Background/Aims: Hepatocellular carcinoma (HCC) exhibits significant sex disparities in incidence, yet its molecular mechanisms remain unclear. We explored the role of telomerase reverse transcriptase (TERT) genetic alterations and hepatitis B virus (HBV) integration, both known major contributors to HCC, in sex-specific risk for HBV-related HCC. Methods: We examined 310 HBV-related HCC tissues to investigate sex-specific TERT promoter (TERT-pro) mutations and HBV integration profiles, stratified by sex and age, and validated with single-cell RNA sequencing (scRNA-seq) data. Results: Tumors predominantly exhibited TERT-pro mutations (26.0% vs. 0%) and HBV-TERT integration (37.0% vs. 3.0%) compared to non-tumorous tissues. While TERT-pro mutations increased with age in both sexes, younger males (≤60 years) showed marked predominance compared to younger females. Males had significantly more HBV integrations at younger ages, while females initially had fewer integrations that gradually increased with age. Younger males' integrations showed significantly greater enrichment in the TERT locus compared to younger females, alongside a preference for promoters, PreS/S regions, and CpG islands. Overall, TERT genetic alterations were significantly sex-differential in younger individuals (75.3% in males vs. 23.1% in females) but not in older individuals (76.9% vs. 83.3%, respectively). These alterations were associated with increased TERT expression. The skewed TERT abnormalities in younger males were further corroborated by independent scRNA-seq data. Conclusions Our findings highlight the critical role of TERT alterations and HBV integration patterns in the male predominance of HCC incidence among younger HBV carriers, offering insights for future exploration to optimize sex-specific patient care and HCC surveillance strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits significant sex disparities in incidence, yet its molecular mechanisms remain unclear. We explored the role of telomerase reverse transcriptase (TERT) genetic alterations and hepatitis B virus (HBV) integration, both known major contributors to HCC, in sex-specific risk for HBV-related HCC. Methods: We examined 310 HBV-related HCC tissues to investigate sex-specific TERT promoter (TERT-pro) mutations and HBV integration profiles, stratified by sex and age, and validated with single-cell RNA sequencing (scRNA-seq) data. Results: Tumors predominantly exhibited TERT-pro mutations (26.0% vs. 0%) and HBV-TERT integration (37.0% vs. 3.0%) compared to non-tumorous tissues. While TERT-pro mutations increased with age in both sexes, younger males (≤60 years) showed marked predominance compared to younger females. Males had significantly more HBV integrations at younger ages, while females initially had fewer integrations that gradually increased with age. Younger males' integrations showed significantly greater enrichment in the TERT locus compared to younger females, alongside a preference for promoters, PreS/S regions, and CpG islands. Overall, TERT genetic alterations were significantly sex-differential in younger individuals (75.3% in males vs. 23.1% in females) but not in older individuals (76.9% vs. 83.3%, respectively). These alterations were associated with increased TERT expression. The skewed TERT abnormalities in younger males were further corroborated by independent scRNA-seq data. Conclusions Our findings highlight the critical role of TERT alterations and HBV integration patterns in the male predominance of HCC incidence among younger HBV carriers, offering insights for future exploration to optimize sex-specific patient care and HCC surveillance strategies.

Background/Aims: Hepatocellular carcinoma (HCC) exhibits significant sex disparities in incidence, yet its molecular mechanisms remain unclear. We explored the role of telomerase reverse transcriptase (TERT) genetic alterations and hepatitis B virus (HBV) integration, both known major contributors to HCC, in sex-specific risk for HBV-related HCC. Methods: We examined 310 HBV-related HCC tissues to investigate sex-specific TERT promoter (TERT-pro) mutations and HBV integration profiles, stratified by sex and age, and validated with single-cell RNA sequencing (scRNA-seq) data. Results: Tumors predominantly exhibited TERT-pro mutations (26.0% vs. 0%) and HBV-TERT integration (37.0% vs. 3.0%) compared to non-tumorous tissues. While TERT-pro mutations increased with age in both sexes, younger males (≤60 years) showed marked predominance compared to younger females. Males had significantly more HBV integrations at younger ages, while females initially had fewer integrations that gradually increased with age. Younger males' integrations showed significantly greater enrichment in the TERT locus compared to younger females, alongside a preference for promoters, PreS/S regions, and CpG islands. Overall, TERT genetic alterations were significantly sex-differential in younger individuals (75.3% in males vs. 23.1% in females) but not in older individuals (76.9% vs. 83.3%, respectively). These alterations were associated with increased TERT expression. The skewed TERT abnormalities in younger males were further corroborated by independent scRNA-seq data. Conclusions Our findings highlight the critical role of TERT alterations and HBV integration patterns in the male predominance of HCC incidence among younger HBV carriers, offering insights for future exploration to optimize sex-specific patient care and HCC surveillance strategies.

Arterial pH reflects both metabolic and respiratory distress in cardiac arrest and has prognostic implications. However, it was excluded from the 2024 update of the Utstein out-of-hospital cardiac arrest (OHCA) registry template. We investigated the rationale for including arterial pH into models predicting clinical outcomes. Methods: Data were sourced from the Korean Cardiac Arrest Research Consortium, a nationwide OHCA registry (NCT03222999). Prediction models were constructed using logistic regression, random forest, and eXtreme Gradient Boosting frameworks. Each framework included three model types: pH, low-flow time, and combined models. Then the area under the receiver operating characteristic curve (AUROC) of each predicting model was compared. The primary outcome was 30- day death or neurologically unfavorable status (cerebral performance category ≥3). Results: Among the 15,765 patients analyzed, 92.2% experienced death or unfavorable neurological outcomes. The predicting performance of the models including pH (AUROC, 0.92–0.94) were comparable to the models including low-flow time in all frameworks (0.93–0.94) (all P>0.05). Inclusion of pH into low-flow time models consistently showed higher AUROCs than individual models in all frameworks (AUROC, 0.93–0.95; all P<0.05). Conclusions: The predicting performance of models including arterial pH was comparable to models including low-flow time, and addition of arterial pH into low-flow time models could increase the performance of the models. Key Words: blood pH; hydrogen-ion con

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Purpose: In this study, we evaluated 66 patients diagnosed with adenoid cystic carcinoma (ACC) enrolled in two Korean Cancer Study Group trials to investigate the response and progression patterns in recurrent and/or metastatic ACC treated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs). Materials and Methods: We evaluated 66 patients diagnosed with ACC who were enrolled in the Korean Cancer Study Group trials. The tumor measurements, clinical data, treatment outcomes, and progression patterns of therapy were analyzed. Results: In the 66 patients (53 receiving axitinib and 13 receiving nintedanib), the disease control rate was 61%, and three patients achieved partial response. The median follow-up, median progression-free survival (PFS), overall survival, and 6-month PFS rate were 27.6%, 12.4%, and 18.1% months and 62.1%, respectively. Among 42 patients who experienced progression, 27 (64.3%) showed target lesion progression. Bone metastasis was an independent poor prognostic factor. Conclusion Overall, most patients demonstrated stable disease with prolonged PFS; however, prominent target lesion progression occurred in some patients. Thus, PFS may capture VEGFR-TKI efficacy better than the objective response rate.