scholarly journals Development and Validation of a Novel Recurrence Risk Stratification for Initial Non-Muscle Invasive Bladder Cancer in the Han Chinese Population

2020 ◽  
Vol 11 (7) ◽  
pp. 1668-1678 ◽  
Author(s):  
Zhiyong Wang ◽  
Wansheng Gao ◽  
Jian Li ◽  
Tianen Wang ◽  
Man Zhu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Chinese Population ◽  
Recurrence Risk ◽  
Han Chinese ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Han Chinese Population ◽  
Muscle Invasive ◽  
Development And Validation

scholarly journals Development and Validation of a Novel Recurrence Risk Stratification for Initial Non-muscle Invasive Bladder Cancer in Asia

EBioMedicine ◽  
2016 ◽  
Vol 12 ◽  
pp. 98-104 ◽  
Author(s):  
Takeshi Ieda ◽  
Satoru Muto ◽  
Fumitaka Shimizu ◽  
Masataka Taguri ◽  
Shigeto Yanada ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Recurrence Risk ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Development And Validation

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 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.

Microfluidic Assaying of Circulating Tumor Cells and its Correlation with Muscle Invasiveness and Tumor Grade of Urothelial Bladder Cancer

2021 ◽  
Author(s):  
Guanghou Fu ◽  
Kok Suen Cheng ◽  
Anqi Chen ◽  
Zhijie Xu ◽  
Xiaoyi Chen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Stratification ◽  
Cancer Patients ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Tumor Grade ◽  
Invasive Bladder Cancer ◽  
Low Grade ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive

Abstract Background: Bladder cancer is characterized by its frequent recurrence and progression. Effective treatment strategies need to be based on an accurate risk stratification, in which muscle invasiveness and tumor grade represent the two most important factors. Traditional imaging techniques provide preliminary information about muscle invasiveness but are lacking in terms of accuracy. Although as the gold standard, pathological biopsy is only available after the surgery and cannot be performed longitudinally for long-term surveillance. Methods: In this work, we developed a microfluidic approach that interrogates circulating tumor cells (CTCs) in the peripheral blood of bladder cancer patients to reflect the risk stratification of the disease. Results：In a cohort of 48 bladder cancer patients comprising 33 non-muscle invasive bladder cancer (NMIBC) cases and 15 muscle invasive bladder cancer (MIBC) cases, the CTC count was found to be considerably higher in the MIBC group compared with the NMIBC group (4.67 vs. 1.88 CTCs/3 mL, P=0.019), and was significantly higher in high-grade bladder cancer patients verses low-grade bladder cancer patients (3.69 vs. 1.18 CTCs/3mL, P=0.024). Conclusions: This microfluidic assay of CTCs is believed to be a promising complementary tool for the risk stratification of bladder cancer.Trial registration: This research was conducted under the approval of the Ethics Committee of the First Affiliated Hospital at Zhejiang University School of Medicine with the Registration No. 2015-218.

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