Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Purpose: Renal allograft rejection, either acute or chronic, is prevalent among many recipients. This study aimed to identify multiple Doppler ultrasound parameters for predicting renal allograft rejection. Materials and Methods: Between November 2021 and April 2022, 61 renal allograft recipients were studied prospectively after excluding two patients with dual transplants and seven with hydronephrosis. The analysis excluded 11 cases (10 due to missing Doppler data or pathology reports and one due to a high interquartile range/median dispersion value), resulting in a final analysis of 50 patients. Clinical characteristics, color Doppler imaging, superb microvascular imaging, and shear-wave imaging parameters were assessed by three experienced genitourinary radiologists. The Banff classification of the biopsy tissue served as the reference standard. Univariable and multivariable logistic regression, contingency matrices, and multiple machine-learning models were employed to estimate the associations. Results: Fifty kidney transplant recipients (mean age, 53.26±8.86 years; 29 men) were evaluated. Elasticity (≤14.8 kPa) demonstrated significant associations for predicting the combination of (borderline) T cell-mediated rejection (TCMR) categories (Banff categories 3 and 4) (p=0.006) and yielded equal or higher area under the receiver operating characteristics curve (AUC) values compared to various classifiers. Dispersion (>15.0 m/s/kHz) was the only significant factor for predicting the combination of nonTCMR categories (Banff categories 2, 5, and 6) (p=0.026) and showed equal or higher AUC values than multiple machine learning classifiers. Conclusion Elasticity (≤14.8 kPa) showed a significant association with the combination of (borderline) TCMR categories, whereas dispersion (>15.0 m/s/kHz) was significantly associated with the combination of non-TCMR categories in renal allografts.

Objective: To elucidate the use of radiological studies, including nuclear medicine, and biopsy for the diagnosis and staging of prostate cancer (PCA) in clinical practice and understand the current status of PCA in Asian countries via an international survey. Materials and Methods: The Asian Prostate Imaging Working Group designed a survey questionnaire with four domains focused on prostate magnetic resonance imaging (MRI), other prostate imaging, prostate biopsy, and PCA backgrounds. The questionnaire was sent to 111 members of professional affiliations in Korea, Japan, Singapore, and Taiwan who were representatives of their working hospitals, and their responses were analyzed. Results: This survey had a response rate of 97.3% (108/111). The rates of using 3T scanners, antispasmodic agents, laxative drugs, and prostate imaging-reporting and data system reporting for prostate MRI were 21.6%−78.9%, 22.2%−84.2%, 2.3%−26.3%, and 59.5%−100%, respectively. Respondents reported using the highest b-values of 800−2000 sec/mm2 and fields of view of 9−30 cm. The prostate MRI examinations per month ranged from 1 to 600, and they were most commonly indicated for biopsy-naïve patients suspected of PCA in Japan and Singapore and staging of proven PCA in Korea and Taiwan.The most commonly used radiotracers for prostate positron emission tomography are prostate-specific membrane antigen in Singapore and fluorodeoxyglucose in three other countries. The most common timing for prostate MRI was before biopsy (29.9%). Prostate-targeted biopsies were performed in 63.8% of hospitals, usually by MRI-ultrasound fusion approach. The most common presentation was localized PCA in all four countries, and it was usually treated with radical prostatectomy. Conclusion This survey showed the diverse technical details and the availability of imaging and biopsy in the evaluation of PCA. This suggests the need for an educational program for Asian radiologists to promote standardized evidence-based imaging approaches for the diagnosis and staging of PCA.

BACKGROUND: Bone marrow-derived mesenchymal stem cells (BMSCs) and bone morphogenetic protein-2 (BMP-2) have been studied for bone repair because they have regenerative potential to differentiate into osteoblasts. The development of injectable and in situ three-dimensional (3D) scaffolds to proliferate and differentiate BMSCs and deliver BMP-2 is a crucial technology in BMSC-based tissue engineering. METHODS: The proliferation of mouse BMSCs (mBMSCs) in collagen/poly-γ-glutamic acid (Col/γ-PGA) hydrogel was evaluated using LIVE/DEAD and acridine orange and propidium iodide assays. In vitro osteogenic differentiation and the gene expression level of Col/γ-PGA(mBMSC/BMP-2) were assessed by alizarin red S staining and quantitative reversetranscription polymerase chain reaction. The bone regeneration effect of Col/γ-PGA(mBMSC/BMP-2) was evaluated in a mouse calvarial bone defect model. The cranial bones of the mice were monitored by micro-computed tomography and histological analysis. RESULTS: The developed Col/γ-PGA hydrogel showed low viscosity below ambient temperature, while it provided a high elastic modulus and viscous modulus at body temperature. After gelation, the Col/γ-PGA hydrogel showed a 3D and interconnected porous structure, which helped the effective proliferation of BMSCs with BMP-2. The Col/γ-PGA (mBMSC/BMP-2) expressed more osteogenic genes and showed effective orthotopic bone formation in a mouse model with a critical-sized bone defect in only 3–4 weeks. CONCLUSION The Col/γ-PGA(mBMSC/BMP-2) hydrogel was suggested to be a promising platform by combining collagen as a major component of the extracellular matrix and γ-PGA as a viscosity reducer for easy handling at room temperature in BMSC-based bone tissue engineering scaffolds.

Objective: To categorize multiparametric MRI features of Bacillus Calmette–Guérin (BCG)-related granulomatous prostatitis (GP) and discover potential manifestations for its differential diagnosis from prostate cancer. Materials and Methods: The cases of BCG-related GP in 24 male (mean age ± standard deviation, 66.0 ± 9.4 years; range, 50–88 years) pathologically confirmed between January 2011 and April 2019 were retrospectively reviewed. All patients underwent intravesical BCG therapy followed by a MRI scan. Additional follow-up MRI scans, including diffusion-weighted imaging (DWI), were performed in 19 patients. The BCG-related GP cases were categorized into three: A, B, or C. The lesions with diffusion restriction and homogeneous enhancement were classified as type A. The lesions with diffusion restriction and a poorly enhancing component were classified as type B. A low signal intensity on high b-value DWI (b = 1000 s/mm2 ) was considered characteristic of type C. Two radiologists independently interpreted the MRI scans before making a consensus about the types. Results: The median lesion size was 22 mm with the interquartile range (IQR) of 18–26 mm as measured using the initial MRI scans. The lesion types were A, B, and C in 7, 15, and 2 patients, respectively. Cohen’s kappa value for the inter-reader agreement for the interpretation of the lesion types was 0.837. On the last follow-up MRI scans of 19 patients, the size decreased (median, 5.8 mm; IQR, 3.4–8.5 mm), and the type changed from A or B to C in 11 patients. The lesions resolved in four patients. In five patients who underwent prostatectomy, caseous necrosis on histopathology matched with the non-enhancing components of type B lesions and the entire type C lesions. Conclusion BCG-related GP demonstrated three imaging patterns on multiparametric MRI. Contrast-enhanced T1-weighted imaging and DWI may play a role in its differential diagnosis from prostate cancer.

Ultrasonography is effective for imaging superficial organs, such as the scrotum. Using a highfrequency transducer, ultrasonography can identify the location and characteristics of scrotal lesions with high accuracy. The primary role of ultrasound (US) in the evaluation of a scrotal mass is to determine if it is intratesticular or extratesticular. Additional clinical information and other imaging options may be needed to diagnose benign tumors and pseudo-tumors. MRI is an effective problem-solving tool in cases with nondiagnostic US findings. CT is helpful for staging testicular cancer and localizing undescended testis. This review covers the imaging features of testicular and extratesticular tumors.

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman’s correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27–79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62–0.82). Conclusion Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.