Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System)

Panebianco, Valeria; Narumi, Yasuo; Altun, Ersan; Bochner, Bernard H.; Efstathiou, Jason A.; Hafeez, S.; Huddart, Robert; Kennish, Steve; Lerner, Seth P.; Montironi, Rodolfo; Muglia, Valdair Francisco; Salomon, Georg; Thomas, Stephen; Vargas, Hebert Alberto; Witjes, J. Alfred; Takeuchi, Mitsuru; Barentsz, Jelle O.; Catto, James W.F.

doi:10.1016/j.eururo.2018.04.029

Cited by 391 publications

(364 citation statements)

References 86 publications

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“…6 Therefore, preoperative prediction of the recurrence risk in TFTY after operation is very crucial for individualized follow-up strategies of BCa patients. Previous studies 14,15,25,34 have indicated that: 1) postoperative DW and DCE images were capable of identifying tumor recurrence from chronic inflammation and fibrosis after TURBT or cystectomy, 2) mpMRI, including T 2 W, DW, ADC, and DCE images, might be a complementary and potential alternative of cystoscopy for posttreatment follow-up and recurrence surveillance. Whether the preoperative mpMRI with the radiomics strategy is capable of predicting BCa recurrence remains unknown to now.…”

Section: Discussionmentioning

confidence: 99%

“…Based on these findings above, we hypothesized that: 1) the radiomics features extracted from preoperative mpMRI to characterize the subtle variations of tissue distribution within the lesion might be potential in predicting BCa recurrence; 2) the combination of the radiomics strategy with important clinical factors, mainly including age, gender, histological grade, and MIS of the archived tumor with the maximal size in bladder lumen, tumor size, NoT, operation choice, together with the imaging signs like stalk and SLE, might add the incremental value for TFTY BCa prediction.…”

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confidence: 99%

“…In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, [16][17][18] as well as predicting mental disorders schizophrenia. 19 Based on these findings above, we hypothesized that: 1) the radiomics features extracted from preoperative mpMRI to characterize the subtle variations of tissue distribution within the lesion [20][21][22] might be potential in predicting BCa recurrence; 2) the combination of the radiomics strategy with important clinical factors, mainly including age, gender, histological grade, and MIS of the archived tumor with the maximal size in bladder lumen, tumor size, NoT, operation choice, together with the imaging signs like stalk and SLE, 13,[23][24][25] might add the incremental value for TFTY BCa prediction.…”

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confidence: 99%

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A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

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Background Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type Retrospective. Population Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W), multi‐b‐value diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics features were extracted from the T2W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine‐based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle‐invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics‐clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. Statistical Tests Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics‐clinical nomogram than using the radiomics or clinical model alone. Data Conclusion The proposed radiomics‐clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893–1904.

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Section: Discussionmentioning

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A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

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“…Although several articles exists supporting the role of mpMRI in patients with bladder cancer [10,11], the first description of the VI-RADS score was performed by Panebianco et al [3] in 2018. A multidisciplinary team composed of members from Europe, North America, South America and Asia with a Delphi-like consensus protocol proposed a 5-point score.…”

Section: The Development Of Vi-radsmentioning

confidence: 99%

The effectiveness of multiparametric magnetic resonance imaging in bladder cancer (Vesical Imaging-Reporting and Data System): A systematic review

Carando

Afferi

Marra

et al. 2020

Arab Journal of Urology

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2020) The effectiveness of multiparametric magnetic resonance imaging in bladder cancer (Vesical Imaging-Reporting and Data System): A systematic review, ABSTRACT Objective: To evaluate the role of the Vesical Imaging-Reporting and Data System (VI-RADS) score in the diagnostic pathway of bladder cancer. Methods: A systemic search of the contemporary literature was performed in December 2019 using the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica dataBASE (EMBASE), and Web of Science databases focussing on all available articles on VI-RADS.Results: Overall, six of 15 articles were included. All the available articles evaluated the ability of radiologists to use the VI-RADS score for discriminating non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC). Considering a cut-off VI-RADS score of >2, the sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were 78-91.9%, 85-91%.1, 69-78%, and 88-97.1%, respectively. Considering a VI-RADS score cut-off of >3, the sensitivity, specificity, PPV and NPV were 77-94.6%, 43.9-96.5%, 51.6-86%, and 63.7-93%, respectively. Good interobserver agreement was demonstrated in the evaluated studies with a κ score of 0.73-0.89. Only one study evaluated the utility of VI-RADS in determining the presence of MIBC in patients treated with transurethral resection of the bladder diagnosed with high-grade T1 before the second transurethral resection using a VI-RADS score cut-off of >2; the sensitivity, specificity, PPV and NPV were 85%, 93.6%, 74.5%, and 96.6%, respectively. Conclusion: The VI-RADS score, using multiparametric magnetic resonance imaging, showed excellent results in discriminating MIBC from NMIBC. Preliminary results have been reported for its use in patients with high-grade T1 bladder cancer. These results need to be validated in high-quality real-world settings.Abbreviations: DCE: dynamic contrast enhancement; DWI: diffusion-weighted imaging; (N) MIBC: (non-)muscle-invasive bladder cancer; mpMRI: multiparametric MRI; TURBT: transurethral resection of bladder tumour; (N)(P)PV: (negative) (positive) predictive value; SC: structural category; T2W: T2-weighted; VI-RADS: vesical imaging-reporting and data system ARTICLE HISTORY

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“…Magnetic resonance imaging (MRI) techniques have currently shown potential in determining tumor locations and characteristics in a noninvasive manner . Quantitative characterization of these images to build the associated bridge between image signatures and cancer properties, ie, radiomics, has attracted increasing attention .…”

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confidence: 99%

Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis

Zhang

Tian

et al. 2018

Magnetic Resonance Imaging

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Background Preoperative discrimination between nonmuscle‐invasive bladder carcinomas (NMIBC) and the muscle‐invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). Purpose To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. Study Type Retrospective, radiomics. Population Fifty‐four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W) and multi‐b‐value diffusion‐weighted (DW) sequences. Assessment A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM‐RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. Statistical Tests Chi‐square test and Student's t‐test were applied on clinical characteristics to analyze the significant differences between patient groups. Results Of the 1104 features, an optimal subset involving 19 features was selected from T2W and DW sequences, which outperformed the other two subsets selected from T2W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM‐RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. Data Conclusion The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2W sequence or DW sequence only. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489–1498.

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Multiparametric Magnetic Resonance Imaging for Bladder Cancer: Development of VI-RADS (Vesical Imaging-Reporting And Data System)

Cited by 391 publications

References 86 publications

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

The effectiveness of multiparametric magnetic resonance imaging in bladder cancer (Vesical Imaging-Reporting and Data System): A systematic review

Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis

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