Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Background: Although patients with lymphoma appear particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the clinical evolution of coronavirus disease 2019 (COVID-19) in a patient with lymphoid malignancies has been under-represented, especially in relation to chemo-, chemo-immunotherapy. Materials and Methods: Among adult patients with lymphoma receiving treatment in a specialized lymphoma center at a 500-bed, university-affiliated hospital, we retrospectively reviewed the medical records of patients diagnosed with SARS-CoV-2 infection from January 2020 to April 2022. Results: A total of 117 patients with a median age of 53 years were included. One hundred twelves (95.7%) were non-Hodgkin lymphoma. Eighty-six patients (73.5%) were on active chemotherapy and 9 were post stem cell transplant state. Sixty-one patients had more than one comorbidity and 29 had hypogammaglobulinemia. Thirty-four patients (29.1%) had never received a COVID-19 vaccine. During a median follow-up of 134 days, COVID-19 pneumonia developed in 37 patients (31.6%). Excluding three patients who died before the 30 days, 31 out of 34 patients had ongoing symptomatic COVID-19. Eleven patients (9.4%) had post COVID-19 lung condition that persisted 90 days after COVID-19 diagnosis. Overall mortality was 10.3% (12 of 117), which was higher in patients with pneumonia. In multivariate analyses, age 65 years or older, follicular lymphoma, receiving rituximab maintenance therapy, and lack of vaccination were significantly associated with the development of COVID-19 pneumonia. Conclusion Patients with lymphoma are at high risk for developing pneumonia after SARS-CoV-2 infection and suffer from prolonged symptoms. More aggressive vaccination and protective measures for patients with lymphoma who have impaired humoral response related to rituximab maintenance therapy and hypogammaglobulinemia are needed.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

With advancements in both pharmacologic and non-pharmacologic treatments, significant changes have occurred in heart failure (HF) management. The previous Korean HF registries, namely the Korea Heart Failure Registry (KorHF-registry) and Korean Acute Heart Failure Registry (KorAHF-registry), no longer accurately reflect contemporary acute heart failure (AHF) patients. Our objective is to assess contemporary AHF patients through a nationwide registry encompassing various aspects, such as clinical characteristics, management approaches, hospital course, and long-term outcomes of individuals hospitalized for AHF in Korea. This prospective observational multicenter cohort study (KorHF III) is organized by the Korean Society of Heart Failure. We aim to prospectively enroll 7,000 or more patients hospitalized for AHF at 47 tertiary hospitals in Korea starting from March 2018. Eligible patients exhibit signs and symptoms of HF and demonstrate either lung congestion or objective evidence of structural or functional cardiac abnormalities in echocardiography, or isolated right-sided HF. Patients will be followed up for up to 5 years after enrollment in the registry to evaluate long-term clinical outcomes. KorHF III represents the nationwide AHF registry that will elucidate the clinical characteristics, management strategies, and outcomes of contemporary AHF patients in Korea.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

Background: Although patients with lymphoma appear particularly vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the clinical evolution of coronavirus disease 2019 (COVID-19) in a patient with lymphoid malignancies has been under-represented, especially in relation to chemo-, chemo-immunotherapy. Materials and Methods: Among adult patients with lymphoma receiving treatment in a specialized lymphoma center at a 500-bed, university-affiliated hospital, we retrospectively reviewed the medical records of patients diagnosed with SARS-CoV-2 infection from January 2020 to April 2022. Results: A total of 117 patients with a median age of 53 years were included. One hundred twelves (95.7%) were non-Hodgkin lymphoma. Eighty-six patients (73.5%) were on active chemotherapy and 9 were post stem cell transplant state. Sixty-one patients had more than one comorbidity and 29 had hypogammaglobulinemia. Thirty-four patients (29.1%) had never received a COVID-19 vaccine. During a median follow-up of 134 days, COVID-19 pneumonia developed in 37 patients (31.6%). Excluding three patients who died before the 30 days, 31 out of 34 patients had ongoing symptomatic COVID-19. Eleven patients (9.4%) had post COVID-19 lung condition that persisted 90 days after COVID-19 diagnosis. Overall mortality was 10.3% (12 of 117), which was higher in patients with pneumonia. In multivariate analyses, age 65 years or older, follicular lymphoma, receiving rituximab maintenance therapy, and lack of vaccination were significantly associated with the development of COVID-19 pneumonia. Conclusion Patients with lymphoma are at high risk for developing pneumonia after SARS-CoV-2 infection and suffer from prolonged symptoms. More aggressive vaccination and protective measures for patients with lymphoma who have impaired humoral response related to rituximab maintenance therapy and hypogammaglobulinemia are needed.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.