Synthetic magnetic resonance imaging for primary prostate cancer evaluation:Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements

PURPOSE: Bi-parametric magnetic resonance imaging (bpMRI) with diffusion-weighted images has wide utility in diagnosing clinically significant prostate cancer (csPCa). However, bpMRI yields more false-negatives for PI-RADS category 3 lesions than multiparametric (mp)MRI with dynamic-contrast-enhanced (DCE)-MRI. We investigated the utility of synthetic MRI with relaxometry maps for bpMRI-based diagnosis of csPCa. METHODS: One hundred and five treatment-naïve patients who underwent mpMRI and synthetic MRI before prostate biopsy for suspected PCa between August 2019 and December 2020 were prospectively included. Three experts and three basic prostate radiologists evaluated the diagnostic performance of conventional bpMRI and synthetic bpMRI for csPCa. PI-RADS version 2.1 category 3 lesions were identified by consensus, and relaxometry measurements (T1-value, T2-value, and proton density [PD]) were performed. The diagnostic performance of relaxometry measurements for PI-RADS category 3 lesions in peripheral zone was compared with that of DCE-MRI. Histopathological evaluation results were used as the reference standard. Statistical analysis was performed using the areas under the receiver operating characteristic curve (AUC) and McNemar test. RESULTS: In 102 patients without significant MRI artefacts, the diagnostic performance of conventional bpMRI was not significantly different from that of synthetic bpMRI for all readers (p = 0.11–0.79). The AUCs of the combination of T1-value, T2-value, and PD (T1 + T2 + PD) for csPCa in peripheral zone for PI-RADS category 3 lesions were 0.85 for expert and 0.86 for basic radiologists, with no significant difference between T1 + T2 + PD and DCE-MRI for both expert and basic radiologists (p = 0.29–0.45). CONCLUSION: Synthetic MRI with relaxometry maps shows promise for contrast media-free evaluation of csPCa

Clinical utility of the Vesical Imaging-Reporting and Data System for muscle-invasive bladder cancer between radiologists and urologists based on multiparametric MRI including 3D FSE T2-weighted acquisitions

Objectives: To investigate the clinical utility of the Vesical Imaging-Reporting and Data System (VI-RADS) by comparing its diagnostic performance for muscle-invasive bladder cancer (MIBC) between radiologists and urologists based on multiparametric MRI, including three-dimensional (3D) fast spin-echo (FSE) T2-weighted acquisitions. Methods: This study included 66 treatment-naïve patients (60 men, 6 women; mean age 74.0 years) with pathologically proven bladder cancer who underwent multiparametric MRI, including 3D FSE T2-weighted imaging, before transurethral bladder tumour resection between January 2010 and November 2018. The MRI scans were categorised according to the five-point VI-RADS score by four independent readers (two board-certified radiologists and board-certified urologists each), blinded to the histopathological findings. The VI-RADS scores were compared with the postoperative histopathological diagnosis. Interobserver agreement was assessed using weighted kappa coefficients. ROC analysis and generalised estimating equations were used to evaluate the diagnostic performance. Results: Forty-nine (74.2%) and 17 (25.8%) tumours were confirmed to be non-MIBC and MIBC, respectively, based on pathological examination. The interobserver agreement was good-to-excellent between all pairs of readers (range, 0.73–0.91). The urologists’ sensitivity/specificity values for DCE-MRI VI-RADS scores were significantly lower than those of radiologists. No significant differences were observed for the overall VI-RADS score. The AUC for the overall VI-RADS score was 0.94, 0.92, 0.89, and 0.87 for radiologists 1 and 2 and urologists 1 and 2, respectively. Conclusions: The VI-RADS score, based on multiparametric MRI including 3D FSE T2-weighted acquisitions, can be useful for radiologists and urologists to determine the bladder cancer muscle invasion status preoperatively. Key Points: • VI-RADS (using multiparametric MRI including 3D FSE T2-weighted acquisitions) achieves good to excellent interobserver agreement and has similar diagnostic performance for detecting muscle invasion by both radiologists and urologists. • The diagnostic performance of the overall VI-RADS score is high for both radiologists and urologists, particularly due to the dominant effect of diffusion-weighted imaging on the overall VI-RADS score. • The sensitivity and specificity values of the T2WI VI-RADS scores for four readers in our study (using 3D FSE T2-weighted acquisitions) were similar (with slightly higher specificity values) to previously published results (using 2D FSE T2-weighted acquisitions)

Diagnostic value of texture analysis of apparent diffusion coefficient maps for differentiating fat-poor angiomyolipoma from non-clear-cell renal cell carcinoma

Purpose: To investigate the feasibility of texture analysis of apparent diffusion coefficient (ADC) maps for differentiating fat-poor angiomyolipomas (fpAMLs) from non-clear-cell renal cell carcinomas (non-ccRCCs). Methods: In this bi-institutional study, we included two consecutive cohorts from different institutions with pathologically confirmed solid renal masses: 67 patients (fpAML = 46; non-ccRCC = 21) for model development and 39 (fpAML = 24; non-ccRCC = 15) for validation. Patients underwent preoperative magnetic resonance imaging (MRI), including diffusion-weighted imaging. We extracted 45 texture features using a software with volumes of interest on ADC maps. Receiver operating characteristic curve analysis was performed to compare the diagnostic performance between the random forest (RF) model (derived from extracted texture features) and conventional subjective evaluation using computed tomography and MRI by radiologists. Results: RF analysis revealed that grey-level zone length matrix long-zone high grey-level emphasis was the dominant texture feature for diagnosing fpAML. The area under the curve (AUC) of the RF model to distinguish fpAMLs from non-ccRCCs was not significantly different between the validation and development cohorts (p = .19). In the validation cohort, the AUC of the RF model was similar to that of board-certified radiologists (p = .46) and significantly higher than that of radiology residents (p = .03). Conclusions: Texture analysis of ADC maps demonstrated similar diagnostic performance to that of board-certified radiologists for discriminating between fpAMLs and non-ccRCCs. Diagnostic performances in the development and validation cohorts were comparable despite using data from different imaging device manufacturers and institutions