Objectives: Allergic rhinitis (AR) is a chronic inflammation of the nasal mucosa triggered by environmental allergens. Although its pathophysiology has been extensively investigated, the influence of environmental aggravating factors—particularly combined pollutant exposure—remains insufficiently characterized. This study aimed to assess the impact of coexposure to PM2.5, formaldehyde, and Zn on allergic inflammation in a murine AR model and to delineate the associated immunological and histopathological responses. Methods: Female BALB/c mice were sensitized with ovalbumin (OVA) and challenged intranasally to induce AR. On days 21–24, the mice were exposed to PM2.5, formaldehyde, and Zn, either individually or in combination with OVA. Allergic symptoms were evaluated through behavioral observation, while immune responses were assessed by analyzing nasal and bronchoalveolar lavage fluids (NALF and BALF), serum immunoglobulin levels, nasal histopathology, and cytokine profiles. Results: Combined exposure to PM2.5, formaldehyde, and Zn significantly intensified allergic inflammation compared with single exposures. Coexposure to PM2.5 and Zn led to synergistic increases in total and OVA-specific immunoglobulin E levels, eosinophilic infiltration, nasal rubbing, sneezing, and Th2/Th17 cytokine levels in NALF and BALF. Histological analysis demonstrated epithelial thickening and goblet cell hyperplasia after combined exposure. Other combinations, including PM2.5 with formaldehyde, also produced additive or modestly amplified inflammatory responses. Conclusions Coexposure to PM2.5, formaldehyde, and Zn aggravated allergic inflammation in an OVA-induced murine model, with PM2.5+Zn yielding the strongest synergistic effects. These findings emphasize the role of pollutant–pollutant interactions in allergic airway diseases and highlight the need for further research to clarify long-term health effects and relevance to human disease.

Background/Aims: Various expanded criteria (EC) for liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) have been proposed to avoid the narrow nature of the Milan criteria (MC). To investigate which EC predicts more favorable outcomes in terms of overall survival (OS) and recurrence-free survival (RFS), we conducted a network meta-analysis (NMA). Methods: A database search was conducted on PubMed, Embase, and the Cochrane Library, to identify studies comparing OS and RFS between patients within the MC and those exceeding the MC but within the EC. Hazard ratios (HRs) were pooled using a random-effects NMA and validated in an in-house cohort of 1,008 LT recipients. Results: Among 22,466 articles identified, 35 studies with 45 pairwise comparisons were included in the NMA along with 8 different EC. The University of California San Francisco (HR, 1.43; 95% CI, 1.19–1.71), Up-to-Seven (HR, 1.50; 95% CI, 1.15–1.97), and Hangzhou criteria (HR, 1.69; 95% CI, 1.11–2.57) showed inferior OS to the MC. The MC ranked highest for both OS and RFS, followed by Metroticket 2.0 for OS and the Asan criteria for RFS. In the validation cohort, both Metroticket 2.0 and AFP model yielded more favorable HCC-specific mortality than other EC. Conclusions Several EC, of which those of Metroticket 2.0 were the best, yielded comparable outcomes to the MC. AFP-based EC such as Metroticket 2.0 and AFP model appeared to be useful in both the NMA and the validation cohort, suggesting a potential role in identifying selected low-risk patients beyond the MC.

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.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

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.

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.

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.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.