International validation of the prognostic value of lymphovascular invasion in patients treated with radical cystectomy
Bibliographic record
Abstract
OBJECTIVE: To externally validate the prognostic value of lymphovascular invasion (LVI) in a large international cohort of patients treated with radical cystectomy (RC) for urothelial carcinoma of the bladder (UCB). PATIENTS AND METHODS: We collected data from 4257 patients treated with RC and pelvic lymphadenectomy for UCB, without neoadjuvant chemotherapy, at 12 centres. LVI was defined as presence of nests of tumour cells within an endothelium-lined space. RESULTS: LVI was detected in 1407 patients (33.1%); the proportion of LVI increased with advancing stage, higher grade, soft-tissue surgical margin involvement, and lymph node metastasis (P < 0.001 for all). In standard multivariate models, LVI was associated with both disease recurrence (hazard ratio 1.43, P < 0.001) and cancer-specific mortality (1.45, P < 0.001). In the entire cohort, adding LVI to a base model that included standard features improved only minimally its predictive accuracy for both recurrence and cancer-specific mortality (by 1.1% and 1.2%, respectively). In 3122 patients with negative lymph nodes, LVI remained independently associated with and improved the predictive accuracy of the standard predictors for recurrence (hazard ratio 1.68, P < 0.001; +2.3%) and cancer-specific mortality (1.70, P < 0.001; +2.4%). By contrast, in 1071 node-positive patients, LVI only marginally improved the prediction of cancer-specific recurrence (hazard ratio 1.20, P < 0.001; +0.2%) and survival (1.23, P < 0.001; +0.5%). CONCLUSIONS: LVI is strongly associated with clinical outcome in node-negative patients treated with RC. The assessment of LVI might help to identify patients who could benefit from adjuvant therapy after RC. After confirmation in different populations, LVI should be included in the staging of UCB.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".