Applicability of the EORTC risk tables to predict outcomes in  non-muscle-invasive bladder cancer in Turkish patients

Kılınç, Muhammet Fatih; Bayar, Göksel; Dalkılıç, Ayhan; Sönmez, Nurettin Cem; Arısan, Serdar; Güney, Soner

doi:10.5152/tud.2016.77603

Cited by 8 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EORTC-GUCG risk system is one of the commonly used models to evaluate the prognosis of patients, with a high predictive efficiency. 30 In this study, pathological factors as tumor size, grade and T stage were the main risk factors affecting EORTC-GUCG score in patients with bladder cancer, which was consistent with the conclusions of Busato, 31 Cerbone 32 and other studies. Following patient stratification through the EORTC-GUCG risk scoring system, the results revealed statistically significant differences in RFS outcomes (p<0.05) among the different groups, suggesting the accuracy of our patient datasets.…”

Section: Discussionsupporting

confidence: 91%

Development of a New Recurrence-Free Survival Prediction Nomogram for Patients with Primary Non-Muscle-Invasive Bladder Cancer Based on Preoperative Controlling Nutritional Status Score

Zhao¹,

Sun²,

Wang³

et al. 2021

CMAR

View full text Add to dashboard Cite

Background Bladder cancer is the second most prevalent neoplasm in the urogenital system in terms of morbidity and mortality, and there is an urgent need for a more accurate assessment of individual prognosis in patients with primary non-muscle-invasive bladder cancer (NMIBC). The Controlling Nutritional Status (CONUT) score is an emerging biomarker score which has been confirmed to have prognostic value in various malignant tumors. The study attempted to systematically identify the prognostic role of preoperative CONUT score on posttreatment recurrence-free survival (RFS) in patients with NMIBC, and determine the predictive value and feasibility of the new prognostic prediction model. Methods A total of 94 patients with NMIBC were analyzed retrospectively between January 2011 and December 2015. Statistical analysis was conducted using the nonparametric method. The Kaplan-–Meier method was used to assess recurrence-free survival (RFS), and Log rank tests was used to analyze the equivalences of survival curves. We used univariate and multivariate Cox proportional hazards regression model to identify important predictors of RFS. Discrimination of nomogram was measured by the concordance index. Predictive accuracy of the model was evaluated using the internal validation. Results In univariate analysis, age, history of smoking, pathological T stage, tumor grade, tumor size, and CONUT score were significantly correlated with RFS. Multivariate analysis indicated that CONUT score (HR =3.855, 95% CI 1.242–11.970, p=0.020) was an independent predictor of RFS in patients with NMIBC. Based on significant parameters in multivariate analysis and reliable recurrence predictors determined in predictive models and relevant guidelines, a new age-, history of smoking-, pathologic factors- and the CONUT score-based scoring model was developed to predict recurrence of NMBIC. In addition, we internally validated the nomogram using the consistency index and calibration plots, which demonstrated that the model has high prediction accuracy (c-index= 0.851). Conclusion The development of a new nomogram based on CONUT score could increase the accuracy of recurrence prediction and improve individualized treatment plans for patients with NMIBC.

show abstract

Section: Discussionsupporting

confidence: 91%

Development of a New Recurrence-Free Survival Prediction Nomogram for Patients with Primary Non-Muscle-Invasive Bladder Cancer Based on Preoperative Controlling Nutritional Status Score

Zhao¹,

Sun²,

Wang³

et al. 2021

CMAR

View full text Add to dashboard Cite

show abstract

“…When assessing the discrimination capability of predictive models, an AUC or c-index of less than 0.70 reflects poor performance [ 36 ]. In the present study, we found that Choi et al [ 14 ] reported an AUC of 0.894 for the CUETO scoring model and Killinç et al [ 21 ] reported a c-index of 0.817 for the EORTC scoring model for recurrence outcome. However, in most of the external validations included for these predictive models (13 studies) [ 5, 16, 17, 20, 21, 23, 25–27, 31–34 ], we found a poor discrimination capability (AUC or c-index under 0.7).…”

Section: Discussionsupporting

confidence: 57%

Predicting Recurrence and Progression in Patients with Non-Muscle-Invasive Bladder Cancer: Systematic Review on the Performance of Risk Stratification Models

et al. 2022

View full text Add to dashboard Cite

BACKGROUND: Several classifications have been reported to stratify non-muscle-invasive bladder cancer (NMIBC) in risk groups according to the probability of recurrence and progression. OBJECTIVE: To systematically review the current evidence regarding risk stratification of NMIBC. METHODS: The systematic review was performed in accordance with the PRISMA statement. Studies providing data on development and/or external validation cohorts of models and risk stratification tables for recurrence and/or progression for patients with NMIBC, reporting at least one discrimination measure (AUC or C-Index) were included. RESULTS: Twenty-five studies involving 22,737 patients were included. Six classifications were identified, three of them were predictive models (EORTC, CUETO, EAU 2021) and three were based on expert opinion (EAU 2020, AUA, NCCN). A high risk of bias was present in the majority of the studies. Certain heterogenicity was found among the studies regarding adjuvant therapy, postoperative instillation or second resection. The definition of oncological outcomes was not standardized in the included studies. CUETO and EORTC scoring systems are the most validated. In general, validations showed a poor discrimination capability to predict recurrence, slightly better for progression. The EAU 2021 model overestimates the risk of progression in patients treated with BCG. Carcinoma in situ is underrepresented in all the studies analyzed. CONCLUSIONS: The existing classifications show poor discrimination capability for recurrence and possibly helpful discrimination capability for progression in NMIBC patients. These results highlight the unmet need to develop novel accurate risk models for patients with NMIBC, which could be improved with the combination of clinicopathological and molecular information.

show abstract

“…After screening, a full‐text review was performed for 201 articles. According to the inclusion criteria, we finally identified 72 studies comprising 28 139 patients eligible for the meta‐analysis [12–83]. The demographics of each included study are shown in Table 1 and Table S2.…”

Section: Resultsmentioning

confidence: 99%

“…Of the 72 included studies, five included only Ta NMIBC patients [12–16], and 26 studies included only T1 NMIBC patients [17–42]. The other 41 studies included both Ta and T1 patients [43–83].…”

Section: Resultsmentioning

confidence: 99%

Oncological impact of cystoscopic findings in non‐muscle‐invasive bladder cancer: a meta‐analysis

et al. 2023

View full text Add to dashboard Cite

Objective To assess the association between cystoscopic findings and oncological outcomes in patients with non‐muscle‐invasive bladder cancer (NMIBC) given that the oncological impact of quantity and quality assessment of tumours with cystoscopy has not been well verified. Methods Multiple databases were queried in May 2022 for studies investigating the association of oncological outcomes, such as recurrence‐free (RFS), progression‐free (PFS), and cancer‐specific survival (CSS), with cystoscopic findings, including multiplicity, size, and gross appearance of tumours in patients with NMIBC. Results Overall, 73 studies comprising 28 139 patients were eligible for the meta‐analysis. Tumour multiplicity was associated with worse RFS (pooled hazard ratio [HR] 1.61, 95% confidence interval [CI] 1.48–1.74) and PFS (pooled HR 1.44, 95% CI 1.18–1.76) in NMIBC patients (including both Ta and T1). Tumour size (≥3 cm) was associated with worse RFS (pooled HR 1.97, 95% CI 1.69–2.30) and PFS (pooled HR 1.81, 95% CI 1.52–2.15) in NMIBC patients. In patients with T1 bladder cancer (BCa), tumour multiplicity and size (≥3 cm) were also associated with worse RFS, PFS and CSS. By contrast, among patients treated with bacillus Calmette‐Guérin (BCG), tumour multiplicity was not associated with worse RFS, and tumour size (≥3 cm) was not associated with worse PFS. Sessile tumours were associated with worse RFS (pooled HR 2.14, 95% CI 1.52–3.01) and PFS (pooled HR 2.17, 95% CI 1.42–3.32) compared to pedunculated tumours. Compared to papillary tumours, solid tumours were associated with worse RFS (pooled HR 1.84, 95% CI 1.25–2.72) and PFS (pooled HR 3.06, 95% CI 2.31–4.07) in NMIBC patients, and CSS in T1 BCa patients (pooled HR 2.32, 95% CI 1.63–3.30). Conclusions Cystoscopic findings, including tumour multiplicity, size, and gross appearance, strongly predict oncological outcomes in NMIBC patients. Cystoscopic visual features can help in the decision‐making process regarding the timeliness and extent of tumour resection as well as future management such as intravesical therapy.

show abstract

Applicability of the EORTC risk tables to predict outcomes in non-muscle-invasive bladder cancer in Turkish patients

Cited by 8 publications

References 18 publications

Development of a New Recurrence-Free Survival Prediction Nomogram for Patients with Primary Non-Muscle-Invasive Bladder Cancer Based on Preoperative Controlling Nutritional Status Score

Development of a New Recurrence-Free Survival Prediction Nomogram for Patients with Primary Non-Muscle-Invasive Bladder Cancer Based on Preoperative Controlling Nutritional Status Score

Predicting Recurrence and Progression in Patients with Non-Muscle-Invasive Bladder Cancer: Systematic Review on the Performance of Risk Stratification Models

Oncological impact of cystoscopic findings in non‐muscle‐invasive bladder cancer: a meta‐analysis

Contact Info

Product

Resources

About