Predictive models for improved prognostication and selection of neoadjuvant and adjuvant systemic chemotherapy in upper tract urothelial cell carcinoma.
Bibliographic record
Abstract
456 Background: Chemotherapy is still underutilized in management of upper tract urothelial carcinoma (UTUC). We created pre and post-operative predictive tools combining independent prognostics to guide selection of patients for neoadjuvant and adjuvant chemotherapy. Methods: From the UTUC collaboration database (1,453 patients who underwent radical nephroureterectomy [RNU] at 13 academic institutions); a preoperative predictive model was created using 659 patients in whom all preoperative prognostic variables were available and a post-operative model was created using 586 patients with non-metastatic/ high-grade UTU. After multivariable survival analyses, a backward step-down selection process was applied to create a preoperative nomogram. Internal validation was performed using 200 bootstrap resamples. For the postoperative model, TALL score was created based on the sum of the independent prognostic variables. Results: Preoperative model: Grade, architecture and location of the tumor were independently associated with nonorgan confined disease. A nomogram including these 3 variables achieved 76.6% accuracy in predicting nonorgan confined upper tract urothelial cancer. Postoperative model: TALL score (1-7) was the sum of T ( ≤ T1 = 1, T2 = 2, T3 = 3 and T4 = 4), A (papillary = 0 and sessile = 1), LVI (absent = 0 and present = 1) and L (lymphadenectomy = 0 and no lymphadenectomy = 1). Five-year disease-free survival (DFS) and cancer-specific survival (CSS) were stratified into four risk categories according to the TALL score: low (TALL 0-2; 86 % DFS and 90 % CSS), intermediate (TALL = 3; 71 % DFS and 75 % CSS), high (TALL = 4; 57 % DFS and 58 % CSS) and very high risk (TALL ≥ 5; 34 % DFS and 38 % CSS) using Kaplan-Meier survival analyses. TALL score was externally validated in a single-center cohort of 85 UTUC patients. Conclusions: We developed validated multivariable prognostic tools for prediction of locally advanced UTUC and oncological outcomes after RNU for UTUC. These prediction models can be used for patient counseling, selection for neoadjuvant/adjuvant systemic therapies and design of clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".