Accuracy of the EORTC risk tables and of the CUETO scoring model to predict outcomes in non-muscle-invasive urothelial carcinoma of the bladder
Bibliographic record
Abstract
BACKGROUND: The European Organization for Research and Treatment of Cancer (EORTC) risk tables and the Spanish Urological Club for Oncological Treatment (CUETO) scoring model are the two best-established predictive tools to help decision making for patients with non-muscle-invasive bladder cancer (NMIBC). The aim of the current study was to assess the performance of these predictive tools in a large multicentre cohort of NMIBC patients. METHODS: We performed a retrospective analysis of 4689 patients with NMIBC. To evaluate the discrimination of the models, we created Cox proportional hazard regression models for time to disease recurrence and progression. We incorporated the patients calculated risk score as a predictor into both of these models and then calculated their discrimination (concordance indexes). We compared the concordance index of our models with the concordance index reported for the models. RESULTS: With a median follow-up of 57 months, 2110 patients experienced disease recurrence and 591 patients experienced disease progression. Both tools exhibited a poor discrimination for disease recurrence and progression (0.597 and 0.662, and 0.523 and 0.616, respectively, for the EORTC and CUETO models). The EORTC tables overestimated the risk of disease recurrence and progression in high-risk patients. The discrimination of the EORTC tables was even lower in the subgroup of patients treated with BCG (0.554 and 0.576 for disease recurrence and progression, respectively). Conversely, the discrimination of the CUETO model increased in BCG-treated patients (0.597 and 0.645 for disease recurrence and progression, respectively). However, both models overestimated the risk of disease progression in high-risk patients. CONCLUSION: The EORTC risk tables and the CUETO scoring system exhibit a poor discrimination for both disease recurrence and progression in NMIBC patients. These models overestimated the risk of disease recurrence and progression in high-risk patients. These overestimations remained in BCG-treated patients, especially for the EORTC tables. These results underline the need for improving our current predictive tools. However, our study is limited by its retrospective and multi-institutional design.
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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".