Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

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 and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

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: 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: Recently, we developed allele-discriminating priming system (ADPS) technology. This method increases the sensitivity of conventional quantitative polymerase chain reaction up to 100 folds, with limit of detection, 0.01%, with reinforced specificity. This prospective study aimed to develop and validate the accuracy of ADPS epidermal growth factor receptor (EGFR) Mutation Test Kit using clinical specimens. Materials and Methods: In total 189 formalin-fixed paraffin-embedded tumor tissues resected from patients with non–small cell lung cancer were used to perform a comparative evaluation of the ADPS EGFR Mutation Test Kit versus the cobas EGFR Mutation Test v2, which is the current gold standard. When the two methods had inconsistent results, next-generation sequencing–based CancerSCAN was utilized as a referee. Results: The overall agreement of the two methods was 97.4% (93.9%-99.1%); the positive percent agreement, 95.0% (88.7%-98.4%); and the negative percent agreement, 100.0% (95.9%-100.0%). EGFR mutations were detected at a frequency of 50.3% using the ADPS EGFR Mutation Test Kit and 52.9% using the cobas EGFR Mutation Test v2. There were 10 discrepant mutation calls between the two methods. CancerSCAN reproduced eight ADPS results. In two cases, mutant allele fraction was ultra-low at 0.02% and 0.06%, which are significantly below the limit of detection of the cobas assay and CancerSCAN. Based on the EGFR genotyping by ADPS, the treatment options could be switched in five patients. Conclusion The highly sensitive and specific ADPS EGFR Mutation Test Kit would be useful in detecting the patients who have lung cancer with EGFR mutation, and can benefit from the EGFR targeted therapy.

This paper provides a comprehensive overview of the Cancer Public Library Database (CPLD), established under the Korean Clinical Data Utilization for Research Excellence project (K-CURE). The CPLD links data from four major population-based public sources: the Korea National Cancer Incidence Database in the Korea Central Cancer Registry, cause-of-death data in Statistics Korea, the National Health Information Database in the National Health Insurance Service, and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service. These databases are linked using an encrypted resident registration number. The CPLD, established in 2022 and updated annually, comprises 1,983,499 men and women newly diagnosed with cancer between 2012 and 2019. It contains data on cancer registration and death, demographics, medical claims, general health checkups, and national cancer screening. The most common cancers among men in the CPLD were stomach (16.1%), lung (14.0%), colorectal (13.3%), prostate (9.6%), and liver (9.3%) cancers. The most common cancers among women were thyroid (20.4%), breast (16.6%), colorectal (9.0%), stomach (7.8%), and lung (6.2%) cancers. Among them, 571,285 died between 2012 and 2020 owing to cancer (89.2%) or other causes (10.8%). Upon approval, the CPLD is accessible to researchers through the K-CURE portal. The CPLD is a unique resource for diverse cancer research to investigate medical use before a cancer diagnosis, during initial diagnosis and treatment, and long-term follow-up. This offers expanded insight into healthcare delivery across the cancer continuum, from screening to end-of-life care.

Background: Cervical esophageal cancer is a rare malignancy that requires specialized care. While definitive chemoradiation is the standard treatment approach, surgery remains a valuable option for certain patients. This study examined the surgical outcomes of patients with cervical esophageal cancer. Methods: The study involved a retrospective review and analysis of 24 patients with cervical esophageal cancer. These patients underwent surgical resection between September 1994 and December 2018. Results: The mean age of the patients was 61.0±10.2 years, and 22 (91.7%) of them were male. Furthermore, 21 patients (87.5%) had T3 or T4 tumors, and 11 (45.8%) exhibited lymph node metastasis. Gastric pull-up with esophagectomy was performed for 19 patients (79.2%), while 5 (20.8%) underwent free jejunal graft with cervical esophagectomy.The 30-day operative mortality rate was 8.3%. During the follow-up period, complications included leakage at the anastomotic site in 9 cases (37.5%) and graft necrosis of the gastric conduit in 1 case. Progression to oral feeding was achieved in 20 patients (83.3%). Fifteen patients (62.5%) displayed tumor recurrence. The median time from surgery to recurrence was 10.5 months, and the 1-year recurrence rate was 73.3%. The 1-year and 3-year survival rates were 75% and 33.3%, respectively, with a median survival period of 17 months. Conclusion Patients with cervical esophageal cancer who underwent surgical resection faced unfavorable outcomes and relatively poor survival. The selection of cases and decision to proceed with surgery should be made cautiously, considering the risk of severe complications.