Evidence map›Paper›PMID 42866795›Full record

ArticleTranslational andrology and urology2026

An interpretable LASSO-Boruta nomogram for non-muscle-invasive bladder cancer recurrence after intravesical instillation.

Bingjie Li, Guangyun Ji, Wenting Wang, Lina Guo

Abstract read
In one paragraph

Article in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Bingjie LiDepartment of Urology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Guangyun JiDepartment of Urology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Wenting WangDepartment of Urology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Lina GuoDepartment of Urology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recurrence remains a major clinical challenge in non-muscle-invasive bladder cancer (NMIBC), highlighting the need for interpretable tools for individualized risk assessment. This study aimed to develop a transparent model that estimates recurrence risk in patients with NMIBC after transurethral resection of bladder tumor (TURBT) and intravesical instillation. Methods: We reviewed 200 patients with NMIBC who received TURBT and postoperative intravesical instillation at the Department of Urology, The Affiliated Hospital of Xuzhou Medical University, from January to December 2020. Recurrence status was determined from follow-up records. We collected baseline variables, preoperative laboratory results, and clinicopathological data. Patients were divided into training and validation sets (7:3). We screened predictors with multivariate Cox regression, least absolute shrinkage and selection operator (LASSO)-Cox regression, and Boruta analysis before building the nomogram. Model performance was checked with receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Shapley additive explanations (SHAP) analysis was used for interpretation, and an R Shiny application was built. Results: Of the 200 patients, recurrence was observed in 90 cases. The final identified predictors were smoking, hypertension, hemoglobin (Hb), and serum Na. The nomogram showed stable performance, with time-dependent areas under the curves (AUCs) of 0.862, 0.918, and 0.948 in the training set and 0.725, 0.870, and 0.919 in the validation set at 12, 36, and 60 months, respectively. SHAP analysis ranked Hb as the main contributor; higher Hb and serum Na indicated lower recurrence risk, while smoking and hypertension indicated higher risk. Conclusions: This model provided internally validated recurrence-risk estimates; external validation and usability testing are required before clinical use.

Indexed as

interpretable prediction modelIntravesical instillationnon-muscle-invasive bladder cancer (NMIBC)recurrence

Identifiers

PMID42866795
PMCPMC13657710

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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 Socratic graph.