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

Diagnostic value of computed high b-value whole-body diffusion-weighted imaging for primary prostate cancer

Purpose: To investigate the utility of post-acquisition computed diffusion-weighted imaging (cDWI) for primary prostate cancer (PCa) evaluation in biparametric whole-body MRI (bpWB-MRI). Methods: Patients who underwent pelvic MRI for PCa screening and subsequent bpWB-MRI for staging were included. Two radiologists assessed the diagnostic performance of the following datasets for clinically significant PCa diagnosis (grade group >= 2 according to the Prostate Imaging-Reporting and Data System, version 2.1): bpMRI(2000) (axial DWI scans with a b-value of 2,000 s/mm(2) + axial T2WI scans from pre-biopsy pelvic MRI), computed bpWB-MRI2000 (computed WB-DWI scans with a b-value of 2,000 s/mm(2) + axial WB-T2WI scans), and native bpWB-MRI1000 (native axial WB-DWI scans with a b-value of 1,000 s/mm(2) + axial WB-T2WI scans). Systemic biopsy was used as reference standard. Results: Fifty-one patients with PCa were included. The areas under the curve (AUCs) of bpMRI(2000) (0.89 for reader 1 and 0.86 for reader 2) and computed bpWB-MRI2000 (0.86 for reader 1 and 0.83 for reader 2) were significantly higher (p < 0.001) than those of native bpWB-MRI1000 (0.67 for both readers). No significant difference was observed between the AUCs of bpMRI(2000) and computed bpWB-MRI2000 (p = 0.10 for reader 1 and p = 0.25 for reader 2). Conclusions: The diagnostic performance of computed bpWB-MRI2000 was similar to that of dedicated pelvic bpMRI(2000) for primary PCa evaluation. cDWI can be recommended for implementation in standard WB-MRI protocols to facilitate a one-step evaluation for concurrent detection of primary and metastatic PCa

Clinical utility of the Bosniak classification version 2019:Diagnostic value of adding magnetic resonance imaging to computed tomography examination

Purpose: To assess the impact of the updated Bosniak classification (BC2019) for cystic renal masses (CRMs) on interobserver agreement between radiologists and urologists and the diagnostic value of adding MRI to CT examination (combined CT/MRI). Method: This study included 103 CRMs from 83 consecutive patients assessed using contrast-enhanced CT and MRI between 2010 and 2016. Nine readers in three groups (three radiologists, three radiology residents, and three urologists) reviewed CT alone and the combined CT/MRI using BC2019. Bosniak category was determined by consensus in each group for diagnosing malignancy, with a cut-off category of ?>= III. Interobserver agreement was assessed using Fleiss' kappa values. The effect of CT or combined CT/MRI on the diagnosis of malignancy was assessed using McNemar's test. Results: Interobserver agreement of BC2019 for CT alone was substantial for radiologists and residents, moderate for urologists (0.77, 0.63, and 0.58, respectively). Interobserver agreement of BC2019 for combined CT/MRI was substantial for all three groups (radiologists: 0.78; residents: 0.65; and urologists: 0.61). Among residents, the sensitivity/specificity/accuracy rates of combined CT/MRI vs. CT alone were 82.1/74.7/76.7% vs. 75.0/66.7/68.9%, and specificity and accuracy were significantly higher for combined CT/MRI than that for CT alone (p = 0.03 and 0.008, respectively). Similarly, sensitivity/specificity/accuracy values were significantly higher for combined CT/MRI among urologists (78.6/73.3/74.8% vs. 64.3/64.0/64.1%, p = 0.04/0.04/0.008). However, sensitivity/specificity/accuracy did not significantly differ between the two among radiologists (89.3/74.7/78.6% vs. 85.7/73.3/76.7%, p = 0.32/0.56/0.32). Conclusions: Combined CT/MRI is useful for diagnosing malignancy in patients with CRMs using BC2019, especially for non-expert readers

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

Symbolic modeling of driving behavior based on hierarchical segmentation and formal grammar

Abstract — This paper presents a new hierarchical segmen-tation of the observed driving behavioral data based on the multiple levels of abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering tech-nique, the hierarchical segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed