ArticleCancer medicine2023
Predictive models of long-term survival outcomes following radical cystectomy.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 3 citations in OpenAlex.
- MRI-based deep learning combined with radiomics for the preoperative prediction of lymphovascular invasion in patients with bladder cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- An interpretable machine learning model based on CT imaging for predicting lymphovascular invasion and survival in bladder urothelial carcinoma: a multicenter study.BMC medical imaging · 2025Article
- Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.Insights into imaging · 2025Article
- Predictive models of long-term survival outcomes following radical cystectomy.Cancer medicine · 2023Article
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11 authors at 3 institutions in 1 country.
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Abstract
backgroundIdentifying the likelihood of life-threatening recurrence after radical cystectomy by reliable and user-friendly predictive models remains an unmet need in the clinical management of invasive bladder cancer.
methodsA total of 204 consecutive patients undergoing open radical cystectomy (ORC) for bladder cancer were retrospectively enrolled between May 2005 and August 2020. Clinicopathological and peri-ORC therapeutic data were extracted from clinical records. We explored predictive factors that significantly affected the primary endpoint of overall survival (OS) and secondary endpoints of cancer-specific survival (CSS) and recurrence-free survival (RFS).
resultsDuring a median follow-up of 3.9 years, 42 (20.6%) and 10 (4.9%) patients died due to bladder cancer and other causes, respectively. Five-year RFS, CSS, and OS were 66.5%, 77.6%, and 75.4%, respectively. Pathological T and N categories and lymphovascular invasion (LVI) significantly affected RFS by Cox regression analysis. Accordingly, clinical T and pathological N categories and LVI significantly affected CSS. Clinical T and pathological N categories, LVI, age, and ORC tumor grade significantly affected OS. Based on the assessment score for each independent risk factor, we developed the Gunma University Oncology Study Group (GUOSG) score, which predicts RFS, CSS, and OS. The GUOSG score classified four groups for RFS, three for CSS, and five for OS, with statistically significant distribution for nearly all comparisons.
conclusionsThe GUOSG model is helpful to show individualized prognosis and functions as a risk-stratified historical cohort for assessing the lifelong efficacy of new salvage treatment regimens.
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