scholarly journals Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Zongtai Zheng ◽  
Feijia Xu ◽  
Zhuoran Gu ◽  
Yang Yan ◽  
Tianyuan Xu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Multiparametric Mri ◽  
Data System ◽  
Invasive Bladder Cancer ◽  
Training Set ◽  
Muscle Invasive Bladder Cancer ◽  
Invasive Status ◽  
Validation Set ◽  
Muscle Invasive ◽  
Radiomics Signature

BackgroundThe treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC.MethodThe retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set.ResultsCompared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram.ConclusionsThe mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.

scholarly journals Staging of bladder cancer with multiparametric MRI

2020 ◽  
Vol 93 (1112) ◽  
pp. 20200116
Author(s):  
Hiroshi Juri ◽  
Yoshifumi Narumi ◽  
Valeria. Panebianco ◽  
Keigo Osuga
Keyword(s):  
Bladder Cancer ◽  
Multiparametric Mri ◽  
Data System ◽  
Invasive Bladder Cancer ◽  
Systematic Evaluation ◽  
Clinical Settings ◽  
Future Perspectives ◽  
Muscle Invasive Bladder Cancer ◽  
Muscle Invasive ◽  
Selection Of

The distinction of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer is important for the selection of the optimal treatment. Multiparametric MRI (mp-MRI) has been an useful modality for the T staging of bladder cancer, and a systematic evaluation of mp-MRI is needed. The Vesical Imaging Reporting and Data System was designed to standardize the scanning and reporting criteria based on mp-MRI for clinical and research applications. This review briefly describes the method, interpretation, and timing of mp-MRI examinations in the clinical settings. Validation studies of Vesical Imaging Reporting and Data System and future perspectives are also considered.

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 Systematic Review and Meta-Analysis of Vesical Imaging-Reporting and Data System (VI-RADS) Inter-Observer Reliability: An Added Value for Muscle Invasive Bladder Cancer Detection

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2994
Author(s):  
Francesco Del Del Giudice ◽  
Martina Pecoraro ◽  
Hebert Alberto Vargas ◽  
Stefano Cipollari ◽  
Ettore De Berardinis ◽  
...  
Keyword(s):  
Systematic Review ◽  
Bladder Cancer ◽  
Meta Analysis ◽  
Intraclass Correlation ◽  
Data System ◽  
Invasive Bladder Cancer ◽  
Muscle Invasive Bladder Cancer ◽  
Diagnostic Reliability ◽  
Meta Regression ◽  
Muscle Invasive

The Vesical Imaging-Reporting and Data System (VI-RADS) has been introduced to provide preoperative bladder cancer staging and has proved to be reliable in assessing the presence of muscle invasion in the pre-TURBT (trans-urethral resection of bladder tumor). We aimed to assess through a systematic review and meta-analysis the inter-reader variability of VI-RADS criteria for discriminating non-muscle vs. muscle invasive bladder cancer (NMIBC, MIBC). PubMed, Web of Science, Cochrane, and Embase were searched up until 30 July 2020. The Quality Appraisal of Diagnostic Reliability (QAREL) checklist was utilized to assess the quality of included studies and a pooled measure of inter-rater reliability (Cohen’s Kappa [κ] and/or Intraclass correlation coefficients (ICCs)) was calculated. Further sensitivity analysis, subgroup analysis, and meta-regression were conducted to investigate the contribution of moderators to heterogeneity. In total, eight studies between 2018 and 2020, which evaluated a total of 1016 patients via 21 interpreting genitourinary (GU) radiologists, met inclusion criteria and were critically examined. No study was considered to be significantly flawed with publication bias. The pooled weighted mean κ estimate was 0.83 (95%CI: 0.78–0.88). Heterogeneity was present among the studies (Q = 185.92, d.f. = 7, p < 0.001; I2 = 92.7%). Meta-regression analyses showed that the relative % of MIBC diagnosis and cumulative reader’s experience to influence the estimated outcome (Coeff: 0.019, SE: 0.007; p= 0.003 and 0.036, SE: 0.009; p = 0.001). In the present study, we confirm excellent pooled inter-reader agreement of VI-RADS to discriminate NMIBC from MIBC underlying the importance that standardization and reproducibility of VI-RADS may confer to multiparametric magnetic resonance (mpMRI) for preoperative BCa staging.

Development of a genomic-clinical classifier for predicting progression after radical cystectomy in patients with muscle invasive bladder cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 4542-4542
Author(s):  
Anirban Pradip Mitra ◽  
Nicholas Erho ◽  
Lucia L.C. Lam ◽  
Ismael A. Vergara ◽  
Thomas Sierocinski ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Radical Cystectomy ◽  
Cancer Progression ◽  
External Validation ◽  
Multivariable Analysis ◽  
Invasive Bladder Cancer ◽  
K Nearest Neighbor ◽  
Muscle Invasive Bladder Cancer ◽  
Validation Set ◽  
Muscle Invasive

4542 Background: The mainstay of muscle-invasive bladder cancer treatment is surgical resection with/without multi-agent chemotherapy. Management decisions are based on a small number of clinical and pathologic parameters with poor prognostic and predictive power. There is an urgent need for enhanced biomarkers to guide therapy of this lethal disease. Here we have developed a genomic signature of bladder cancer progression using whole transcriptome profiling technology. Methods: 251 FFPE bladder cancer specimens were obtained from patients undergoing radical cystectomy at the University of Southern California (1998-2004). All patients had pT2-T4a,N0 urothelial carcinoma in the absence of pre-operative chemotherapy. Median follow-up was 5 years. RNA expression levels were measured with 1.4 million feature oligonucleotide microarrays. Patients were divided into a training set (2/3 of cohort) to develop a genomic classifier for risk of progression (defined as any type of bladder cancer recurrence), and a validation set (1/3 of cohort). In parallel, multivariable analysis was used to develop a clinical classifier using typical clinical and pathologic variables. Finally, a genomic-clinical classifier was built combining the genomic classifier with clinical variables using logistic regression. The receiver-operator characteristic (ROC) area under the curve (AUC) metric was used to evaluate each classifier in the validation set. Results: The genomic classifier consisted of 89 features corresponding to 80 genes that were combined in a k-nearest neighbor model (KNN89). KNN89 showed an AUC of 0.77 in ROC analysis on the validation set. The best clinical classifier showed an AUC of 0.72. The genomic-clinical classifier demonstrated an AUC of 0.81. Multivariable analysis incorporating all clinical parameters and KNN89 further revealed that KNN89 was the only significant predictor of bladder cancer progression (p=0.0077). Conclusions: We have developed a combined genomic-clinical classifier that shows improved performance over clinical models alone for prediction of progression after radical cystectomy. External validation of this classifier is ongoing.

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