Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Purpose: This study compared the diagnostic performance of two attenuation imaging (ATI) modes—low-frequency (3 MHz) and high-frequency (4 MHz)—for assessing hepatic steatosis, with histopathological hepatic fat fraction (HFF) as the reference standard. Methods: This prospective single-center study enrolled participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) scheduled for liver biopsy or surgery between June 2023 and June 2024. Attenuation coefficient (AC) values were consecutively measured using low- and high-frequency ATI modes, while the skin-to-region of interest distance (SRD) was measured simultaneously. Spearman correlation analysis evaluated the relationships of AC with HFF and SRD, and linear regression identified factors affecting AC. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: In total, 119 participants (mean age, 37.2±12.0 years; 87 men) were included, with 73 (61.3%) diagnosed with MASLD. HFF ranged from 0% to 50%. The AC values in the lowfrequency mode were significantly higher than those in the high-frequency mode (0.61 vs. 0.54 dB/cm/MHz, P<0.001). HFF significantly influenced AC in both modes, whereas SRD affected AC only in the high-frequency mode (P<0.001). AC correlated positively with HFF in both modes (r_s≥0.514, P<0.001) and negatively with SRD in the high-frequency mode (r_s=-0.338, P<0.001). The AUROC for hepatic steatosis did not differ significantly between the two modes (0.751 vs. 0.771; P=0.609). Conclusion The low-frequency mode produced higher AC values than the high-frequency mode and demonstrated comparable diagnostic accuracy for assessing hepatic steatosis. Unlike the high-frequency mode, the low-frequency mode was not influenced by SRD.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Purpose: This study compared the diagnostic performance of two attenuation imaging (ATI) modes—low-frequency (3 MHz) and high-frequency (4 MHz)—for assessing hepatic steatosis, with histopathological hepatic fat fraction (HFF) as the reference standard. Methods: This prospective single-center study enrolled participants with suspected metabolic dysfunction-associated steatotic liver disease (MASLD) scheduled for liver biopsy or surgery between June 2023 and June 2024. Attenuation coefficient (AC) values were consecutively measured using low- and high-frequency ATI modes, while the skin-to-region of interest distance (SRD) was measured simultaneously. Spearman correlation analysis evaluated the relationships of AC with HFF and SRD, and linear regression identified factors affecting AC. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: In total, 119 participants (mean age, 37.2±12.0 years; 87 men) were included, with 73 (61.3%) diagnosed with MASLD. HFF ranged from 0% to 50%. The AC values in the lowfrequency mode were significantly higher than those in the high-frequency mode (0.61 vs. 0.54 dB/cm/MHz, P<0.001). HFF significantly influenced AC in both modes, whereas SRD affected AC only in the high-frequency mode (P<0.001). AC correlated positively with HFF in both modes (r_s≥0.514, P<0.001) and negatively with SRD in the high-frequency mode (r_s=-0.338, P<0.001). The AUROC for hepatic steatosis did not differ significantly between the two modes (0.751 vs. 0.771; P=0.609). Conclusion The low-frequency mode produced higher AC values than the high-frequency mode and demonstrated comparable diagnostic accuracy for assessing hepatic steatosis. Unlike the high-frequency mode, the low-frequency mode was not influenced by SRD.

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, affecting millions annually and posing a significant global health concern. Traditional therapies for UTIs are becoming increasingly ineffective due to rising drug resistance and their tendency to disrupt the host's healthy microbiota, leading to further side effects. Consequently, there is an urgent need to develop alternative therapeutic agents that differ from conventional regimens and have fewer or no side effects. In this context, microbiome therapeutics offer a promising solution, given their demonstrated efficacy against various infectious diseases. Advances in scientific technology, particularly next-generation sequencing, have deepened our understanding of urinary microbiome dynamics, revealing a complex interplay within the urobiome that influences the onset and progression of UTIs. Uropathogenic bacteria do not solely cause UTIs; shifts in the composition of the urinary microbiome and interactions within the microbial community, known as host-microbiota interactions, also play a significant role. Although recent studies underscore the potential of targeting the urinary microbiome to manage UTIs and related complications, this field is still emerging and faces numerous regulatory and technical challenges. Further in-depth and comprehensive research is required to advance this pioneering concept into clinical practice.

Background: s/Aims: Hepatocellular carcinoma (HCC) is the sixth most common cancer and second leading cause of cancer-related deaths in South Korea. This study evaluated the characteristics of Korean patients newly diagnosed with HCC in 2016-2018. Methods: Data from the Korean Primary Liver Cancer Registry (KPLCR), a representative database of patients newly diagnosed with HCC in South Korea, were analyzed. This study investigated 4,462 patients with HCC registered in the KPLCR in 2016-2018. Results: The median patient age was 63 years (interquartile range, 55-72). 79.7% of patients were male. Hepatitis B infection was the most common underlying liver disease (54.5%). The Barcelona Clinic Liver Cancer (BCLC) staging system classified patients as follows: stage 0 (14.9%), A (28.8%), B (7.5%), C (39.0%), and D (9.8%). The median overall survival was 3.72 years (95% confidence interval, 3.47-4.14), with 1-, 3-, and 5-year overall survival rates of 71.3%, 54.1%, and 44.3%, respectively. In 2016-2018, there was a significant shift toward BCLC stage 0-A and Child-Turcotte-Pugh liver function class A (P<0.05), although survival rates did not differ by diagnosis year. In the treatment group (n=4,389), the most common initial treatments were transarterial therapy (31.7%), surgical resection (24.9%), best supportive care (18.9%), and local ablation therapy (10.5%). Conclusions Between 2016 and 2018, HCC tended to be diagnosed at earlier stages, with better liver function in later years. However, since approximately half of the patients remained diagnosed at an advanced stage, more rigorous and optimized HCC screening strategies should be implemented.

Hepatocellular adenomas (HCAs) are benign monoclonal liver tumors. Advances in molecular studies have led to the identification of distinct subtypes of HCA with unique pathways, clinical characteristics, and complication risks, underscoring the need for precise diagnosis and tailored management. Malignant transformation and bleeding remain significant concerns. Imaging plays a crucial role in the identification of these subtypes, offering a non-invasive method to guide clinical decision-making. Most studies involving patients with HCAs have been conducted in Western populations; however, the number of studies focused on Asian population has increased in recent years. HCAs exhibit distinct features in Asian population, such as a higher prevalence among male patients and specific subtypes (e.g., inflammatory HCAs). Current clinical guidelines are predominantly influenced by Western data, which may not fully capture these regional differences in epidemiology and subtype distribution. Therefore, this review presents the updated molecular classification of HCAs and their epidemiologic differences between Asian and Western populations, and discuss the role of imaging techniques, particularly magnetic resonance imaging using hepatobiliary contrast agents, in classifying the subtypes and predicting the risk of hepatocellular carcinoma.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.