ArticleBritish journal of cancer2023
A clinically practical model for the preoperative prediction of lymph node metastasis in bladder cancer: a multicohort study.
Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Development and validation of MRI-based models to predict lymph node metastasis in bladder cancer: a multi-center study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- A prediction model for urological tumor metastasis using liquid biopsy-derived biomarkers.Frontiers in medicine · 2026Article
- Development and prospective validation of a multitarget urine RNA assay for noninvasive detection of urothelial carcinoma.iScience · 2025Article
- METTL11A serves as a novel therapeutic target for acute myeloid leukemia through regulation of the p38-MAPK pathway.Translational cancer research · 2025Article
- The ETS transcription factor GABPA inhibits bladder cancer aggressiveness by repressing extracellular matrix deposition and mechanotransduction signaling.Cell death & disease · 2025Article
- Evaluation of gene expression-based predictors of lymph node metastasis in bladder cancer.Bladder cancer (Amsterdam, Netherlands)Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to construct a clinically practical model to precisely predict lymph node (LN) metastasis in bladder cancer patients.
methodsFour independent cohorts were included. The least absolute shrinkage and selection operator regression with multivariate logistic regression were applied. The diagnostic efficacy of LN score and CT/MRI was compared by accuracy, sensitivity, specificity, and area under curve (AUC).
resultsA total of 606 patients were included to develop a basic prediction model. After multistep gene selection, the LN metastasis prediction model was constructed with 5 genes. The model can accurately predict LN metastasis with an AUC of 0.781. For clinically practical use, we transformed the model into a Fast LN Scoring System using the SYSMH cohort (n = 105). High LN score patients exhibited a 72.2% LN metastasis rate, while low LN score patients showed a 3.4% LN metastasis rate. The LN score achieved a superior accuracy than CT/MRI (0.882 vs. 0.727). Application of LN score can correct the diagnosis of 88% (22/25) patients who were misdiagnosed by CT/MRI. DISCUSSION: The clinically practical LN score can precisely, rapidly, and conveniently predict LN status, which will assist preoperative diagnosis for LN metastasis and guide precise therapy.
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