who might benefit from early systemic therapeutic intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".