Simplified Integration of ADC Quantification Enhances Biparametric and Multiparametric VI-RADS for Detrusor Muscle Invasion Prediction in Bladder Cancer

Simplified Integration of ADC Quantification Enhances Biparametric and Multiparametric VI-RADS for Detrusor Muscle Invasion Prediction in Bladder Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Simplified Integration of ADC Quantification Enhances Biparametric and Multiparametric VI-RADS for Detrusor Muscle Invasion Prediction in Bladder Cancer Merve Nur Tasdemir, Uluhan Eryuruk, Esra Ibis, Serdar Aslan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7733624/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To assess the diagnostic performance of an adjusted VI-RADS (adj-VI-RADS), combining ADC measurements with biparametric(bp)/multiparametric (mp) magnetic resonance imaging (MRI), for predicting detrusor muscle invasion in bladder cancer. Methods In this retrospective study, 184 patients underwent bladder mpMRI before TUR-B/cystectomy. Two radiologists independently evaluated the images. T2-Weighted imaging, difussion weighted- imaging, and dynamic contrast enhanced images were scored according to the VI-RADS criteria. Whole-tumor (wADC) and normalized ADC (nADC) values were calculated. An optimal nADC cutoff for muscle invasion was determined by ROC analysis. Scores were upgraded if nADC was ≤ this cutoff, deriving the adj-VI-RADS. Results A total of 140 patients had NMIBC, while 42 had MIBC. Both readers found significantly lower mean wADC and nADC values in MIBC compared to NMIBC (e.g., Reader 1 nADC: 0.317 vs. 0.49; Reader 2 nADC: 0.39 vs. 0.524; all p < 0.001). The optimal nADC cutoff for predicting muscle invasion was 0.403. In ROC analysis, Reader 1 obtained the following area under the curve (AUC) values: baseline bp-VI-RADS: 0.84; baseline mp-VI-RADS: 0.92; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.94. For Reader 2, the corresponding values were as follows: baseline bp-VI-RADS: 0.82; baseline mp-VI-RADS: 0.85; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.90. For all models, a VI-RADS category ≥ 4 was the optimal cutoff for predicting muscle invasion. Conclusion Integrating nADC quantification via a standardized upgrade rule significantly enhances VI-RADS diagnostic precision for detecting muscle invasion in BC. Our contrast-free adj-bp-VI-RADS protocol achieves accuracy comparable to that of contrast-enhanced mp-VI-RADS, offering a safer and more cost-efficient alternative. Bladder cancer Apparent diffusion coefficient Muscle invasion Magnetic resonance imaging VI-RADS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Bladder cancer (BC) ranks as the 10th most prevalent and 13th most fatal malignancy worldwide [ 1 ]. It is essential to differentiate accurately between muscle-invasive (MIBC) and nonmuscle-invasive bladder cancer (NMIBC), as involvement of the muscle layer plays a crucial role in determining bladder-sparing treatment strategies. MIBC (T2-T4) is usually treated with aggressive interventions, including cystectomy, systemic treatment, or a combination of the two, and has a poor prognosis. NMIBC (Ta-T1) is characterized by a nonaggressive demeanor and low-grade characteristics [ 2 ]. Due to the high complication rate and poor quality of life associated with radical cystectomy, a thorough preoperative assessment of the need for this procedure is important. Cystoscopy and evaluation of pathological tissue obtained by transurethral resection of bladder tumor (TURBT) are the recommended diagnostic methods. However, diagnostic TURBT is associated with a significant risk of serious complications, including bladder perforation [ 3 ]. Due to its noninvasive nature and superior soft tissue contrast, magnetic resonance imaging (MRI) holds significant promise in the assessment of MIBC [ 4 ]. The Vesical Imaging Reporting and Data System (VI-RADS) is a standardized procedure for the evaluation of MRI findings in BC, with an emphasis on the diagnosis of muscle invasion [ 5 ]. VI-RADS was introduced in 2018, and several studies, including prospective ones, have confirmed its clinical relevance and efficacy [ 6 ]. However, VI-RADS has a potential limitation because it includes dynamic contrast-enhanced imaging (DCE). The rare adverse effects of gadolinium-based contrast agents following intravenous injection include allergic reactions, nephrogenic systemic fibrosis, renal failure, and gadolinium accumulation in the brain [ 7 – 8 ]. Therefore, there is a clinical need to develop a reporting model that does not use DCEI. Biparametric MRI (bp-MRI; T2-weighted + diffusion-weighted imaging [DWI]) protocols have emerged, with studies suggesting accuracy comparable to that of multiparametric MRI (mp-MRI) for VI-RADS-based MIBC detection [ 9 , 10 ]. Existing versions of the VI-RADS scoring system are limited by the lack of objective and reproducible quantitative parameters. As a noninvasive imaging technique, DWI enables the quantification of water molecule motion within tissues and provides objective measurements through the apparent diffusion coefficient (ADC) [ 11 ]. The efficacy of ADC values in identifying the histological grade of BC has been a subject of research [ 12 ]. In addition, recent studies indicate its potential to augment VI-RADS. For instance, combining ADC with mp-MRI VI-RADS improved MIBC diagnosis [ 13 , 14 ]. Zhang et al. [ 11 ] demonstrated similar benefits for bp-MRI VI-RADS. However, existing approaches often employ complex multiparametric models or lack standardization in ADC measurement (e.g., single vs. multiple regions of interest [ROIs]), limiting clinical practicality. Acknowledging these practical challenges, we propose a simplified integration strategy using a single normalized ADC (nADC) value—calculated from whole-tumor ADC normalized to bladder-lumen ADC—to refine VI-RADS scoring via an objective upgrade rule. We hypothesize that this approach enhances the diagnostic performance of both bp- and mp-VI-RADS. Specifically, patients with nADC values below an optimal threshold would receive an adjusted VI-RADS (adj-VI-RADS) score upgrade, resolving ambiguity in equivocal cases (e.g., VI-RADS 3) while minimizing gadolinium dependence. In this study, we aim to assess the diagnostic performance of an adjusted VI-RADS (adj-VI-RADS), derived from a combination of ADC measurements and bp/mp-MRI, in predicting detrusor muscle invasion in BC. Methods This retrospective investigation was approved by the institutional ethics board, and informed consent was relinquished (****/BAEK-442/2025/04). The study protocol aligned with the ethical standards of the 1975 Declaration of Helsinki. Study group Between June 2020 and June 2025, 208 patients who underwent TUR-B and/or radical cystectomy for BC with mpMRI performed within three months prior were identified. Patients were selected for the study according to the following criteria 1.MRI assessment included the required sequences for mp-MRI protocols. 2.MRI was performed within two weeks prior to TUR bladder or cystectomy. 3.Bladder urothelial carcinoma was pathologically proven. Patients were excluded from the study according to the following criteria: 1. Patients with ADC images of poor or nonvisible quality. 2. Patients with tumours measuring less than 1 cm. 3. Patients with different histopathological variants of bladder cancer. 4. Patients with hyperintense urine in the bladder lumen on the T1 sequence (which may confound bladder lumen ADC normalization) [15]. Figure 1 shows the patient selection. Image acquisition A 1.5-T MRI system (Magnetom Aera, Siemens Medical Solutions, Erlangen, Germany) was utilised for MRI examinations. Prior to the procedure, ultrasonography was performed to ensure that the patients had adequate bladder distension. Imaging was performed in a supine position with the utilisation of a pelvic phased-array coil. The following types of images were acquired: T1-weighted images (T1-WI), axial, coronal, and sagittal fast spin-echo T2-WI, dynamic contrast-enhanced images with three-dimensional high temporal resolution, and diffusion-weighted images with b-values of 0, 800, and 1200 s/mm2. Gadopentetate dimeglumine (Gadovist, 0.2 mL per kilogram of body weight; Bayer Healthcare, Berlin, Germany) was delivered via a power injector at a rate of 2 mL per second, followed by a further infusion of 20 mL of normal saline. Following the injection of the intravenous contrast agent, axial DCE images were captured in post-contrast phases with no gap between them. Image analysis and VI-RADS evaluation All MRI scans were transferred to the picture archiving and communication system. Two radiologists with different levels of experience independently evaluated the images without reference to histopathology. Reader 1 had recently qualified as a specialist in radiology, while Reader 2 was a radiology resident with four years of training. Two sets were created: Set 1 included T2WI and DWI, while Set 2 included T2WI, DWI, and DCE. These sets were evaluated one month apart to avoid recall bias. Both readers were blinded and had no access to patient demographics or surgical information. T2WI, DWI, and DCE were scored according to the VI-RADS criteria for each index lesion. On T2WI, the integrity of the detrusor muscle, seen as a continuous band of low signal intensity, was evaluated. On DWI and corresponding ADC maps, invasion was characterized by the presence of restricted diffusion extending into or beyond the muscle layer. On DCE imaging, early, vivid enhancement of the tumor extending into the muscularis propria or perivesical fat was the criterion for a score indicating invasion. [5]. Scores for the bp- and mp-VI-RADS categories were then obtained for these lesions. The slice exhibiting the largest tumor area was selected, and free-hand ROIs were placed on it (Fig. 2). The whole tumor ADC (wADC) was calculated [10]. A 20 mm 2 ROI was placed in the bladder lumen, away from urinary flow turbulence or artifacts. nADC was calculated by dividing the tumor ADC by the bladder lumen ADC [12, 16]. The optimal nADC threshold for distinguishing muscle invasion was determined via ROC analysis using pooled data from both readers. When nADC values were equal to or less than this cutoff, an upgrade rule was applied to both the bp-MRI and mp-MRI VI-RADS scores to obtain the adj-VI-RADS score. Statistical analysis Statistical analyses were conducted utilizing SPSS Statistics version 25 (IBM Corporation, Armonk, NY, USA). Continuous data are presented as mean ± standard deviation (SD) and were subjected to the Wilcoxon signed-rank test. Categorical variables are expressed as frequency counts and percentages, analyzed via Fisher's exact test. The diagnostic performance of both bp/mp VI-RADS and the adj-VIRADS for detecting muscle invasion was evaluated per reader using sensitivity, specificity, accuracy, and area under the curve (AUC). We evaluated uncertainty in model AUC differences (ΔAUC = AUC adjusted − AUC baseline ) through 1000 paired bootstrap replicates, maintaining within-case correlation structures. Percentile-based 95% CIs and two-tailed p-values are reported. Clinical applicability of adjusted bp/mp-VI-RADS was evaluated via decision curve analysis. The intraclass correlation coefficient (ICC) was used to assess inter-reader reliability for wADC, nADC, bp-/mp-VI-RADS and adj-bp-/mp- VI-RADS scores. Statistical significance was defined as a two-sided p-value < 0.05. All analyses incorporated 95% confidence intervals (CIs). Results A total of 208 patients with histopathologically confirmed BC were initially identified. Of these, two were diagnosed with alternative histological subtypes of bladder carcinoma. Twelve patients presented with lesions smaller than 1 cm, 10 exhibited poor-quality or nondiagnostic ADC images, and two demonstrated hyperintense urine within the bladder lumen on T1-weighted imaging. These 26 individuals were excluded from the analysis, resulting in a final study cohort of 182 patients (167 males; median age 72 years). A total of 140 patients had NMIBC, while 42 had MIBC. Table 1 summarizes the demographic and pathological characteristics of the study population. For Reader 1, NMIBC demonstrated a mean wADC of 1,055 × 10⁻⁶ mm²/s and a mean nADC of 0.49 ( p < 0.001), whereas MIBC showed significantly lower values (wADC: 710 × 10⁻⁶ mm²/s; nADC: 0.317; p < 0.001). For Reader 2, NMIBC yielded a mean wADC of 1,234 × 10⁻⁶ mm²/s and a mean nADC of 0.524 ( p < 0.001), whereas MIBC exhibited reduced values (wADC: 913 × 10⁻⁶ mm²/s; nADC: 0.39; p < 0.001). Table 1 Demographic and pathological characteristics of the study population Variable n (%)/mean ± SD Age (years) 72.0 ± 10.6 Maximum diameter (mm) 27.6 ± 20.1 Gender Male 167 (91.8%) Female 15 (8.2%) Stage Ta 75 (41.2%) T1 65 (35.7%) T2 36 (19.7%) T3 5 (2.7%) T4 1 (0.7%) Muscle Invasion NMIBC 140 (76.9%) MBIC 42 (23.1%) Grade Low 69 (37.9%) High 113 (62.1%) NMIBC, non-muscle invasive bladder cancer; MIBC, muscle invasive bladder cancer. Prediction of muscle invasion in bladder cancer The optimal ADC cutoffs for predicting muscle invasion were a tumour ADC of 864 µm²/s and an nADC of 0.403. An upgrade criterion was applied to assign the adj-VI-RADS score when the nADC values were ≤ 0.403. In ROC analysis, Reader 1 obtained the following area under the curve (AUC) values: baseline bp-VI-RADS: 0.84; baseline mp-VI-RADS: 0.92; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.94. For Reader 2, the corresponding values were as follows: baseline bp-VI-RADS: 0.82; baseline mp-VI-RADS: 0.85; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.90 (Fig. 3 ). For both readers, a VI-RADS category ≥ 4 was the optimal cutoff for predicting muscle invasion using both baseline and adjusted VI-RADS models. The adjusted models demonstrated significantly improved discrimination over their respective baselines. For Reader 1, comparison of adj-bp-VI-RADS versus baseline bp-VI-RADS yielded a ΔAUC of 0.056 (95% CI: 0.018–0.098; p < 0.001), while adj-mp-VI-RADS versus baseline mp-VI-RADS showed a ΔAUC of 0.025 (95% CI: 0.001–0.058; p = 0.038). Similarly, for Reader 2, adj-bp-VI-RADS improved upon baseline bp-VI-RADS with a ΔAUC of 0.036 (95% CI: 0.007–0.069; p = 0.002), and adj-mp-VI-RADS surpassed baseline mp-VI-RADS with a ΔAUC of 0.030 (95% CI: 0.004–0.058; p = 0.004). Decision curve analysis revealed a superior net benefit for the adjusted VI-RADS protocol compared to both treat-all management and bp-/mp-VI-RADS across most threshold probabilities (Fig. 4 ). For Reader 1, the sensitivity and specificity of bp-VI-RADS 4 for detecting muscle invasion were 67% and 87%, respectively. With adj-bp-VI-RADS 4, sensitivity increased to 75%, while specificity decreased slightly to 86%. For Reader 2, the sensitivity and specificity of bp-VI-RADS 4 for detecting muscle invasion were 61% and 90%, respectively. With adj-bp-VI-RADS 4, sensitivity increased to 72%, while specificity remained unchanged at 90%. Table 2 (Reader 1) and Table 3 (Reader 2) present a summary of the diagnostic performance measures for the other VI-RADS scores. Table 2 Diagnostic performance of bp VI-RADS and adjusted bp VI-RADS for Reader 1 in predicting muscle invasive bladder cancer Bp-VIRADS Adj-Bp-VIRADS Cutoff Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy 1 1.0 (0.9-1.0) 0.0 (-0.0-0.03) 0.2 (0.15–0.26) 2 1.0 (0.9-1.0) 0.12 (0.08–0.19) 0.3 (0.24–0.37) 1.0 (0.9-1.0) 0.1 (0.06–0.16) 0.28 (0.22–0.35) 3 0.83 (0.68–0.92) 0.73 (0.66–0.8) 0.75 (0.69–0.81) 0.97 (0.86-1.0) 0.66 (0.58–0.73) 0.72 (0.65–0.78) 4 0.67 (0.5–0.8) 0.87 (0.81–0.92) 0.83 (0.77–0.88) 0.75 (0.59–0.86) 0.86 (0.8–0.91) 0.84 (0.78–0.89) 5 0.39 (0.25–0.55) 0.97 (0.92–0.99) 0.85 (0.79–0.9) 0.64 (0.48–0.78) 0.92 (0.87–0.96) 0.87 (0.81–0.91) 6 0.36 (0.22–0.52) 0.97 (0.93–0.99) 0.85 (0.79–0.9) Mp-VIRADS Adj-Mp-VIRADS Cutoff Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy 1 1.0 (0.9-1.0) 0.0 (-0.0-0.03) 0.2 (0.15–0.26) 2 1.0 (0.9-1.0) 0.13 (0.08–0.19) 0.3 (0.24–0.37) 1.0 (0.9-1.0) 0.11 (0.07–0.17) 0.29 (0.23–0.36) 3 0.94 (0.82–0.98) 0.73 (0.66–0.8) 0.77 (0.71–0.83) 1.0 (0.9-1.0) 0.67 (0.59–0.74) 0.74 (0.67–0.79) 4 0.89 (0.75–0.96) 0.89 (0.83–0.93) 0.89 (0.84–0.93) 0.94 (0.82–0.98) 0.88 (0.81–0.92) 0.89 (0.84–0.93) 5 0.42 (0.27–0.58) 0.97 (0.92–0.99) 0.86 (0.8–0.9) 0.75 (0.59–0.86) 0.92 (0.86–0.95) 0.88 (0.83–0.92) 6 0.39 (0.25–0.55) 0.97 (0.93–0.99) 0.86 (0.8–0.9) VI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; adj, adjusted. Table 3 Diagnostic performance of bp VI-RADS and adjusted bp VI-RADS for Reader 2 in predicting muscle invasive bladder cancer Bp-VIRADS Adj-Bp-VIRADS Cutoff Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy 1 1.0 (0.9-1.0) 0.0 (-0.0-0.03) 0.2 (0.15–0.26) 2 1.0 (0.9-1.0) 0.18 (0.12–0.25) 0.34 (0.28–0.41) 1.0 (0.9-1.0) 0.17 (0.12–0.24) 0.34 (0.27–0.41) 3 0.83 (0.68–0.92) 0.65 (0.57–0.72) 0.69 (0.62–0.75) 0.89 (0.75–0.96) 0.6 (0.52–0.68) 0.66 (0.59–0.72) 4 0.61 (0.45–0.75) 0.9 (0.85–0.94) 0.85 (0.79–0.89) 0.72 (0.56–0.84) 0.9 (0.84–0.94) 0.86 (0.81–0.91) 5 0.36 (0.22–0.52) 0.95 (0.9–0.97) 0.83 (0.77–0.88) 0.5 (0.34–0.66) 0.93 (0.88–0.96) 0.85 (0.79–0.89) 6 0.28 (0.16–0.44) 0.99 (0.95-1.0) 0.85 (0.79–0.89) Mp-VIRADS Adj-Mp-VIRADS Cutoff Sensitivity Specificity Accuracy Sensitivity Specificity Accuracy 1 1.0 (0.9-1.0) 0.0 (-0.0-0.03) 0.2 (0.15–0.26) 2 1.0 (0.9-1.0) 0.18 (0.13–0.26) 0.35 (0.28–0.42) 1.0 (0.9-1.0) 0.18 (0.12–0.25) 0.34 (0.28–0.41) 3 0.83 (0.68–0.92) 0.75 (0.67–0.81) 0.76 (0.7–0.82) 0.89 (0.75–0.96) 0.7 (0.62–0.77) 0.74 (0.67–0.79) 4 0.69 (0.53–0.82) 0.89 (0.83–0.93) 0.85 (0.79–0.9) 0.78 (0.62–0.88) 0.88 (0.82–0.93) 0.86 (0.81–0.91) 5 0.36 (0.22–0.52) 0.95 (0.9–0.98) 0.84 (0.77–0.88) 0.53 (0.37–0.68) 0.94 (0.89–0.97) 0.86 (0.8–0.9) 0.28 (0.16–0.44) 0.99 (0.95-1.0) 0.85 (0.79–0.89) VI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; adj, adjusted. For Reader 1, 50% (3/6) of the six invasive tumors originally scored as BP-VI-RADS 3 were correctly upgraded to adj-VI-RADS 4. Of the 20 noninvasive cases, only one (5%) received a false-positive upgrade. All invasive mp-VI-RADS 3 cases (2/2, 100%) were upgraded correctly, while upgrades occurred in just two of 23 (8.7%) noninvasive cases. For Reader 2, 50% (4/8) of invasive BP-VI-RADS 3 tumors were upgraded versus one false positive among 37 (2.7%) noninvasive cases. Sixty percent (3/5) of invasive mp-VI-RADS 3 tumors were correctly upgraded to VI-RADS 4, with only one false upgrade in 21 (4.8%) noninvasive lesions. Inter-reader agreement for nADC measurements demonstrated excellent reliability (ICC = 0.81, 95% CI 0.74–0.87). The ICC values for the other quantitative parameters are presented in Table 4 . Table 4 Inter-reader agreements on variables Variables ICC 95% CI Bp VIRADS 0.74 0.65–0.82 Mp VIRADS 0.81 0.73–0.87 Tumor ADC 0.79 0.71–0.86 Normalize ADC 0.81 0.74–0.87 Adj-bp VI-RADS 0.78 0.70–0.84 Adj-mp VI-RADS 0.83 0.76–0.88 VI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; ADC, apparent diffusion coefficient adj, adjusted. Discussion The results of our study show that nADC quantification significantly improves VI-RADS for detecting muscle invasion. Among the 182 patients, the mean nADC values were significantly lower in MIBC than in NMIBC (0.32 vs. 0.46; p < 0.001). The adjusted VI-RADS with nADC integration consistently improved the AUC values across both readers for bp-VI-RADS. The mp-MRI with nADC integration (mp + nADC) demonstrated the highest diagnostic performance among all evaluated combinations, achieving AUC values of 0.94 for Reader 1 and 0.90 for Reader 2. The adj-VI-RADS protocol demonstrated significant utility in resolving diagnostic uncertainty for VI-RADS category 3 lesions, minimizing false-positive upgrades for both readers and MRI protocols (bp/mp). The literature contains numerous investigations into the diagnostic accuracy of VI-RADS for muscle invasion detection, with specific comparisons made between noncontrast bp-VI-RADS and contrast-enhanced mp-VI-RADS [ 9 , 10 , 17 , 18 ]. However, interest in the potential added value of combined or adjusted VI-RADS scoring systems is growing. The diagnostic utility of existing VI-RADS versions is limited by the lack of objective, reproducible quantitative parameters. Accordingly, several studies have focused on defining such parameters to optimize VI-RADS performance. Takeuchi et al. [ 19 ] recently refined VI-RADS by incorporating qualitative peritumoral enhancement—a DCE-MRI feature requiring contrast administration and subjective interpretation. While innovative, this approach inherits the core limitations of gadolinium-based protocols. By contrast, our primary focus in this study was to identify and integrate objective, quantitative MRI parameters (specifically nADC) with a view to enhancing VI-RADS standardization and reproducibility. We aimed to evaluate whether an adjusted bp-VI-RADS (bp-VI-RADS + nADC) could improve diagnostic accuracy for muscle invasion. This emphasis is rooted in significant clinical concerns regarding gadolinium-based contrast agents, including the risk of nephrogenic systemic fibrosis and patient discomfort. The current literature indicates that ADC values may serve as promising biomarkers of the invasive and proliferative capacities of BC [ 20 , 21 ]. Zhang et al. [ 11 ] demonstrated that a combined bp-VI-RADS model (integrating ADC values) significantly outperformed the standard bp-VI-RADS model in detecting muscle invasion, with AUC values of 0.942 and 0.886, respectively. Our findings are consistent with their results. We observed that our adjusted bp-VI-RADS provided comparable superior diagnostic accuracy for predicting muscle invasion compared with bp-VI-RADS, with AUC values of 0.89 and 0.84, respectively. Likewise, when comparing adjusted bp-VI-RADS with mp-VI-RADS, we found that the diagnostic performance of our noncontrast model approached that of mp-VI-RADS (AUC: 0.89 vs. 0.92). Furthermore, our methodology advances quantitative rigor. Whereas Zhang et al. [ 11 ] used three focal ROIs, we implemented whole-tumor freehand ROI analysis with bladder-lumen normalization (nADC), providing a more comprehensive assessment of tumor heterogeneity while minimizing sampling bias. This approach optimizes reproducibility, as evidenced by the excellent inter-reader agreement (ICC = 0.81). Gong et al. [ 22 ] investigated quantitative parameters, such as tumor contact length, tumor longitudinal length, tumor morphologic index, tumor cellularity index, and DCE-derived time-intensity curve types. Their adjusted VI-RADS model, incorporating these metrics, demonstrated significantly higher diagnostic accuracy for predicting muscle invasion than standard mp-MRI VI-RADS, with AUC values of 0.908 versus 0.798, respectively. However, their model’s dependence on contrast-enhanced sequences and complex multiparametric measurements may limit practical clinical utility. By contrast, our study identified nADC as a standalone, highly discriminative biomarker—significantly lower in MIBC than NMIBC ( p < 0.001)—and integrated it into a streamlined VI-RADS modification. Given the significant discriminative power of nADC, this parameter was integrated into a modified VI-RADS scoring system. Diverging from the aforementioned investigation, our study comprehensively evaluated bp-VI-RADS, mp-VI-RADS, adjusted bp-VI-RADS, and adjusted mp-VI-RADS for detecting muscle invasion. Although the high diagnostic performance of the contrast-free adjusted bp-VI-RADS model for detecting muscle invasion is a key finding of our study, our results indicate that the adjusted mp-VI-RADS model (mp-VI-RADS + nADC) not only demonstrated superior overall diagnostic performance but also exhibited the potential to mitigate false-negative interpretations on a per-case basis. While existing modified VI-RADS models rely on multiparametric integrations or complex scoring adjustments, our study pioneers a fundamentally distinct approach: a single-step upgrade algorithm driven solely by nADC. By restricting score adjustments exclusively to cases in which nADC values fall below the validated threshold (≤ 0.403), we introduce a targeted binary decision rule that enhances muscle invasion detection without adding interpretive ambiguity. Critically, our method requires no specialized software or quantitative expertise, enabling immediate implementation in routine radiology practice. Our study has some limitations. First, it was a single-institution retrospective study. Larger multicenter studies are required to validate our findings. Second, ADC measurement can be affected by acquisition parameters, such as b-value selection and signal-to-noise ratio. These factors introduce variability and may reduce the reproducibility of ADC-based VI-RADS, but we aimed to minimize this by using references to the nADC we obtained and by involving two different readers. Third, we did not include lesions smaller than 1 cm. However, our approach was effective in preventing the partial volume effect. Future studies should explore harmonized ADC protocols and include smaller tumors using high-resolution techniques. In conclusion, integrating nADC quantification via a standardized upgrade rule significantly enhances VI-RADS diagnostic precision for detecting muscle invasion in BC. Our contrast-free adj-bp-VI-RADS protocol achieves accuracy comparable to that of contrast-enhanced mp-VI-RADS, offering a safer and more cost-efficient alternative for routine practice. It serves as a quantitative adjunct for clinical decision making in VI-RADS category 3 lesions and advances BC staging toward greater reproducibility and clinical utility without increasing interpretive complexity. Declarations Author Contribution MNT—Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Validation; Visualization; Roles/Writing— original draft; Writing—review & editing. 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Barchetti, G., Simone, G., Ceravolo, I., Salvo, V., Campa, R., Del Giudice, F., De Berardinis, E., Buccilli, D., Catalano, C., Gallucci, M., Catto, J. W. F., & Panebianco, V. (2019). Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center. European radiology , 29 (10), 5498–5506. https://doi.org/10.1007/s00330-019-06117-8 . American College of Radiology Committee on Drugs and Contrast Media (2021) ACR Manual On Contrast Media Version 10.3. Available at https://www.acr.org/-/media/ACR/Files/Clinical- Resources/Contrast_Media.pdf Accessed 29 Dec 2021. Watanabe, M., Taguchi, S., Machida, H., Tambo, M., Takeshita, Y., Kariyasu, T., Fukushima, K., Shimizu, Y., Okegawa, T., Fukuhara, H., & Yokoyama, K. (2022). Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field. European radiology , 32 (11), 7513–7521. https://doi.org/10.1007/s00330-022-08813-4 . Eryuruk, U., Tasdemir, M. N., & Aslan, S. (2023). Comparison of the diagnostic performance of biparametric and multiparametric MRI in detecting muscle invasion of bladder cancer located at the ureteral orifice. Abdominal radiology (New York) , 48 (10), 3174–3182. https://doi.org/10.1007/s00261-023-03979-x . Aslan, S., Cakir, I. M., Oguz, U., Bekci, T., & Demirelli, E. (2022). Comparison of the diagnostic accuracy and validity of biparametric MRI and multiparametric MRI-based VI-RADS scoring in bladder cancer; is contrast material really necessary in detecting muscle invasion?. Abdominal radiology (New York) , 47 (2), 771–780. https://doi.org/10.1007/s00261-021-03383-3 . Zhang, Z., Xiao, W., Wang, Y., Zhang, W., & Luo, M. (2025). Exploring the potential of the combined diagnostic model of ADC value and bp-MRI VI-RADS in the evaluation of muscle invasion in bladder Cancer. Abdominal radiology (New York) , 50 (7), 3100–3107. https://doi.org/10.1007/s00261-024-04788-6 . Taşdemir, M. N., Eryürük, U., Oğuz, U., Tok, B., & Aslan, S. (2025). The role of multiparametric magnetic resonance imaging in the differentiation of low- and high-grade non-muscle invasive bladder cancer. Diagnostic and interventional radiology (Ankara, Turkey) , 31 (4), 295–302. https://doi.org/10.4274/dir.2024.243004 . Liu, P., Cai, L., Yu, R., Cao, Q., Bai, K., Zhuang, J., Wu, Q., Li, P., Yang, X., & Lu, Q. (2024). Significance of Normalized Apparent Diffusion Coefficient in the Vesical Imaging-Reporting and Data System for Diagnosing Muscle-Invasive Bladder Cancer. Journal of magnetic resonance imaging: JMRI , 60 (4), 1639–1647. https://doi.org/10.1002/jmri.29208 . Li, S., Liang, P., Wang, Y., Feng, C., Shen, Y., Hu, X., Hu, D., Meng, X., & Li, Z. (2021). Combining volumetric apparent diffusion coefficient histogram analysis with vesical imaging reporting and data system to predict the muscle invasion of bladder cancer. Abdominal radiology (New York) , 46 (9), 4301–4310. https://doi.org/10.1007/s00261-021-03091-y . Wang, H. J., Pui, M. H., Guo, Y., Li, S. R., Liu, M. J., Guan, J., Zhang, X. L., & Feng, Y. (2014). Value of normalized apparent diffusion coefficient for estimating histological grade of vesical urothelial carcinoma. Clinical radiology , 69 (7), 727–731. https://doi.org/10.1016/j.crad.2014.03.001 . Wang, H. J., Pui, M. H., Guo, Y., Li, S. R., Liu, M. J., Guan, J., Zhang, X. L., & Feng, Y. (2014). Value of normalized apparent diffusion coefficient for estimating histological grade of vesical urothelial carcinoma. Clinical radiology , 69 (7), 727–731. https://doi.org/10.1016/j.crad.2014.03.001 . Ye, L., Chen, Y., Xu, H., Xie, H., Yao, J., Liu, J., & Song, B. (2022). Biparametric magnetic resonance imaging assessment for detection of muscle-invasive bladder cancer: a systematic review and meta-analysis. European radiology , 32 (9), 6480–6492. https://doi.org/10.1007/s00330-022-08696-5 . Wang, H., Luo, C., Zhang, F., Guan, J., Li, S., Yao, H., Chen, J., Luo, J., Chen, L., & Guo, Y. (2019). Multiparametric MRI for Bladder Cancer: Validation of VI-RADS for the Detection of Detrusor Muscle Invasion. Radiology , 291 (3), 668–674. https://doi.org/10.1148/radiol.2019182506 . Takeuchi, M., Higaki, A., Kojima, Y., Ono, K., Maruhisa, T., Yokoyama, T., Watanabe, H., Yamamoto, A., & Tamada, T. (2025). Diagnostic significance of peritumoral enhancement in distinguishing between muscle-invasive and non-muscle-invasive bladder cancer. Abdominal radiology (New York) , 50 (4), 1679–1688. https://doi.org/10.1007/s00261-024-04658-1 . Wang, Y., Shen, Y., Hu, X., Li, Z., Feng, C., Hu, D., & Kamel, I. R. (2020). Application of R2* and Apparent Diffusion Coefficient in Estimating Tumor Grade and T Category of Bladder Cancer. AJR. American journal of roentgenology , 214 (2), 383–389. https://doi.org/10.2214/AJR.19.21668 . Li, Q., Cao, B., Liu, K., Sun, H., Ding, Y., Yan, C., Wu, P. Y., Dai, C., Rao, S., Zeng, M., Jiang, S., & Zhou, J. (2022). Detecting the muscle invasiveness of bladder cancer: An application of diffusion kurtosis imaging and tumor contact length. European journal of radiology , 151 , 110329. https://doi.org/10.1016/j.ejrad.2022.110329 . Gong, Y., Cheng, Y., Zhang, J., Bao, M. L., Zhu, F. P., Sun, X. Y., & Zhang, Y. D. (2024). Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer. Journal of magnetic resonance imaging: JMRI , 60 (3), 1113–1123. https://doi.org/10.1002/jmri.29184 . Additional Declarations No competing interests reported. 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Tasdemir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBADxgYG5gNAWkKGSA0JIC1sCSAtPKRo4TEAMQlr0W3vfSbx8Ued7NrZZz6/ulFjwcPAfvjoBnxazM4cN5OckXDYeNu53G3WOceADuNJS7uBV8uNNGZjnoQDidvO8G4zzmEDapHgMcOv5f4zZuM/CXVALTzPjHP+EaPlBhvjY4YEZpAW5se5bcRoOZPG+LAnDeiXM2xmzLl9EjxsBP1y/BjDgR82dbLbzjA//pzzrU6On/3wMbxakAGbBJgkVjkIMH8gRfUoGAWjYBSMHAAA7l9JSiBRmTsAAAAASUVORK5CYII=","orcid":"","institution":"Giresun University","correspondingAuthor":true,"prefix":"","firstName":"Merve","middleName":"Nur","lastName":"Tasdemir","suffix":""},{"id":528402732,"identity":"7c50fe54-5d09-4016-8fbc-e50d320cfbda","order_by":1,"name":"Uluhan Eryuruk","email":"","orcid":"","institution":"Giresun University","correspondingAuthor":false,"prefix":"","firstName":"Uluhan","middleName":"","lastName":"Eryuruk","suffix":""},{"id":528402733,"identity":"eff62fe4-cc2b-4c95-9aaf-c9e6539c30fc","order_by":2,"name":"Esra 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02:12:43","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117896,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/58a7e1f5d9b550efc73dacf5.html"},{"id":93538944,"identity":"eb0b2c09-d336-47bf-9dc5-2b1ac6e13f6f","added_by":"auto","created_at":"2025-10-15 02:12:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51199,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection. ADC, apparent diffusion coefficient; NMIBC, non-muscle invasive bladder cancer; MIBC, muscle invasive bladder cancer\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/a729ee1fb7772ae786a64ff1.jpeg"},{"id":93538949,"identity":"77e587ab-30ba-4470-b8cc-7fa6d9a765b6","added_by":"auto","created_at":"2025-10-15 02:12:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":264302,"visible":true,"origin":"","legend":"\u003cp\u003eA 65-year-old patient with tumour in bladder. No tumour stalk was seen on T2WI, DWI and DCE. No disruption of the muscularis layer with low signal intensity(a, b, c). mp and bp-VI-RADS were scored as 3. nADC was calculated as 0.391(d). Adjusted VIRADS was scored as 4. Histopathological examination was consistent with muscle invasive bladder cancer. WI, weighted imaging; DWI, diffusion weighted imaging; DCE, dynamic contrast-enhanced imaging; VI-RADS, Vesical Imaging Reporting and Data System; nADC, normalized apparent diffusion coefficient.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/3365fcacae57fca95df7124f.jpeg"},{"id":93538951,"identity":"c36d83c4-8ab4-42d5-8ea0-f3584a5de848","added_by":"auto","created_at":"2025-10-15 02:12:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115408,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve analysis for the muscle invasive bladder cancer for reader 1 (a) and reader 2 (b). bp, biparametric; mp, multiparametric; adj, adjusted.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/58bee0cdc000e186b0c2bd5b.jpeg"},{"id":93539742,"identity":"acd30206-3296-4c18-a66a-5bd3f540160a","added_by":"auto","created_at":"2025-10-15 02:20:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":274188,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curves for bp and mp VI-RADS and their combinations with nADC (adj_bp, adj_mp), including the 'treat all' strategy, for Reader 1 and Reader 2. VI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; nADC, normalized apparent diffusion coefficient; adj, adjusted.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/9a3e18f4c435bafae3ed2722.png"},{"id":96604698,"identity":"92bd3312-8cfa-40f2-afb3-25b2aadb06e7","added_by":"auto","created_at":"2025-11-24 09:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1676519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7733624/v1/72abf0bc-ee1b-454d-8c95-3d76fad0278c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simplified Integration of ADC Quantification Enhances Biparametric and Multiparametric VI-RADS for Detrusor Muscle Invasion Prediction in Bladder Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBladder cancer (BC) ranks as the 10th most prevalent and 13th most fatal malignancy worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is essential to differentiate accurately between muscle-invasive (MIBC) and nonmuscle-invasive bladder cancer (NMIBC), as involvement of the muscle layer plays a crucial role in determining bladder-sparing treatment strategies. MIBC (T2-T4) is usually treated with aggressive interventions, including cystectomy, systemic treatment, or a combination of the two, and has a poor prognosis. NMIBC (Ta-T1) is characterized by a nonaggressive demeanor and low-grade characteristics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Due to the high complication rate and poor quality of life associated with radical cystectomy, a thorough preoperative assessment of the need for this procedure is important. Cystoscopy and evaluation of pathological tissue obtained by transurethral resection of bladder tumor (TURBT) are the recommended diagnostic methods. However, diagnostic TURBT is associated with a significant risk of serious complications, including bladder perforation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDue to its noninvasive nature and superior soft tissue contrast, magnetic resonance imaging (MRI) holds significant promise in the assessment of MIBC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Vesical Imaging Reporting and Data System (VI-RADS) is a standardized procedure for the evaluation of MRI findings in BC, with an emphasis on the diagnosis of muscle invasion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. VI-RADS was introduced in 2018, and several studies, including prospective ones, have confirmed its clinical relevance and efficacy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, VI-RADS has a potential limitation because it includes dynamic contrast-enhanced imaging (DCE). The rare adverse effects of gadolinium-based contrast agents following intravenous injection include allergic reactions, nephrogenic systemic fibrosis, renal failure, and gadolinium accumulation in the brain [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, there is a clinical need to develop a reporting model that does not use DCEI. Biparametric MRI (bp-MRI; T2-weighted\u0026thinsp;+\u0026thinsp;diffusion-weighted imaging [DWI]) protocols have emerged, with studies suggesting accuracy comparable to that of multiparametric MRI (mp-MRI) for VI-RADS-based MIBC detection [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting versions of the VI-RADS scoring system are limited by the lack of objective and reproducible quantitative parameters. As a noninvasive imaging technique, DWI enables the quantification of water molecule motion within tissues and provides objective measurements through the apparent diffusion coefficient (ADC) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The efficacy of ADC values in identifying the histological grade of BC has been a subject of research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, recent studies indicate its potential to augment VI-RADS. For instance, combining ADC with mp-MRI VI-RADS improved MIBC diagnosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Zhang et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated similar benefits for bp-MRI VI-RADS. However, existing approaches often employ complex multiparametric models or lack standardization in ADC measurement (e.g., single vs. multiple regions of interest [ROIs]), limiting clinical practicality.\u003c/p\u003e\u003cp\u003eAcknowledging these practical challenges, we propose a simplified integration strategy using a single normalized ADC (nADC) value\u0026mdash;calculated from whole-tumor ADC normalized to bladder-lumen ADC\u0026mdash;to refine VI-RADS scoring via an objective upgrade rule. We hypothesize that this approach enhances the diagnostic performance of both bp- and mp-VI-RADS. Specifically, patients with nADC values below an optimal threshold would receive an adjusted VI-RADS (adj-VI-RADS) score upgrade, resolving ambiguity in equivocal cases (e.g., VI-RADS 3) while minimizing gadolinium dependence.\u003c/p\u003e\u003cp\u003eIn this study, we aim to assess the diagnostic performance of an adjusted VI-RADS (adj-VI-RADS), derived from a combination of ADC measurements and bp/mp-MRI, in predicting detrusor muscle invasion in BC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective investigation was approved by the institutional ethics board, and informed consent was relinquished (****/BAEK-442/2025/04). The study protocol aligned with the ethical standards of the 1975 Declaration of Helsinki.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy group\u003c/h2\u003e\n \u003cp\u003eBetween June 2020 and June 2025, 208 patients who underwent TUR-B and/or radical cystectomy for BC with mpMRI performed within three months prior were identified. Patients were selected for the study according to the following criteria\u003c/p\u003e\n \u003cp\u003e1.MRI assessment included the required sequences for mp-MRI protocols.\u003c/p\u003e\n \u003cp\u003e2.MRI was performed within two weeks prior to TUR bladder or cystectomy.\u003c/p\u003e\n \u003cp\u003e3.Bladder urothelial carcinoma was pathologically proven.\u003c/p\u003e\n \u003cp\u003ePatients were excluded from the study according to the following criteria:\u003c/p\u003e\n \u003cp\u003e1. Patients with ADC images of poor or nonvisible quality.\u003c/p\u003e\n \u003cp\u003e2. Patients with tumours measuring less than 1 cm.\u003c/p\u003e\n \u003cp\u003e3. Patients with different histopathological variants of bladder cancer.\u003c/p\u003e\n \u003cp\u003e4. Patients with hyperintense urine in the bladder lumen on the T1 sequence (which may confound bladder lumen ADC normalization) [15].\u003c/p\u003e\n \u003cp\u003eFigure 1 shows the patient selection.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eImage acquisition\u003c/h3\u003e\n\u003cp\u003eA 1.5-T MRI system (Magnetom Aera, Siemens Medical Solutions, Erlangen, Germany) was utilised for MRI examinations. Prior to the procedure, ultrasonography was performed to ensure that the patients had adequate bladder distension. Imaging was performed in a supine position with the utilisation of a pelvic phased-array coil. The following types of images were acquired: T1-weighted images (T1-WI), axial, coronal, and sagittal fast spin-echo T2-WI, dynamic contrast-enhanced images with three-dimensional high temporal resolution, and diffusion-weighted images with b-values of 0, 800, and 1200 s/mm2. Gadopentetate dimeglumine (Gadovist, 0.2 mL per kilogram of body weight; Bayer Healthcare, Berlin, Germany) was delivered via a power injector at a rate of 2 mL per second, followed by a further infusion of 20 mL of normal saline. Following the injection of the intravenous contrast agent, axial DCE images were captured in post-contrast phases with no gap between them.\u003c/p\u003e\n\u003ch3\u003eImage analysis and VI-RADS evaluation\u003c/h3\u003e\n\u003cp\u003eAll MRI scans were transferred to the picture archiving and communication system. Two radiologists with different levels of experience independently evaluated the images without reference to histopathology. Reader 1 had recently qualified as a specialist in radiology, while Reader 2 was a radiology resident with four years of training. Two sets were created: Set 1 included T2WI and DWI, while Set 2 included T2WI, DWI, and DCE. These sets were evaluated one month apart to avoid recall bias. Both readers were blinded and had no access to patient demographics or surgical information.\u003c/p\u003e\n\u003cp\u003eT2WI, DWI, and DCE were scored according to the VI-RADS criteria for each index lesion. On T2WI, the integrity of the detrusor muscle, seen as a continuous band of low signal intensity, was evaluated. On DWI and corresponding ADC maps, invasion was characterized by the presence of restricted diffusion extending into or beyond the muscle layer. On DCE imaging, early, vivid enhancement of the tumor extending into the muscularis propria or perivesical fat was the criterion for a score indicating invasion. [5]. Scores for the bp- and mp-VI-RADS categories were then obtained for these lesions. The slice exhibiting the largest tumor area was selected, and free-hand ROIs were placed on it (Fig.\u0026nbsp;2). The whole tumor ADC (wADC) was calculated [10]. A 20 mm\u003csup\u003e2\u003c/sup\u003e ROI was placed in the bladder lumen, away from urinary flow turbulence or artifacts. nADC was calculated by dividing the tumor ADC by the bladder lumen ADC [12, 16].\u003c/p\u003e\n\u003cp\u003eThe optimal nADC threshold for distinguishing muscle invasion was determined via ROC analysis using pooled data from both readers. When nADC values were equal to or less than this cutoff, an upgrade rule was applied to both the bp-MRI and mp-MRI VI-RADS scores to obtain the adj-VI-RADS score.\u003c/p\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were conducted utilizing SPSS Statistics version 25 (IBM Corporation, Armonk, NY, USA). Continuous data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and were subjected to the Wilcoxon signed-rank test. Categorical variables are expressed as frequency counts and percentages, analyzed via Fisher\u0026apos;s exact test. The diagnostic performance of both bp/mp VI-RADS and the adj-VIRADS for detecting muscle invasion was evaluated per reader using sensitivity, specificity, accuracy, and area under the curve (AUC). We evaluated uncertainty in model AUC differences (\u0026Delta;AUC\u0026thinsp;=\u0026thinsp;AUC\u003csub\u003eadjusted\u003c/sub\u003e \u0026minus; AUC\u003csub\u003ebaseline\u003c/sub\u003e) through 1000 paired bootstrap replicates, maintaining within-case correlation structures. Percentile-based 95% CIs and two-tailed p-values are reported. Clinical applicability of adjusted bp/mp-VI-RADS was evaluated via decision curve analysis. The intraclass correlation coefficient (ICC) was used to assess inter-reader reliability for wADC, nADC, bp-/mp-VI-RADS and adj-bp-/mp- VI-RADS scores. Statistical significance was defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses incorporated 95% confidence intervals (CIs).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 208 patients with histopathologically confirmed BC were initially identified. Of these, two were diagnosed with alternative histological subtypes of bladder carcinoma. Twelve patients presented with lesions smaller than 1 cm, 10 exhibited poor-quality or nondiagnostic ADC images, and two demonstrated hyperintense urine within the bladder lumen on T1-weighted imaging. These 26 individuals were excluded from the analysis, resulting in a final study cohort of 182 patients (167 males; median age 72 years). A total of 140 patients had NMIBC, while 42 had MIBC. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic and pathological characteristics of the study population. For Reader 1, NMIBC demonstrated a mean wADC of 1,055 \u0026times; 10⁻⁶ mm\u0026sup2;/s and a mean nADC of 0.49 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas MIBC showed significantly lower values (wADC: 710 \u0026times; 10⁻⁶ mm\u0026sup2;/s; nADC: 0.317; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For Reader 2, NMIBC yielded a mean wADC of 1,234 \u0026times; 10⁻⁶ mm\u0026sup2;/s and a mean nADC of 0.524 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas MIBC exhibited reduced values (wADC: 913 \u0026times; 10⁻⁶ mm\u0026sup2;/s; nADC: 0.39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eDemographic and pathological characteristics of the study population\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)/mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaximum diameter (mm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167 (91.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMuscle Invasion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNMIBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (76.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMBIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrade\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (37.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (62.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNMIBC, non-muscle invasive bladder cancer; MIBC, muscle invasive bladder cancer.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of muscle invasion in bladder cancer\u003c/h2\u003e\u003cp\u003eThe optimal ADC cutoffs for predicting muscle invasion were a tumour ADC of 864 \u0026micro;m\u0026sup2;/s and an nADC of 0.403. An upgrade criterion was applied to assign the adj-VI-RADS score when the nADC values were \u0026le;\u0026thinsp;0.403.\u003c/p\u003e\u003cp\u003eIn ROC analysis, Reader 1 obtained the following area under the curve (AUC) values: baseline bp-VI-RADS: 0.84; baseline mp-VI-RADS: 0.92; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.94. For Reader 2, the corresponding values were as follows: baseline bp-VI-RADS: 0.82; baseline mp-VI-RADS: 0.85; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For both readers, a VI-RADS category\u0026thinsp;\u0026ge;\u0026thinsp;4 was the optimal cutoff for predicting muscle invasion using both baseline and adjusted VI-RADS models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe adjusted models demonstrated significantly improved discrimination over their respective baselines. For Reader 1, comparison of adj-bp-VI-RADS versus baseline bp-VI-RADS yielded a ΔAUC of 0.056 (95% CI: 0.018\u0026ndash;0.098; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while adj-mp-VI-RADS versus baseline mp-VI-RADS showed a ΔAUC of 0.025 (95% CI: 0.001\u0026ndash;0.058; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038). Similarly, for Reader 2, adj-bp-VI-RADS improved upon baseline bp-VI-RADS with a ΔAUC of 0.036 (95% CI: 0.007\u0026ndash;0.069; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and adj-mp-VI-RADS surpassed baseline mp-VI-RADS with a ΔAUC of 0.030 (95% CI: 0.004\u0026ndash;0.058; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Decision curve analysis revealed a superior net benefit for the adjusted VI-RADS protocol compared to both treat-all management and bp-/mp-VI-RADS across most threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor Reader 1, the sensitivity and specificity of bp-VI-RADS 4 for detecting muscle invasion were 67% and 87%, respectively. With adj-bp-VI-RADS 4, sensitivity increased to 75%, while specificity decreased slightly to 86%. For Reader 2, the sensitivity and specificity of bp-VI-RADS 4 for detecting muscle invasion were 61% and 90%, respectively. With adj-bp-VI-RADS 4, sensitivity increased to 72%, while specificity remained unchanged at 90%. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Reader 1) and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Reader 2) present a summary of the diagnostic performance measures for the other VI-RADS scores.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic performance of bp VI-RADS and adjusted bp VI-RADS for Reader 1 in predicting muscle invasive bladder cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAdj-Bp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-0.0-0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.15\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12 (0.08\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3 (0.24\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1 (0.06\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.28 (0.22\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.68\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73 (0.66\u0026ndash;0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75 (0.69\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97 (0.86-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.66 (0.58\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72 (0.65\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.67 (0.5\u0026ndash;0.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.87 (0.81\u0026ndash;0.92)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.83 (0.77\u0026ndash;0.88)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.75 (0.59\u0026ndash;0.86)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.86 (0.8\u0026ndash;0.91)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.84 (0.78\u0026ndash;0.89)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39 (0.25\u0026ndash;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97 (0.92\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85 (0.79\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64 (0.48\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92 (0.87\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.87 (0.81\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.36 (0.22\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97 (0.93\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85 (0.79\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAdj-Mp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-0.0-0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.15\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13 (0.08\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3 (0.24\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11 (0.07\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.29 (0.23\u0026ndash;0.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.94 (0.82\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73 (0.66\u0026ndash;0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.71\u0026ndash;0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.67 (0.59\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74 (0.67\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.89 (0.75\u0026ndash;0.96)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.89 (0.83\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.89 (0.84\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.94 (0.82\u0026ndash;0.98)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.88 (0.81\u0026ndash;0.92)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.89 (0.84\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.42 (0.27\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97 (0.92\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.8\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75 (0.59\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92 (0.86\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88 (0.83\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.39 (0.25\u0026ndash;0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97 (0.93\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86 (0.8\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eVI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; adj, adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic performance of bp VI-RADS and adjusted bp VI-RADS for Reader 2 in predicting muscle invasive bladder cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAdj-Bp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-0.0-0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.15\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18 (0.12\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34 (0.28\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.17 (0.12\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34 (0.27\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.68\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.57\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.62\u0026ndash;0.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.75\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6 (0.52\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.66 (0.59\u0026ndash;0.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.61 (0.45\u0026ndash;0.75)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.9 (0.85\u0026ndash;0.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.85 (0.79\u0026ndash;0.89)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.72 (0.56\u0026ndash;0.84)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.9 (0.84\u0026ndash;0.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.86 (0.81\u0026ndash;0.91)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36 (0.22\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95 (0.9\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 (0.77\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5 (0.34\u0026ndash;0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93 (0.88\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85 (0.79\u0026ndash;0.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28 (0.16\u0026ndash;0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.95-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.85 (0.79\u0026ndash;0.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eAdj-Mp-VIRADS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0 (-0.0-0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.15\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18 (0.13\u0026ndash;0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.35 (0.28\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0 (0.9-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18 (0.12\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.34 (0.28\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.68\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75 (0.67\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76 (0.7\u0026ndash;0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89 (0.75\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.7 (0.62\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.74 (0.67\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.69 (0.53\u0026ndash;0.82)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.89 (0.83\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.85 (0.79\u0026ndash;0.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.78 (0.62\u0026ndash;0.88)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.88 (0.82\u0026ndash;0.93)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.86 (0.81\u0026ndash;0.91)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36 (0.22\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95 (0.9\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.77\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53 (0.37\u0026ndash;0.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.89\u0026ndash;0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.86 (0.8\u0026ndash;0.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28 (0.16\u0026ndash;0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99 (0.95-1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85 (0.79\u0026ndash;0.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eVI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; adj, adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor Reader 1, 50% (3/6) of the six invasive tumors originally scored as BP-VI-RADS 3 were correctly upgraded to adj-VI-RADS 4. Of the 20 noninvasive cases, only one (5%) received a false-positive upgrade. All invasive mp-VI-RADS 3 cases (2/2, 100%) were upgraded correctly, while upgrades occurred in just two of 23 (8.7%) noninvasive cases. For Reader 2, 50% (4/8) of invasive BP-VI-RADS 3 tumors were upgraded versus one false positive among 37 (2.7%) noninvasive cases. Sixty percent (3/5) of invasive mp-VI-RADS 3 tumors were correctly upgraded to VI-RADS 4, with only one false upgrade in 21 (4.8%) noninvasive lesions.\u003c/p\u003e\u003cp\u003eInter-reader agreement for nADC measurements demonstrated excellent reliability (ICC\u0026thinsp;=\u0026thinsp;0.81, 95% CI 0.74\u0026ndash;0.87). The ICC values for the other quantitative parameters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInter-reader agreements on variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eICC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBp VIRADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65\u0026ndash;0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMp VIRADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026ndash;0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor ADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.71\u0026ndash;0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormalize ADC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u0026ndash;0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-bp VI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.70\u0026ndash;0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdj-mp VI-RADS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.76\u0026ndash;0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eVI-RADS, Vesical Imaging Reporting and Data System; bp, biparametric; mp, multiparametric; ADC, apparent diffusion coefficient adj, adjusted.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of our study show that nADC quantification significantly improves VI-RADS for detecting muscle invasion. Among the 182 patients, the mean nADC values were significantly lower in MIBC than in NMIBC (0.32 vs. 0.46; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The adjusted VI-RADS with nADC integration consistently improved the AUC values across both readers for bp-VI-RADS. The mp-MRI with nADC integration (mp\u0026thinsp;+\u0026thinsp;nADC) demonstrated the highest diagnostic performance among all evaluated combinations, achieving AUC values of 0.94 for Reader 1 and 0.90 for Reader 2. The adj-VI-RADS protocol demonstrated significant utility in resolving diagnostic uncertainty for VI-RADS category 3 lesions, minimizing false-positive upgrades for both readers and MRI protocols (bp/mp).\u003c/p\u003e\u003cp\u003eThe literature contains numerous investigations into the diagnostic accuracy of VI-RADS for muscle invasion detection, with specific comparisons made between noncontrast bp-VI-RADS and contrast-enhanced mp-VI-RADS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, interest in the potential added value of combined or adjusted VI-RADS scoring systems is growing. The diagnostic utility of existing VI-RADS versions is limited by the lack of objective, reproducible quantitative parameters. Accordingly, several studies have focused on defining such parameters to optimize VI-RADS performance.\u003c/p\u003e\u003cp\u003eTakeuchi et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] recently refined VI-RADS by incorporating qualitative peritumoral enhancement\u0026mdash;a DCE-MRI feature requiring contrast administration and subjective interpretation. While innovative, this approach inherits the core limitations of gadolinium-based protocols. By contrast, our primary focus in this study was to identify and integrate objective, quantitative MRI parameters (specifically nADC) with a view to enhancing VI-RADS standardization and reproducibility. We aimed to evaluate whether an adjusted bp-VI-RADS (bp-VI-RADS\u0026thinsp;+\u0026thinsp;nADC) could improve diagnostic accuracy for muscle invasion. This emphasis is rooted in significant clinical concerns regarding gadolinium-based contrast agents, including the risk of nephrogenic systemic fibrosis and patient discomfort.\u003c/p\u003e\u003cp\u003eThe current literature indicates that ADC values may serve as promising biomarkers of the invasive and proliferative capacities of BC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Zhang et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated that a combined bp-VI-RADS model (integrating ADC values) significantly outperformed the standard bp-VI-RADS model in detecting muscle invasion, with AUC values of 0.942 and 0.886, respectively. Our findings are consistent with their results. We observed that our adjusted bp-VI-RADS provided comparable superior diagnostic accuracy for predicting muscle invasion compared with bp-VI-RADS, with AUC values of 0.89 and 0.84, respectively. Likewise, when comparing adjusted bp-VI-RADS with mp-VI-RADS, we found that the diagnostic performance of our noncontrast model approached that of mp-VI-RADS (AUC: 0.89 vs. 0.92). Furthermore, our methodology advances quantitative rigor. Whereas Zhang et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] used three focal ROIs, we implemented whole-tumor freehand ROI analysis with bladder-lumen normalization (nADC), providing a more comprehensive assessment of tumor heterogeneity while minimizing sampling bias. This approach optimizes reproducibility, as evidenced by the excellent inter-reader agreement (ICC\u0026thinsp;=\u0026thinsp;0.81).\u003c/p\u003e\u003cp\u003eGong et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] investigated quantitative parameters, such as tumor contact length, tumor longitudinal length, tumor morphologic index, tumor cellularity index, and DCE-derived time-intensity curve types. Their adjusted VI-RADS model, incorporating these metrics, demonstrated significantly higher diagnostic accuracy for predicting muscle invasion than standard mp-MRI VI-RADS, with AUC values of 0.908 versus 0.798, respectively. However, their model\u0026rsquo;s dependence on contrast-enhanced sequences and complex multiparametric measurements may limit practical clinical utility. By contrast, our study identified nADC as a standalone, highly discriminative biomarker\u0026mdash;significantly lower in MIBC than NMIBC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;and integrated it into a streamlined VI-RADS modification. Given the significant discriminative power of nADC, this parameter was integrated into a modified VI-RADS scoring system. Diverging from the aforementioned investigation, our study comprehensively evaluated bp-VI-RADS, mp-VI-RADS, adjusted bp-VI-RADS, and adjusted mp-VI-RADS for detecting muscle invasion. Although the high diagnostic performance of the contrast-free adjusted bp-VI-RADS model for detecting muscle invasion is a key finding of our study, our results indicate that the adjusted mp-VI-RADS model (mp-VI-RADS\u0026thinsp;+\u0026thinsp;nADC) not only demonstrated superior overall diagnostic performance but also exhibited the potential to mitigate false-negative interpretations on a per-case basis.\u003c/p\u003e\u003cp\u003eWhile existing modified VI-RADS models rely on multiparametric integrations or complex scoring adjustments, our study pioneers a fundamentally distinct approach: a single-step upgrade algorithm driven solely by nADC. By restricting score adjustments exclusively to cases in which nADC values fall below the validated threshold (\u0026le;\u0026thinsp;0.403), we introduce a targeted binary decision rule that enhances muscle invasion detection without adding interpretive ambiguity. Critically, our method requires no specialized software or quantitative expertise, enabling immediate implementation in routine radiology practice.\u003c/p\u003e\u003cp\u003eOur study has some limitations. First, it was a single-institution retrospective study. Larger multicenter studies are required to validate our findings. Second, ADC measurement can be affected by acquisition parameters, such as b-value selection and signal-to-noise ratio. These factors introduce variability and may reduce the reproducibility of ADC-based VI-RADS, but we aimed to minimize this by using references to the nADC we obtained and by involving two different readers. Third, we did not include lesions smaller than 1 cm. However, our approach was effective in preventing the partial volume effect. Future studies should explore harmonized ADC protocols and include smaller tumors using high-resolution techniques.\u003c/p\u003e\u003cp\u003eIn conclusion, integrating nADC quantification via a standardized upgrade rule significantly enhances VI-RADS diagnostic precision for detecting muscle invasion in BC. Our contrast-free adj-bp-VI-RADS protocol achieves accuracy comparable to that of contrast-enhanced mp-VI-RADS, offering a safer and more cost-efficient alternative for routine practice. It serves as a quantitative adjunct for clinical decision making in VI-RADS category 3 lesions and advances BC staging toward greater reproducibility and clinical utility without increasing interpretive complexity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMNT\u0026mdash;Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Validation; Visualization; Roles/Writing\u0026mdash; original draft; Writing\u0026mdash;review \u0026amp; editing. UE\u0026mdash;Formal analysis; Investigation; Visualization; Writing\u0026mdash;review \u0026amp; editing. EI\u0026mdash;Investigation; Resources; Visualization; Formal analysis; SA\u0026mdash; Project administration; Formal analysis; Investigation; Resources; Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArita, Y., Kwee, T. 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Detecting the muscle invasiveness of bladder cancer: An application of diffusion kurtosis imaging and tumor contact length. \u003cem\u003eEuropean journal of radiology\u003c/em\u003e, \u003cem\u003e151\u003c/em\u003e, 110329. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrad.2022.110329\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrad.2022.110329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGong, Y., Cheng, Y., Zhang, J., Bao, M. L., Zhu, F. P., Sun, X. Y., \u0026amp; Zhang, Y. D. (2024). Role of Additional MRI-Based Morphologic Measurements on the Performance of VI-RADS for Muscle-Invasive Bladder Cancer. \u003cem\u003eJournal of magnetic resonance imaging: JMRI\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(3), 1113\u0026ndash;1123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jmri.29184\u003c/span\u003e\u003cspan address=\"10.1002/jmri.29184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bladder cancer, Apparent diffusion coefficient, Muscle invasion, Magnetic resonance imaging, VI-RADS","lastPublishedDoi":"10.21203/rs.3.rs-7733624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7733624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo assess the diagnostic performance of an adjusted VI-RADS (adj-VI-RADS), combining ADC measurements with biparametric(bp)/multiparametric (mp) magnetic resonance imaging (MRI), for predicting detrusor muscle invasion in bladder cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this retrospective study, 184 patients underwent bladder mpMRI before TUR-B/cystectomy. Two radiologists independently evaluated the images. T2-Weighted imaging, difussion weighted- imaging, and dynamic contrast enhanced images were scored according to the VI-RADS criteria. Whole-tumor (wADC) and normalized ADC (nADC) values were calculated. An optimal nADC cutoff for muscle invasion was determined by ROC analysis. Scores were upgraded if nADC was \u0026le;\u0026thinsp;this cutoff, deriving the adj-VI-RADS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 140 patients had NMIBC, while 42 had MIBC. Both readers found significantly lower mean wADC and nADC values in MIBC compared to NMIBC (e.g., Reader 1 nADC: 0.317 vs. 0.49; Reader 2 nADC: 0.39 vs. 0.524; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal nADC cutoff for predicting muscle invasion was 0.403. In ROC analysis, Reader 1 obtained the following area under the curve (AUC) values: baseline bp-VI-RADS: 0.84; baseline mp-VI-RADS: 0.92; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.94. For Reader 2, the corresponding values were as follows: baseline bp-VI-RADS: 0.82; baseline mp-VI-RADS: 0.85; adj-bp-VI-RADS: 0.89; and adj-mp-VI-RADS: 0.90. For all models, a VI-RADS category\u0026thinsp;\u0026ge;\u0026thinsp;4 was the optimal cutoff for predicting muscle invasion.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIntegrating nADC quantification via a standardized upgrade rule significantly enhances VI-RADS diagnostic precision for detecting muscle invasion in BC. Our contrast-free adj-bp-VI-RADS protocol achieves accuracy comparable to that of contrast-enhanced mp-VI-RADS, offering a safer and more cost-efficient alternative.\u003c/p\u003e","manuscriptTitle":"Simplified Integration of ADC Quantification Enhances Biparametric and Multiparametric VI-RADS for Detrusor Muscle Invasion Prediction in Bladder Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:12:38","doi":"10.21203/rs.3.rs-7733624/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ccef384-017e-45ba-9da7-29da95ecfd3b","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-22T15:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:12:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7733624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7733624","identity":"rs-7733624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}