Thymic diseases such as thymic hyperplasia, thymic cysts, thymoma, and thymic carcinoma are common causes of mediastinal masses that present with diverse clinical and radiological features. Magnetic resonance imaging (MRI) is a pivotal tool for evaluating thymic pathologies as it offers superior soft-tissue contrast and has the ability to distinguish between benign and malignant lesions. Thymic MRI protocols include T1- and T2-weighted imaging, diffusion-weighted MRI (DW-MRI) with apparent diffusion coefficient mapping, and contrast-enhanced MRI (CE-MRI), each offering unique diagnostic insights into the composition and behavior of thymic lesions. However, interpreting MRI findings in thymic diseases may present challenges. Thymic cysts containing hemorrhage or proteinaceous material may mimic solid lesions owing to altered signal intensities, necessitating DW-MRI and CE-MRI for accurate differentiation. Small thymic lesions, particularly those <1 cm in diameter, are susceptible to signal distortion and partial volume effects, complicating their detection and characterization. Furthermore, respiratory and cardiac motion artifacts can degrade the image quality and obscure important diagnostic details, especially in lesions near the heart and lungs. Despite these challenges, MRI remains a critical imaging modality for assessing and managing thymic diseases, offering detailed tissue characterization. Interpretive pitfalls and technical limitations underscore the importance of employing optimized imaging protocols and expert analyses to ensure diagnostic accuracy and guide appropriate clinical decision-making.

Purpose: Adjuvant chemotherapy (AC) is recommended for muscle-invasive or lymph node-positive upper urinary tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU). However, disease recurrences are frequently observed in pT1 disease, and AC may increase the risk of overtreatment in pT2 UTUC patients. This study aimed to validate a risk-adapted scoring model for selecting UTUC patients with ≤pT2 disease who would benefit from AC. Materials and Methods: We retrospectively analyzed 443 ≤pT2 UTUC patients who underwent RNU. A risk-adapted scoring model was applied, categorizing patients into low- or high-risk groups. Recurrence-free survival (RFS) and cancer-specific survival (CSS) were analyzed according to risk group. Results: Overall, 355 patients (80.1%) and 88 patients (19.9%) were categorized into the low- and high-risk groups, respectively, with the latter having higher pathological stages, concurrent carcinoma in situ, and synchronous bladder tumors. Disease recurrence occurred in 45 patients (10.2%), among whom 19 (5.4%) and 26 (29.5%) belonged to the low- and high-risk groups, respectively (p<0.001). High-risk patients had significantly shorter RFS (64.3% vs. 93.6% at 60 months; hazard ratio [HR] 13.66; p<0.001) and worse CSS (80.7% vs. 91.5% at 60 months; HR 4.25; p=0.002). Multivariate analysis confirmed that pT2 stage and the high-risk group were independent predictors of recurrence and cancer-specific death (p<0.001). Decision curve analysis for RFS showed larger net benefits with our model than with the T stage model. Conclusions The risk-adapted scoring model effectively predicts recurrence and identifies optimal candidates for AC post RNU in non-metastatic UTUC.

Thymic diseases such as thymic hyperplasia, thymic cysts, thymoma, and thymic carcinoma are common causes of mediastinal masses that present with diverse clinical and radiological features. Magnetic resonance imaging (MRI) is a pivotal tool for evaluating thymic pathologies as it offers superior soft-tissue contrast and has the ability to distinguish between benign and malignant lesions. Thymic MRI protocols include T1- and T2-weighted imaging, diffusion-weighted MRI (DW-MRI) with apparent diffusion coefficient mapping, and contrast-enhanced MRI (CE-MRI), each offering unique diagnostic insights into the composition and behavior of thymic lesions. However, interpreting MRI findings in thymic diseases may present challenges. Thymic cysts containing hemorrhage or proteinaceous material may mimic solid lesions owing to altered signal intensities, necessitating DW-MRI and CE-MRI for accurate differentiation. Small thymic lesions, particularly those <1 cm in diameter, are susceptible to signal distortion and partial volume effects, complicating their detection and characterization. Furthermore, respiratory and cardiac motion artifacts can degrade the image quality and obscure important diagnostic details, especially in lesions near the heart and lungs. Despite these challenges, MRI remains a critical imaging modality for assessing and managing thymic diseases, offering detailed tissue characterization. Interpretive pitfalls and technical limitations underscore the importance of employing optimized imaging protocols and expert analyses to ensure diagnostic accuracy and guide appropriate clinical decision-making.

Thymic diseases such as thymic hyperplasia, thymic cysts, thymoma, and thymic carcinoma are common causes of mediastinal masses that present with diverse clinical and radiological features. Magnetic resonance imaging (MRI) is a pivotal tool for evaluating thymic pathologies as it offers superior soft-tissue contrast and has the ability to distinguish between benign and malignant lesions. Thymic MRI protocols include T1- and T2-weighted imaging, diffusion-weighted MRI (DW-MRI) with apparent diffusion coefficient mapping, and contrast-enhanced MRI (CE-MRI), each offering unique diagnostic insights into the composition and behavior of thymic lesions. However, interpreting MRI findings in thymic diseases may present challenges. Thymic cysts containing hemorrhage or proteinaceous material may mimic solid lesions owing to altered signal intensities, necessitating DW-MRI and CE-MRI for accurate differentiation. Small thymic lesions, particularly those <1 cm in diameter, are susceptible to signal distortion and partial volume effects, complicating their detection and characterization. Furthermore, respiratory and cardiac motion artifacts can degrade the image quality and obscure important diagnostic details, especially in lesions near the heart and lungs. Despite these challenges, MRI remains a critical imaging modality for assessing and managing thymic diseases, offering detailed tissue characterization. Interpretive pitfalls and technical limitations underscore the importance of employing optimized imaging protocols and expert analyses to ensure diagnostic accuracy and guide appropriate clinical decision-making.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Viral load and the duration of viral shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important determinants of the transmission of coronavirus disease 2019.In this study, we examined the effects of viral doses on the lung and spleen of K18-hACE2 transgenic mice by temporal histological and transcriptional analyses. Approximately, 1×105 plaque-forming units (PFU) of SARS-CoV-2 induced strong host responses in the lungs from 2 days post inoculation (dpi) which did not recover until the mice died, whereas responses to the virus were obvious at 5 days, recovering to the basal state by 14 dpi at 1×102 PFU. Further, flow cytometry showed that number of CD8+ T cells continuously increased in 1×102 PFU-virusinfected lungs from 2 dpi, but not in 1×105 PFU-virus-infected lungs. In spleens, responses to the virus were prominent from 2 dpi, and number of B cells was significantly decreased at 1×105PFU; however, 1×102 PFU of virus induced very weak responses from 2 dpi which recovered by 10 dpi. Although the defense responses returned to normal and the mice survived, lung histology showed evidence of fibrosis, suggesting sequelae of SARS-CoV-2 infection. Our findings indicate that specific effectors of the immune response in the lung and spleen were either increased or depleted in response to doses of SARS-CoV-2. This study demonstrated that the response of local and systemic immune effectors to a viral infection varies with viral dose, which either exacerbates the severity of the infection or accelerates its elimination.