Recurrence in Non-Muscle Invasive Bladder Cancer Patients: External Validation of the EORTC, CUETO and EAU Risk Tables and Towards a Non-Linear Survival Model

2020 ◽  
Vol 6 (3) ◽  
pp. 277-284
Author(s):  
Marit Lucas ◽  
Ilaria Jansen ◽  
Jorg R. Oddens ◽  
Ton G. van Leeuwen ◽  
Henk A. Marquering ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Predictive Value ◽  
Cox Regression ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Non Linear ◽  
Muscle Invasive

BACKGROUND: EORTC, CUETO and EAU are the most commonly used risk stratification models for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). OBJECTIVE: We assessed the predictive value of the EORTC, CUETO and EAU risk group stratification methods for our population and explore options to improve the predictive value using Cox Proportional Hazards (CPH), Boosted Cox regression and a non-linear Random Survival Forest (RSF) model. MATERIALS: Our retrospective database included of 452 NMIBC patients who underwent a transurethral resection of bladder tumor (TURBT) between 2000 and 2018 in our hospital. The cumulative incidence of recurrence was calculated at one- and five-years for all risk stratification methods. A customized CPH, Boosted Cox and RSF models were trained in order to predict recurrence, and the performances were compared. RESULTS: Risk stratification using the EORTC, CUETO and EAU showed small differences in recurrence probabilities between the risk groups as determined by the risk stratification. The concordance indices (C-index) were low and ranged between 0.51 and 0.57. The predictive accuracies of CPH, Boosted Cox and RSF models were also moderate, with C-indices ranging from 0.61 to 0.64. CONCLUSIONS: Prediction of recurrence in patients with NMIBC based on patient characteristics is difficult. Alternative (non-linear) approaches have the potential to improve the predictive value. Nonetheless, the currently used characteristics are unable to properly stratify between the recurrence risks of patients.

MCM5 urine expression (ADXBLADDER) is a reliable biomarker of high-risk non- muscle-invasive bladder cancer recurrence: A prospective matched case-control study

2020 ◽  
pp. 1-5
Author(s):  
Łukasz Białek ◽  
Katarzyna Czerwińska ◽  
Łukasz Fus ◽  
Wojciech Krajewski ◽  
Anna Sadowska ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Predictive Value ◽  
External Validation ◽  
Cancer Recurrence ◽  
Invasive Bladder Cancer ◽  
Control Group ◽  
High Grade ◽  
Urinary Biomarker ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive

BACKGROUND: Mini Chromosome Maintenance 5 (MCM5) is considered as a urinary biomarker of bladder cancer. ADXBLADDER is a commercially available test to detect MCM5 antibodies. OBJECTIVE: External validation of ADXBLADDER test as a urinary biomarker of histopathologically confirmed non-muscle invasive bladder cancer (NMIBC) recurrence. METHODS: The study enrolled 119 consecutive patients with a history of NMIBC and 37 healthy volunteers matched as controls. Single, full-void urine samples were collected from patients before cystoscopy ± TUR. To measure MCM5 expression, Arquer Diagnostics ADXBLADDER test was used. The study protocol was registered within the clinical trials database (NCT03796299). RESULTS: Among patients with NMIBC history, recurrence was diagnosed in 83 patients (69.7%). ADXBLADDER demonstrated sensitivity of 73.5% (95% confidence interval (CI) 62.7%–82.6%), specificity of 33.3% (95% CI 18.6% to 51%), overall negative predictive value (NPV) of 35.3% (95% CI 23.3% to 49.5%) and overall positive predictive value of 71.8% (95% CI 66.1% to 76.8%) for detecting recurrence. In a control group, false positive ADXBLADDER results were noticed in 18 patients (48.6%). The sensitivity and NPV were the highest in invasive tumors (100% and 100%, respectively) and in high-grade recurrences (81.8% and 94.1%, respectively). CONCLUSIONS: ADXBLADDER has a moderate sensitivity and poor specificity in detecting NMIBC recurrence. However, it properly diagnoses patients with T1+ stage recurrence or high-grade tumors.

scholarly journals Predictive value of prognostic nutritional index and systemic immune‐inflammation index on tumor progression in bladder cancer patients with after radical cystectomy

2020 ◽  
Author(s):  
Jiatong Zhou ◽  
Xitong Xu ◽  
RanLu Liu
Keyword(s):  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Predictive Value ◽  
Cox Regression ◽  
Prognostic Nutritional Index ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Nutritional Index ◽  
Immune Inflammation ◽  
Muscle Invasive

Abstract OBJECTIVES: The purpose of this study was to explore the predictive value of preoperative prognostic nutritional index(PNI) and systemic immune‐inflammation index(SII) for local tumor stage in bladder cancer(BC) after radical cystectomy(RC).METHODS: We researched our database between April 2011 and October 2019. There were 195 BC patients who underwent RC. The PNI and SII were calculated using preoperative blood sample results. The predictive value of SII and PNI was analysed with univariate and multivariate Cox regression models. Receiver operating characteristic (ROC) was used to determine the optimum PNI. Signifcant P was P<0.05.RESULTS: Of patients, all patients were males with a mean age of 67.94±8.97years. Mean serum albumin was 42.13±4.28(g/L), mean PNI score was 51.29±6.09 and mean SII was 661.67±506.22. Multivariable Cox regression analysis demonstrated that PNI scores and SII could not play a significantly predictive factor between muscle invasive bladder cancer(MIBC) and non-muscle-invasive bladder cancer(NMIBC). While we also found PNI was an independent risk factors for predicting tumor stagep(pT＜3a and pT≥3a).CONCLUSIONS: Our research revealed that preoperative low PNI but not SII could be used as an independent factor to predict worse pathologically stage(pT≥3a). But preoperative PNI and SII might not were significantly related with the incidence risk of muscle invasive. We still need future studies with large cohorts to identify our results.

scholarly journals Predictive Value of Prognostic Nutritional index and Systemic Immune‐Inflammation Index on Tumor Progression in Bladder Cancer Patients with After Radical Cystectomy

2021 ◽  
Author(s):  
jiatong zhou ◽  
xitong xu ◽  
ranlu liu
Keyword(s):  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Predictive Value ◽  
Cox Regression ◽  
Prognostic Nutritional Index ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Nutritional Index ◽  
Immune Inflammation ◽  
Muscle Invasive

Abstract OBJECTIVES: The purpose of this study was to explore the predictive value of preoperative prognostic nutritional index(PNI) and systemic immune‐inflammation index(SII) for local tumor stage in bladder cancer(BC) after radical cystectomy(RC).METHODS: We researched our database between April 2011 and October 2019. There were 195 BC patients who underwent RC. The PNI and SII were calculated using preoperative blood sample results. The predictive value of SII and PNI was analysed with univariate and multivariate Cox regression models. Significant P was P<0.05.RESULTS: Of patients, all patients were males with a mean age of 67.94±8.97years. Mean serum albumin was 42.13±4.28(g/L), mean PNI score was 51.29±6.09 and mean SII was 661.67±506.22. Multivariable Cox regression analysis demonstrated that PNI scores and SII could not play a significantly predictive factor between muscle invasive bladder cancer(MIBC) and non-muscle-invasive bladder cancer(NMIBC). While we also found PNI was an independent risk factors for predicting tumor stagep(pT＜3a and pT≥3a).CONCLUSIONS: Our research revealed that preoperative low PNI but not SII could be used as an independent factor to predict worse pathologically stage(pT≥3a). We still need future studies with large cohorts to identify our results.

Development and Validation of a Prognostic Nomogram Model for Recurrence of Non-Muscle Invasive Bladder Cancer

2021 ◽  
Author(s):  
Sanhe Liu ◽  
Yongzhi Li ◽  
Diansheng Cui ◽  
Yuexia Jiao ◽  
Liqun Duan ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Muscle Invasive Bladder Cancer ◽  
Clinicopathologic Characteristics ◽  
Cox Proportional Hazards Regression ◽  
Proportional Hazards Regression ◽  
Muscle Invasive

Abstract BackgroundDifferent recurrence probability of non-muscle invasive bladder cancer (NMIBC) requests different adjuvant treatments and follow-up strategies. However, there is no simple, intuitive, and generally accepted clinical recurrence predictive model available for NMIBC. This study aims to construct a predictive model for the recurrence of NMIBC based on demographics and clinicopathologic characteristics from two independent centers. MethodsDemographics and clinicopathologic characteristics of 511 patients with NMIBC were retrospectively collected. Recurrence free survival (RFS) was estimated using the Kaplan-Meier method and log-rank tests. Univariate Cox proportional hazards regression analysis was used to screen variables associated with RFS, and a multivariate Cox proportional hazards regression model with a stepwise procedure was used to identify those factors of significance. A final nomogram model was built using the multivariable Cox method. The performance of the nomogram model was evaluated with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed with bootstrap resampling. X-tile software was used for risk stratification calculated by the nomogram model. ResultsIndependent prognostic factors including tumor stage, recurrence status, and European Association of Urology (EAU) risk stratification group were introduced to the nomogram model. The model showed acceptable calibration and discrimination (area under the receiver operating characteristic [ROC] curve was 0.85; the consistency index [C-index] was 0.79 [95% CI: 0.76 to 0.82]), which was superior to the EAU risk stratification group alone. The decision curve also proved well clinical usefulness. Moreover, all populations could be stratified into three distinct risk groups by the nomogram model. ConclusionsWe established and validated a novel nomogram model that can provide individual prediction of RFS for patients with NMIBC. This intuitively prognostic nomogram model may help clinicians in postoperative treatment and follow-up decision-making.

Integrated clinicopathologic and molecular risk stratification for disease recurrence in muscle-invasive bladder cancer.

2021 ◽  
Vol 39 (6_suppl) ◽  
pp. 490-490
Author(s):  
Ruben Carmona ◽  
Alan Pollack ◽  
Zachary L Smith ◽  
Jeff M. Michalski ◽  
Hiram Alberto Gay ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Molecular Subtype ◽  
Disease Recurrence ◽  
Invasive Bladder Cancer ◽  
Training Set ◽  
Combined Model ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Testing Set

490 Background: Integrating molecular subtypes, gene transcripts associated with disease recurrence (DR), and clinicopathologic features may help risk stratify muscle-invasive bladder cancer (MIBC) patients & guide therapy selection. We hypothesized that combined transcriptomic & clinical data would improve risk stratification for DR (local or distant) after cystectomy +/- adjuvant chemotherapy. Methods: We identified 401 MIBC patients (pT2-4 N0-N3 M0) in The Cancer Genome Atlas with detailed demographic, clinical, pathologic, and treatment-related data. We split the data into training (60%) & testing (40%) sets. We produced RNA gene expression scores for molecular subtype using 48 established, relevant genes (PMID 28988769). In the training set, we performed feature selection by conducting random forest modeling of an additional 108 genes associated with DR. We kept genes of highest importance based on the evaluation of increasing mean-squared error & node purity. We excluded highly correlated genes & used the false discovery rate method for multiple hypotheses testing. We performed univariable analyses on genes of highest importance, molecular subtype, & clinicopathologic variables. Using adjusted multivariable analyses (MVA), we built two models: with & without transcriptomic data. Using the testing set, we compared the final models' performance to predict DR, using receiver operating characteristics & area under the curve (AUC). Results: Median follow-up was 18 months (range 1-168). 104 patients recurred with a 5-yr cumulative incidence of 34.6%[28.6-40.5%]. Using the training set, we identified 6 genes significantly associated with DR (VEGFA, TRMT1, FGFR2B, ERBB2, MMP14, PDGFC). The final MVA showed that the new 6-gene signature (HR 1.61, 95% CI 1.27-2.05, p < 0.001); immune molecular subtype [increased expression of PD-L1, PD-1, IDO1, CXCL11, L1CAM, SAA1] (HR 0.52, 95% CI 0.29-0.94, p = 0.03); smoking status (HR 1.17 per 10 pack-years, 95% CI 1.05-1.29, p = 0.005); and local failure risk factors [≥pT3 with negative margins & ≥10 nodes removed (HR 1.63, 95% CI 1.15-2.32, p = 0.006); ≥pT3 and positive margins OR < 10 nodes removed (HR 3.26, 95%CI 2.43 to 4.09, p = 0.007)], were all significantly associated with DR. This combined model outperformed a stand-alone clinicopathologic model (AUC 0.75 vs. 0.66) in the testing set. The combined model stratified patients based on DR risk into 3 groups with 5-yr cumulative incidences of 19.8%[7.7-31.9%] (low-risk); 34.5%[26.1-42.8%] (intermediate); and 49.8%[37.7-61.9%] (high), Gray’s Test p < 0.0001. Conclusions: To our knowledge, this study is the first to integrate clinicopathologic & transcriptomic information (including molecular subtype) to better stratify MIBC patients by risk of recurrence. This stratification may help guide decision-making for adjuvant treatment. Further validation is warranted.

scholarly journals Prediction of histological stage based on cystoscopic appearances of newly diagnosed bladder tumours

2016 ◽  
Vol 98 (8) ◽  
pp. 547-551 ◽  
Author(s):  
VA During ◽  
GM Sole ◽  
AK Jha ◽  
JA Anderson ◽  
RT Bryan
Keyword(s):  
Bladder Cancer ◽  
Predictive Value ◽  
Bladder Tumour ◽  
Visual Assessment ◽  
Invasive Bladder Cancer ◽  
Newly Diagnosed ◽  
Muscle Invasive Bladder Cancer ◽  
Cross Sectional ◽  
Radiological Staging ◽  
Muscle Invasive

INTRODUCTION In the 75–80% of urothelial bladder cancers (UBC) presenting as non-muscle invasive bladder cancer (NMIBC), transurethral resection of bladder tumour (TURBT) is the key treatment and staging procedure. In the 20–25% of patients with muscle invasive bladder cancer (MIBC), further cross-sectional imaging is required to complete the staging process before considering radical treatment. Given the adverse effects of ionising radiation, clinicians identify patients believed to have MIBC, and so requiring further imaging pre-TURBT, at the tumour histology/stage based on the tumour’s visual characteristics. There is minimal evidence describing the accuracy of such predictions in newly-diagnosed patients. METHODS Over a 6-year period, a database of patients undergoing resection of newly-diagnosed bladder lesions in a single UK centre was prospectively established. Predictions based on histology were simultaneously recorded, and the accuracy of these predictions of histology/stage subsequently assessed. RESULTS One hundred and twenty two (73.1%) patients with histologically confirmed NMIBC had predictions recorded versus 45 (26.9%) patients with MIBC. Visual assessment predictions of MIBC had a sensitivity of 88.9% (95% confidence interval [CI] 76.5%–95.2%) and a specificity of 91.0% (95% CI 84.6%–94.9%), giving a positive predictive value of 78.4% (95% CI 65.4%–87.5%) and a negative predictive value of 95.7% (95% CI 90.3%–98.1%). CONCLUSIONS We find that visual assessment is accurate in predicting the presence of MIBC. This supports the practice of stratifying patients at the time of initial cystoscopy for those requiring further radiological staging pre-TURBT.

scholarly journals Validation of EORTC, CUETO and EAU risk stratification in prediction of recurrence, progression and death of patients with initially non-muscle invasive bladder cancer (NMIBC): a cohort analysis with systematic review.

2019 ◽  
Author(s):  
Mateusz Jobczyk ◽  
Konrad Stawiski ◽  
Wojciech Fendler ◽  
Waldemar Różański
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Cohort Analysis ◽  
Risk Groups ◽  
Invasive Bladder Cancer ◽  
Sufficient Evidence ◽  
Current Evidence ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Better Than

Abstract Purpose: To validate and summarize current evidence about the reliability of EORTC, CUETO and EAU risk stratification in prediction of recurrence, progression and death of patients with initially non-muscle-invasive bladder cancer (NMIBC).Methods: Retrospective cohort analysis of 322 patients with newly diagnosed NMIBC. We assessed the concordance (Harrell's c-index) of our results with calculated risk scores in Cox proportional hazard regression models and utilized receiver operating characteristic curve analysis (area under curve; AUCROC). Lastly, to further confirm our observations we conducted a systematic reviewResults: 1-year and 5-year c-indices ranged from 0.55 to 0.66 for recurrence and from 0.72 to 0.82 for progression. AUCROC of predictions ranged from 0.46 for 1-year recurrence risk based on CUETO groups to 0.82 for 1-year progression risk based on EAU risk groups. The accuracy of prediction was lower for patients treated with BCG maintenance immunotherapy. EORTC model (overall c-index c=0.64; 95%CI:0.61-0.68) was superior to EAU (p=0.035; 0.62; 95%CI: 0.59-0.66) and CUETO (p<0.001; c=0.53; 95%CI:0.50-0.56) model in recurrence prediction. EORTC model (c=0.82; 95%CI:0.77-0.86) also performed better than CUETO (p=0.008; c=0.73; 95%CI:0.66-0.81) but there was no sufficient evidence that it performed better than EAU (p=0.572; c=0.81; 95%CI:0.77-0.84) for predicting progression. EORTC and CUETO comparably predicted progression in BCG-treated EAU high-risk patients (p=0.48).Conclusions: The division into risk groups by EORTC, CUETO and EAU offered moderately accurate predictions about recurrence and progression of NMIBC, which emphasizes the urgent need for the development of more personalized and accurate predictive tool. EORTC provided the best recurrence and progression prediction.

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