Prediction Of Muscle-invasive Bladder Cancer Research Articles

Tumor contact length with bladder wall provides effective risk stratification for lesions with a VIRADS score of 2-3.

To evaluate the diagnostic performance of the tumor contact length (TCL) in the prediction of MIBC (muscle-invasive bladder cancer) in lesions corresponding to the vesical imaging-reporting and data system (VIRADS) score 2-3. This is a single institution, retrospective study targeting 191 consecutive patients assigned of VIRADS score 2-3, who had pre-transurethral resection MRI from July 2019 to September 2021. Logistic regression analyses were performed to determine meaningful predictors of MIBC for this score group, and a nomogram was plotted with those variables. The diagnostic performance of each predictor was compared at predefined thresholds (VIRADS score 3 and TCL 3cm) using the generalized linear model and ROC analysis. Both VIRADS score and TCL remained independent predictors of MIBC for this score group (odds ratio 7.3 for VIRADS score, and 1.3 for TCL, p < 0.01 for both). The contribution of TCL to the probability of MIBC in the nomogram was greater than that of the VIRADS score. VIRADS score had a sensitivity of 0.54 (14/26), specificity of 0.92 (203/221), and diagnostic accuracy of 0.88 (217/247), and TCL showed a sensitivity of 0.89 (23/26), specificity of 0.95 (209/221), and diagnostic accuracy of 0.94 (232/247). The difference in sensitivity (p = 0.03) and accuracy (p = 0.04) was statistically significant. The AUC was also significantly wider for TCL than for VIRADS (0.97 vs. 0.73, p < 0.01). A simple index, TCL, may be helpful in further risk stratification for MIBC in patients with a score of VIRADS 2-3. For bladder cancer patients with insufficient qualitative evidence of muscle layer invasion using VIRADS categorization, TCL, a simple quantitative indicator defined as the curvilinear contact length between the bladder wall and the tumor, may be helpful in risk stratification. • Even when only lesions with score 2-3 were targeted, VIRADS was still a meaningful indicator of MIBC. • With a predefined threshold of 3cm applied, TCL outperformed VIRADS in the score 2-3 group, in predicting MIBC. • A longer TCL for a lesion with a VIRADS score 2 may warrant an additional warning for MIBC, whereas a shorter TCL for a lesion with a score 3 may indicate a lower risk of MIBC.

The role of radiomics with machine learning in the prediction of muscle-invasive bladder cancer: A mini review.

Bladder cancer is a common malignant tumor in the urinary system. Depending on whether bladder cancer invades muscle tissue, it is classified into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). It is crucial to accurately diagnose the muscle invasion of bladder cancer for its clinical management. Although imaging modalities such as CT and multiparametric MRI play an important role in this regard, radiomics has shown great potential with the development and innovation of precision medicine. It features outstanding advantages such as non-invasive and high efficiency, and takes on important significance in tumor assessment and laor liberation. In this article, we provide an overview of radiomics in the prediction of muscle-invasive bladder cancer and reflect on its future trends and challenges.

Detecting the muscle invasiveness of bladder cancer: An application of diffusion kurtosis imaging and tumor contact length

PurposeTo evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) parameters and tumor contact length (TCL) among clinical and radiological factors for preoperative prediction of muscle-invasive bladder cancer (MIBC). MethodA total of ninety-seven patients underwent 3.0 T MRI scan with propeller fast spin-echo T2WI, echo planar imaging diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Two radiologists independently viewed multiparametric MRI (mpMRI) of each patient, graded the VI-RADS, drew the region of interest (ROI) and measured TCL. Interclass correlation coefficients (ICCs), Kappa statistics, Kolmogorov-Smirnov test, Mann-Whitney U tests, chi-square tests, logistic regression analyses, Hosmer-Lemeshow tests, receiver operating characteristic curve (ROC) analysis, and area under the curve (AUC) were applied. ResultsThe mean Kapp of NMIBC group (0.62 ± 0.01) was significantly lower than that of MIBC group (0.79 ± 0.08). The mean TCL of MIBC group (4.66 ± 1.89) was significantly larger than TCL of NMIBC group (1.88 ± 1.50) (all p < 0.01). At the corresponding cut-off, AUC of TCL, Kapp, VI-RADS and the combination of Kapp and TCL were 0.87, 0.92, 0.90, and 0.95, respectively. TCL and Kapp were risk factors of BC muscle invasion at both univariate and multivariate analysis. ConclusionsKapp performed better than conventional DWI in predicting MIBC. Kapp and TCL were independent risk factors of MIBC and could complement VI-RADS for predicting muscle invasion. The combination of Kapp and TCL had the largest AUC and highest accuracy among all parameters.

MP73-20 DIAGNOSTIC ACCURACY OF THE BI-PARAMETRIC MRI ANALYSIS FOR MUSCLE-INVASIVE BLADDER CANCER PREDICTION

You have accessJournal of UrologyBladder Cancer: Non-invasive IV (MP73)1 Apr 2020MP73-20 DIAGNOSTIC ACCURACY OF THE BI-PARAMETRIC MRI ANALYSIS FOR MUSCLE-INVASIVE BLADDER CANCER PREDICTION Hyun Jung Jin* Hyun Jung Jin*Hyun Jung Jin* More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000959.020AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To investigate accuracy of the bi-parametric MRI (bpMRI) analysis using Vesical Imaging-Reporting and Data System (VI-RADS) for the prediction of muscle-invasive bladder cancer (MIBC). METHODS: In this retrospective study, a total of 100 patients who were suspected bladder cancer underwent bpMRI from May 2016 to March 2019. All bpMRI were retrospectively reviewed by one radiologist and scoring using the VI-RADS. Sensitivity, specificity and accuracy were calculated for each VI-RADS cutoff. The performance of bpMRI for the prediction of MIBC was analyzed using receiver operating characteristics (ROC) curves. RESULTS: Among the 100 patients, 81 patients were man and 19 patients were women, with the median age of 66.9 years (interquartile range [IQR], 60.0–76.0 years). Histopathological examination revealed 77 patients with non-muscle invasive bladder cancer (NMIBC) and 23 patients with MIBC. When the cutoff VIRADS ≥ 3 was used, sensitivity and specificity were 83% and 82%, and accuracy was 89%, respectively. The area under curve (AUC) on the ROC curve was 0.892 (95% confidence interval [CI] 0.814, 0.945). CONCLUSIONS: Analysis of bpMRI using the VI-RADS showed good accuracy in distinguishing NMIBC from MIBC. However, prospective and multi-institutional studies are needed to verify the validity of this study. Source of Funding: None © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1129-e1130 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hyun Jung Jin* More articles by this author Expand All Advertisement PDF downloadLoading ...

Prediction of Muscle-invasive Bladder Cancer Using Urinary Proteomics

Minimally invasive methods of predicting the risk of muscle-invasive urothelial bladder carcinoma may expedite appropriate therapy and reduce morbidity and cost. Here, capillary electrophoresis coupled mass spectrometry was used to identify urinary polypeptide bladder cancer biomarkers in 127 patients. These markers were used to construct a panel discriminating muscle-invasive from noninvasive disease, which was refined in 297 additional samples from healthy volunteers, patients with malignant and nonmalignant genitourinary conditions. Sequencing of panel polypeptides was then done. Finally, the ability of the panel to predict muscle-invasive disease was evaluated prospectively in 130 bladder carcinoma patients. Four sequenced polypeptides formed a panel predictive of muscle-invasive disease. Prospective evaluation of this panel revealed a sensitivity of 81% [95% confidence interval (CI), 69-90] and specificity of 57% (95% CI, 45-69) for muscle-invasive disease. Multivariate analysis revealed the panel (P < 0.0001) and tumor grade (P = 0.0001), but not urine cytology, predict muscle invasion. A model including grade and panel polypeptide levels improved sensitivity [92% (95% CI, 82-97)] and specificity [68% (95% CI, 55-79)] for muscle-invasive disease. A model score of >0.88 provided a negative predictive value of 77% and positive predictive value of 90% for muscle invasion. Use of urinary peptides seems promising in estimating the probability a patient harbors muscle-invasive urothelial bladder cancer. These peptides may also shed novel insights into the biology of bladder tumor progression not obtainable by other methods. Clinical trials seem warranted to evaluate the effect of this approach on practice.