ArticleBJUI compass2026
DaRenCa risk score: A prognostic model for recurrence in clear cell renal cell carcinoma.
Article in BJUI compass, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to investigate whether a machine-learning model improves the assessment of postsurgical recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC) compared with a Cox proportional hazards (CPH) approach. Patients and Methods: Patients undergoing curative surgery for non-metastatic ccRCC between 2010 and 2018 were identified from the DaRenCa Study-3, a nationwide register-based cohort study. Three recurrence prediction models were developed: an extreme gradient boosting (XGBoost) model, a feature-matched CPH model and a pathology-based CPH model. The data set was divided into training and test cohorts. Missing data were addressed using multiple imputation for the CPH models, whereas XGBoost handled missing values inherently. Model performance was evaluated using the concordance index (C-index) with 1000 bootstrap resamples. The XGBoost model was also compared with the Leibovich nomogram. Results: Among 2782 patients, with a median follow-up of 7.3 years, 13.7% developed a recurrence. In the test cohort, the XGBoost model showed higher discrimination than both CPH models. Compared with the best performing pathology-based CPH model, XGBoost demonstrated a paired bootstrap difference in Uno's C-index of 0.022 (95% CI 0.005-0.038). The model also identified a subgroup of patients with a very low risk of recurrence (<3% after 10 years) and demonstrated improved clinical risk stratification, with clearer separation between risk groups, higher hazard ratios between groups and larger differences in 5-year recurrence-free survival compared with established models. This improved risk stratification could reduce follow-up imaging by approximately 11% compared with current EAU guideline recommendations. Limitations include the retrospective design and lack of external validation. Conclusion: The XGBoost model provided improved prediction of recurrence compared with CPH models and the Leibovich nomogram, supporting more precise risk stratification. With external validation, this approach may help reduce unnecessary imaging after surgery.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.