Muscle Invasion Of Bladder Cancer Research Articles

Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram

ObjectivesThe aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa).MethodsA retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram’s performance was assessed based on discrimination, calibration, and clinical utility.ResultsAfter obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742–0.880), 0.845 (95% CI, 0.781–0.908), and 0.896 (95% CI, 0.846–0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703–0.913), 0.847 (95% CI, 0.739–0.954), and 0.887 (95% CI, 0.803–0.971). According to the DeLong test, the radiomics-clinical nomogram’s AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models.ConclusionsCT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa.

Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.

To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the areaunder the receiver operating characteristiccurve(AUC). Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57min to 3:13min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer.

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

ObjectiveThis study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. MethodsIn this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. ResultsThe nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. ConclusionsA CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.

ERVK13-1/miR-873-5p/GNMT Axis Promotes Metastatic Potential in Human Bladder Cancer though Sarcosine Production.

N-methyl-glycine (sarcosine) is known to promote metastatic potential in some cancers; however, its effects on bladder cancer are unclear. T24 cells derived from invasive cancer highly expressed GNMT, and S-adenosyl methionine (SAM) treatment increased sarcosine production, promoting proliferation, invasion, anti-apoptotic survival, sphere formation, and drug resistance. In contrast, RT4 cells derived from non-invasive cancers expressed low GNMT, and SAM treatment did not produce sarcosine and did not promote malignant phenotypes. In T24 cells, the expression of miR-873-5p, which suppresses GNMT expression, was suppressed, and the expression of ERVK13-1, which sponges miR-873-5p, was increased. The growth of subcutaneous tumors, lung metastasis, and intratumoral GNMT expression in SAM-treated nude mice was suppressed in T24 cells with ERVK13-1 knockdown but promoted in RT4 cells treated with miR-873-5p inhibitor. An increase in mouse urinary sarcosine levels was observed to correlate with tumor weight. Immunostaining of 86 human bladder cancer cases showed that GNMT expression was higher in cases with muscle invasion and metastasis. Additionally, urinary sarcosine concentrations increased in cases of muscle invasion. Notably, urinary sarcosine concentration may serve as a marker for muscle invasion in bladder cancer; however, further investigation is necessitated.

Detecting Muscle Invasion of Bladder Cancer: An Application of Diffusion Kurtosis Imaging Ratio and Vesical Imaging-Reporting and Data System.

Independent factors are needed to supplement vesical imaging-reporting and data system (VI-RADS) to improve its ability to identify muscle invasive bladder cancer (MIBC). To assess the correlation between MIBC and diffusion kurtosis imaging (DKI) ratio, VI-RADS, and other factors (such as tumor location). Retrospective. Sixty-eight patients (50 males and 18 females; age: 70.1 ± 9.5 years) with bladder urothelial carcinoma. 1.5 T, conventional diffusion-weighted imaging (DWI), and DKI (single shot echo-planar sequence). Three radiologists independently measured the diffusion parameters of each bladder cancer (BCa) and obturator internus, including the mean apparent diffusion coefficient (ADCmean), mean kurtosis (MK), and mean diffusion (MD). And the ratio of diffusion parameters between BCa and obturator internus was calculated (diffusion parameter ratio = bladder cancer:obturator internus). Based on the VI-RADS, the target lesions were independently scored. Furthermore, the actual tumor-wall contact length (ACTCL) and absolute tumor-wall contact length (ABTCL) were measured. Multicollinearity among independent variables was evaluated using the variance inflation factor (VIF). Multivariable logistic regression analysis was used to determine the independent risk factors of MIBC. The receiver operating characteristic curve was used to evaluate the efficacy of each variable in detecting MIBC. The DeLong test was used to compare the area under the curve (AUC). A P < 0.05 was considered statistically significant. MKratio (median: 0.62) and VI-RADS were independent risk factors for MIBC. AUCs for MKratio, VI-RADS, and MKratio combined with VI-RADS in assessing MIBC were 0.895, 0.871, and 0.973, respectively. MKratio combined with VI-RADS was more effective in diagnosing MIBC than VI-RADS alone. MKratio has potential to assist the assessment of MIBC. MKratio can be used as a supplement to VI-RADS for detecting MIBC. 4 TECHNICAL EFFICACY: Stage 2.

229P The application of diffusion kurtosis imaging in predicting muscle invasion of bladder cancer: A comparison with conventional DWI

229P The application of diffusion kurtosis imaging in predicting muscle invasion of bladder cancer: A comparison with conventional DWI

Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles?

The presence of muscle invasion is an important factor in establishing a treatment strategy for bladder cancer (BCa).The aim of this study is to reveal the diagnostic performance of radiomic shape features in predicting muscle-invasive BCa. In this study, 60 patients with histologically proven BCa who underwent a preoperative MRI were retrospectively recruited. The whole tumor volume was segmented on apparent diffusion coefficient (ADC) maps and T2W images. Afterward, the shape features of the volume of interest were extracted using PyRadiomics. Machine learning classification was performed using statistically different shape features in MATLAB® (The MathWorks, Inc., Natick, Massachusetts, United States). The findings revealed that 27 bladder cancer patients had muscle invasion, while 33 had superficial bladder cancer (53 men and seven women; mean age: 62±14). Surface area, volume, and relevant features were significantly greater in the invasive group than in the non-invasive group based on the ADC maps (P<0.05). Superficial bladder cancer had a more spherical form compared to invasive bladder cancer (P=0.05) with both imaging modalities. Flatness and elongation did not differ significantly between groups with either modality (P>0.05). Logistic regression had the highest accuracy of 83.3% (sensitivity 82.8%, specificity 84%) in assessing invasion based on the shape features of ADC maps, while K-nearest neighbors had the highest accuracy of 78.2% (sensitivity 79.1%, specificity 69.4%) in assessing invasion based on T2W images. Shape features can be helpful in predicting muscle invasion in bladder cancer using machine learning methods.

Preoperative CT-based radiomics for diagnosing muscle invasion of bladder cancer

BackgroundThe primary objective of the research was to develop a method using radiomics-based computed tomography (CT) to predict muscle invasion in bladder cancer (BCa) before surgery.MethodsA total of 269 patients with bladder cancer were divided into two groups; training group (n = 188 cases) and validation group (n = 81 cases). Radiomics characteristics were determined by analyzing the CT images of each patient. The least absolute shrinkage and selection operator (LASSO) technique was used for developing a radiomics signature. Furthermore, logistic regression (LR), the support vector machine (SVM), decision tree (DT), and Artificial Neural Network (ANN) models were applied to differentiate between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Their performance was determined using the area under the receiver operating characteristic curve (AUC-ROC). In addition, accuracy, specificity, and sensitivity evaluations were also conducted.ResultsThe radiomics signature was found to be successful in its prediction. A total of 1036 radiomics features were found in the 269 patients, and out of those, 16 were selected as the best predictors of radiomics features. The results revealed that the ANN classifier had the best performance, with a validation set accuracy of 0.950.ConclusionsThe current work used machine learning and radiomics techniques to successfully construct a prediction model for muscle invasion in bladder cancer. The ANN model produced significant outcomes that may be used in clinical diagnosis or therapy.

Integrating radiomics with the vesical imaging-reporting and data system to predict muscle invasion of bladder cancer.

To develop predictive models based on the integration of radiomics with the Vesical Imaging-Reporting and Data System (VI-RADS) for determining muscle invasion of bladder cancer. One hundred ninety-one patients were retrospectively included in this study from January 2015 to March 2022. Of these, 121 data were randomly divided into training and validation sets at a ratio of 7:3. The remaining data (n = 70) served as the independent testing set. The radiomics features were extracted from bladder cancer on high-b-value DWI images. The pipelines of radiomics models were trained in the training set. One optimal model was selected based on the performance in the validation set. Then, the selected model was tested in the independent testing set. Two radiologists evaluated the VI-RADS based on T2WI and DWI. Reader 1 was an experienced reader, and Reader 2 was an inexperienced reader. A clinical-radiomics model was built by integrating the radiomics signature and VI-RADS. The performance was assessed using receiver operating characteristic curve analysis. The histopathological results were used as the standard reference to assess the diagnostic accuracy of muscle invasion. The radiomics model had area under the curve (AUC) values of 0.801, 0.867, and 0.806 in the training, validation, and testing sets, respectively. The VI-RADS scores of Readers 1/2 yielded AUC values of 0.831/0.781, 0.909/0.815, and 0.871/0.776 in the training, validation, and testing sets, respectively. The clinical-radiomics model for Readers 1/2 revealed AUC values of 0.889/0.854, 0.961/0.919, and 0.881/0.844 in the training, validation, and testing sets, respectively. The performance of the clinical-radiomics model was improved compared to the VI-RADS score for inexperienced Reader 2 (P < 0.05). The radiomics model was useful in the diagnosis of muscle invasion of bladder cancer. The clinical-radiomics model integrating radiomics and VI-RADS further improved the performance compared to VI-RADS alone, which was helpful for readers with less diagnostic experience.

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning

Background and objectivesRadiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). MethodsA total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. ResultsThe area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. ConclusionsThe T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.

Diagnostic performance of multiparametric MRI based Vesical Imaging-Reporting and Data System (VI-RADS) scoring in discriminating between non-muscle invasive and muscle invasive bladder cancer.

The purpose of the present study was to assess the diagnostic accuracy of the Vesical Imaging-Reporting and Data System (VI-RADS) scoring system in predicting muscle infiltration of bladder cancer (BC) on a pre-operative multiparametric magnetic resonance imaging (mpMRI). The prospective study enrolled patients with bladder lesions detected on a preliminary ultrasonography or cystoscopy. The patients underwent mpMRI on a 3T MRI scanner followed by surgery within 2 weeks. The tumours were assigned a VI-RADS score by 2 experienced abdominal radiologists. The VI-RADS score was compared with postoperative histopathological findings to confirm detrusor muscle infiltration. The diagnostic performance of VI-RADS for predicting muscle invasion was assessed by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. A total of 60 patients were included in the study with a male: female ratio of 4.4 : 1. Transurethral resection of bladder tumour (TURBT) was performed in 47 (78.4%) and radical cystectomy in 13 (21.6%) patients. 19 (31.7%) had non-muscle invasive invasive BC (NMIBCa) and 41 (68.3%) had muscle invasive BC (MIBCa) on histopathology. There was a significant association between VI-RADS score and its components with muscle invasion (p < 0.05). A VI-RADS score of ≥ 3 had a sensitivity of 97.56% (95% CI: 0.87-0.99%), specificity of 73.68% (95% CI: 0.49-0.91), positive predictive value of 88.9% (95% CI: 0.79-0.94), negative predictive value of 93.33% (95% CI: 0.66-0.99), and diagnostic accuracy of 90% (95% CI: 0.80-0.96) for prediction of muscle invasion. VI-RADS scoring system pre-operatively predicts the likelihood of muscle invasion in BC with a satisfactory diagnostic performance, and it should be incorporated in the diagnostic work-up of BC patients.

Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.

To compare the diagnostic performance of a novel deep learning (DL) method based on T2-weighted imaging with the vesical imaging-reporting and data system (VI-RADS) in predicting muscle invasion in bladder cancer (MIBC). A total of 215 tumours (129 for training and 31 for internal validation, centre 1; 55 for external validation, centre 2) were included. MIBC was confirmed by pathological examination. VI-RADS scores were provided by two groups of radiologists (readers 1 and readers 2) independently. A deep convolutional neural network was constructed in the training set, and validation was conducted on the internal and external validation sets. ROC analysis was performed to evaluate the performance for MIBC diagnosis. The AUCs of the DL model, readers 1, and readers 2 were as follows: in the internal validation set, 0.963, 0.843, and 0.852, respectively; in the external validation set, 0.861, 0.808, and 0.876, respectively. The accuracy of the DL model in the tumours scored VI-RADS 2 or 3 was higher than that of radiologists in the external validation set: for readers 1, 0.886 vs. 0.600, p = 0.006; for readers 2, 0.879 vs. 0.636, p = 0.021. The average processing time (38 s and 43 s in two validation sets) of the DL method was much shorter than the readers, with a reduction of over 100 s in both validation sets. Compared to radiologists using VI-RADS, the DL method had a better diagnostic performance, shorter processing time, and robust generalisability, indicating good potential for diagnosing MIBC. • The DL model shows robust performance for MIBC diagnosis in both internal and external validation. • The diagnostic performance of the DL model in the tumours scored VI-RADS 2 or 3 is better than that obtained by radiologists using VI-RADS. • The DL method shows potential in the preoperative assessment of MIBC.

Multi-Sequence and Multi-Regional MRI-Based Radiomics Nomogram for the Preoperative Assessment of Muscle Invasion in Bladder Cancer.

Whether bladder cancer (BCa) invades muscle is a determinant of management. However, the accuracy of preoperative diagnosis of muscle invasion is not satisfactory. To investigate the value of multi-sequence and multi-regional magnetic resonance imaging (MRI)-based radiomics nomogram for assessing muscle invasion of BCa. Retrospective. 342 BCa patients, divided into a training set (239 cases), a validation set (68 cases), and a test set (35 cases). 3.0 T/T2 -weighted image, diffusion-weighted imaging, and dynamic contrast-enhanced imaging. Patients were divided into muscle-invasive (79 cases) and non-muscle-invasive (263 cases). Two radiologists delineated the whole tumor, tumor body, and muscle layer of BCa, respectively, and extracted radiomic features. Recursive feature elimination, Pearson correlation coefficient, logistic regression, least absolute shrinkage and selection operator (Lasso) regression analysis, and 5-fold cross-validation were used to screen features and build a radiomics model. The clinical data were collected to construct a clinical model and a radiomics-clinical nomogram. 23,688 features were extracted. After screening, the radiomics scoring model was constructed using nine radiomics features with area under curve (AUC) values of 0.933, 0.913, and 0.931 in the training, validation, and test sets, respectively. The clinical model was constructed using five clinical independent risk factors; the AUC values in the training, validation, and test set were 0.876, 0.859, and 0.824, respectively. After logistic regression analysis, the AUC values of the radiomics-clinical nomogram were made up of four clinical independent risk factors and radiomics scores were 0.955, 0.922, and 0.935 for the training, validation, and test sets, respectively. The DeLong test between clinical model and radiomics-clinical nomogram shows P < 0.001. Multi-sequence and multi-regional MRI-based radiomics models could effectively assess the state of BCa muscular invasion. The radiomics-clinical nomogram is superior to clinical model for assessing BCa muscular invasion. 4 TECHNICAL EFFICACY: Stage 2.

Application of Vesical Imaging-Reporting and Data System in Evaluation of Urinary Bladder Cancer Using Multiparametric Magnetic Resonance Imaging: A Hospital-Based Cross-Sectional Study.

Background  Multiparametric magnetic resonance imaging (mp-MRI) of urinary bladder (UB) is a novel imaging to predict detrusor muscle invasion in Bladder cancer (BC). The Vesical Imaging-Reporting and Data System (VI-RADS) was introduced in 2018 to standardize the reporting of BC with mp-MRI and to diagnose muscle invasion. This study was performed to evaluate the role of mp-MRI using VI-RADS to predict muscle invasive BC. Methods  This prospective study was carried from June 2020 to May 2021 in a tertiary care institute. Thirty-six patients with untreated BC underwent mp-MRI followed by transuretheral resection of the tumor (TURBT). Mp-MRI findings were evaluated by two radiologists and BC was categorized according to VI-RADS scoring system. Resected tumors along with separate biopsy from the base were reported by two pathologists. Histopathological findings were compared with VI-RADS score and the performance of VI-RADS for determining detrusor muscle invasion was analyzed. Results  VI-RADS scores of 4 and 5 were assigned to 9 (25%) and 15 (41.7%) cases, respectively, while 4 (13.3%) cases had VI-RADS score 3 on mp-MRI. VI-RADS 1 and 2 lesions were observed in six (16.7%) and two (5.5%) cases, respectively. On histopathology, 23 cases (63.9%) had muscle-invasive cancer and 13 cases (36.1%) had non-muscle-invasive cancer. The sensitivity and diagnostic accuracy of mp-MRI in predicting muscle invasive BC was 95.6 and 80.6%, respectively. Conclusion  Mp-MRI has high sensitivity and diagnostic accuracy in predicting muscle invasive BC and should be advocated for evaluation of BC prior to surgery.

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.

CT-based radiomics to predict muscle invasion in bladder cancer

This study investigated the feasibility of a computed tomography (CT)-based radiomics prediction model to evaluate muscle invasive status in bladder cancer. Patients who underwent CT urography at two medical centers from October 2014 to May 2020 and had bladder urothelial carcinoma confirmed by postoperative histopathology were retrospectively enrolled. In total, 441 cases were collected and randomized into a training cohort (n= 293), an internal testing cohort (n= 73), and an external testing cohort (n= 75). The images were first filtered, and then, 1218 features were extracted. The best features related to muscle invasiveness of bladder cancer were identified by ANOVA. A prediction model was built by using the logistic regression method. Statistical analysis was performed by plotting the receiver operating characteristic curve. Indicators of the diagnostic performance of the prediction model, including sensitivity, specificity, accuracy, and area under curve (AUC), were evaluated. In the training, internal testing, and external testing cohorts, the prediction model diagnosed muscle-invasive bladder cancer with AUCs of 0.885 (95% confidence interval [95% CI] 0.841-0.929), 0.820 (95% CI 0.698-0.941), and 0.784 (95% CI 0.674-0.893), respectively. In the internal testing cohort, the sensitivity, specificity, and accuracy of the model were 0.667 (95% CI 0.387-0.870), 0.845 (95% CI 0.721-0.922), and 0.782 (95% CI 0.729-0.827), respectively. In the external testing cohort, the sensitivity, specificity, and accuracy of the model were 0.742 (95% CI 0.551-0.873), 0.750 (95% CI 0.594-0.863), and 0.782 (95% CI 0.729-0.827), respectively. CT-based radiomics prediction model can evaluate muscle invasiveness of bladder cancer before surgery with a good diagnostic performance. • CT-based radiomics model can evaluate muscle invasive status in bladder cancer. • The radiomics model shows good diagnostic performance to differentiate muscle-invasive bladder cancer from non-muscle-invasive bladder cancer. • This preoperative CT-based prediction method might complement MR evaluation of bladder cancer and supplement biopsy.

Visual MRI T-category versus VI-RADS evaluation from multiparametric MRI in the detection of muscle-invasion in patients with suspected bladder cancer: single centre registered clinical trial (MIB-trial)

Background Multiparametric Magnetic Resonance Imaging (mpMRI) has been proposed to add value in the diagnostic pathway of bladder cancer (BC). We wanted to evaluate the performance of mpMRI for muscle-invasion detection in BC patients using a subjective MRI visual T-category and the Vesical Imaging-Reporting and Data System (VI-RADS) score. Methods This single centre clinical trial included 45 patients with suspected BC (ClinicalTrials.gov Identifier: NCT02662166). All patients had mpMRI prior to transurethral resection of bladder tumour (TUR-BT). The imaging was correlated to histopathological findings. Two individual radiologists evaluated all the mpMRI images. A binary cut-off point for the detection of muscle-invasion in the MRI visual T-category was defined between T1 and T2 and the VI-RADS cut-off score was 3. Cohen’s Kappa values were used to evaluate the agreement between the two radiologists. Sensitivity, Specificity, Area Under Receiver Operator Characteristics Curve (AUC), Positive Predictive Value (PPV) and Negative Predictive Value (NPV) were calculated to evaluate the performance of both radiologists separately. Results AUC values for reader A and B using the MRI visual T-category were 0.76 and 0.56, while the corresponding values for VI-RADS were 0.63 and 0.57, respectively. There was no statistically significant difference between the radiologists nor the reporting systems (p > .05) in the detection of muscle-invasion. The inter-reader agreement was substantial (0.61–0.80). Conclusion Both the subjective MRI visual T-category and VI-RADS score had only a low to moderate accuracy for the detection of muscle-invasion in BC with no statistically significant difference between the reporting systems.

Preliminary Exploration of the Application of Vesical Imaging‐Reporting and Data System (VI‐RADS) in Post‐treatment Patients With Bladder Cancer: A Prospective Single‐Center Study

Vesical Imaging-Reporting and Data System (VI-RADS) has been shown to be effective in diagnosing muscle invasion of bladder cancer (BC) in primary patients. To evaluate the diagnostic efficacy of VI-RADS in a BC target population which included post-treatment patients, and to determine the repeatability. Prospective. Seventy-three patients (42 with primary BC, 31 with post-treatment BC). 3.0 T MRI with propeller fast spin-echo T2 WI, echo planer imaging diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCEI). VI-RADS scores were independently assessed by five radiologists with different levels of experience. The diagnostic efficiency in each group (primary and post-treatment) and of each radiologist was assessed. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and area under the curve (AUC) in receiver operating characteristic curve analysis were calculated to evaluate VI-RADS diagnostic performance. Interobserver agreement was assessed using weighted Kappa statistics. A P value <0.05 was considered statistically significant. At the corresponding cut-off, AUC values of three groups range from 0.936 to 0.947 and AUC values of five observers range from 0.901 to 0.963. There was no significant difference between the AUCs in the primary and post-treatment groups (P= 0.870). The cut-off of the whole group and the post-treatment group was ≥4, and the cut-off of the primary group was ≥3. The Kappa values of interobserver agreements range from 0.709 to 0.923. After expanding the target population to include post-treatment patients, VI-RADS still has good diagnostic efficacy and repeatability. VI-RADS could potentially be a preoperative staging tool for post-treatment patients. 1 TECHNICAL EFFICACY STAGE: 2.