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.

Despite the availability of direct-acting antivirals (DAAs) for chronic hepatitis C virus (HCV) infection in Korea, need remains for pangenotypic regimens that can be used in the presence of hepatic impairment, comorbidities, or prior treatment failure. We investigated the efficacy and safety of sofosbuvir–velpatasvir and sofosbuvir–velpatasvir–voxilaprevir for 12 weeks in HCV-infected Korean adults. Methods: This Phase 3b, multicenter, open-label study included 2 cohorts. In Cohort 1, participants with HCV genotype 1 or 2 and who were treatment-naive or treatment-experienced with interferon-based treatments, received sofosbuvir–velpatasvir 400/100 mg/day. In Cohort 2, HCV genotype 1 infected individuals who previously received an NS5A inhibitor-containing regimen ≥ 4 weeks received sofosbuvir–velpatasvir–voxilaprevir 400/100/100 mg/day. Decompensated cirrhosis was an exclusion criterion. The primary endpoint was SVR12, defined as HCV RNA < 15 IU/mL 12 weeks following treatment. Results: Of 53 participants receiving sofosbuvir–velpatasvir, 52 (98.1%) achieved SVR12. The single participant who did not achieve SVR12 experienced an asymptomatic Grade 3 ASL/ALT elevation on day 15 and discontinued treatment. The event resolved without intervention. All 33 participants (100%) treated with sofosbuvir–velpatasvir–voxilaprevir achieved SVR 12. Overall, sofosbuvir–velpatasvir and sofosbuvir–velpatasvir–voxilaprevir were safe and well tolerated. Three participants (5.6%) in Cohort 1 and 1 participant (3.0%) in Cohort 2 had serious adverse events, but none were considered treatment-related. No deaths or grade 4 laboratory abnormalities were reported. Conclusions: Treatment with sofosbuvir–velpatasvir or sofosbuvir–velpatasvir–voxilaprevir was safe and resulted in high SVR12 rates in Korean HCV patients.

Purpose: Renal tumors account for approximately 7% of all childhood cancers. These include Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor of the kidney (MRTK), renal cell carcinoma (RCC), congenital mesoblastic nephroma (CMN) and other rare tumors. We investigated the epidemiology of pediatric renal tumors in Korea. Materials and Methods: From January 2001 to December 2015, data of pediatric patients (0–18 years) newly-diagnosed with renal tumors at 26 hospitals were retrospectively analyzed. Results: Among 439 patients (male, 240), the most common tumor was WT (n=342, 77.9%), followed by RCC (n=36, 8.2%), CCSK (n=24, 5.5%), MRTK (n=16, 3.6%), CMN (n=12, 2.7%), and others (n=9, 2.1%). Median age at diagnosis was 27.1 months (range 0-225.5) and median follow-up duration was 88.5 months (range 0-211.6). Overall, 32 patients died, of whom 17, 11, 1, and 3 died of relapse, progressive disease, second malignant neoplasm, and treatment-related mortality. Five-year overall survival and event free survival were 97.2% and 84.8% in WT, 90.6% and 82.1% in RCC, 81.1% and 63.6% in CCSK, 60.3% and 56.2% in MRTK, and 100% and 91.7% in CMN, respectively (p < 0.001). Conclusion The pediatric renal tumor types in Korea are similar to those previously reported in other countries. WT accounted for a large proportion and survival was excellent. Non-Wilms renal tumors included a variety of tumors and showed inferior outcome, especially MRTK. Further efforts are necessary to optimize the treatment and analyze the genetic characteristics of pediatric renal tumors in Korea.

Coronavirus disease 2019 (COVID-19) vaccination in immunocompromised, especially transplant recipients, may induce a weaker immune response. But there are limited data on the immune response after COVID-19 vaccination in liver transplant (LT) recipients, especially on the comparison of Ab responses after different vaccine platforms between mRNA and adenoviral vector vaccines. Thus, we conducted a prospective study on LT recipients who received two doses of the ChAdOx1 nCoV-19 (ChAdOx1), mRNA-1273, or BNT162b2 vaccines compared with healthy healthcare workers (HCWs). SARS-CoV-2 S1-specific IgG Ab titers were measured using ELISA.Overall, 89 LT recipients (ChAdOx1, n=16 [18%]) or mRNA vaccines (mRNA-1273 vaccine, n=23 [26%]; BNT162b2 vaccine, n=50 [56%]) received 3 different vaccines. Of them, 16 (18%) had a positive Ab response after one dose of COVID-19 vaccine and 62 (73%) after 2 doses. However, the median Ab titer after two doses of mRNA vaccines was significantly higher (44.6 IU/ml) than after two doses of ChAdOx1 (19.2 IU/ml, p=0.04). The longer time interval from transplantation was significantly associated with high Ab titers after two doses of vaccine (p=0.003). However, mycophenolic acid use was not associated with Ab titers (p=0.53). In conclusion, about 3-quarters of LT recipients had a positive Ab response after 2 doses of vaccine, and the mRNA vaccines induced higher Ab responses than the ChAdOx1 vaccine.

Background/Aims: Veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is one of the most fatal complications of hematopoietic cell transplantation (HCT), and defibrotide is the only curative drug. We conducted this study to confirm the survival rate of VOD/SOS patients diagnosed in Korea and assess the efficacy of defibrotide. Methods: Patients diagnosed with VOD/SOS after allogenic HCT between 2003 and 2020 were enrolled. We investigated day +100 survival rates and associated risk factors in patients who satisfied the modified Seattle criteria within 50 days of HCT. Results: A total of 110 patients satisfied the modified Seattle criteria, of which 65.5% satisfied the Baltimore criteria. Thirty-seven patients were treated with defibrotide. The day +100 survival rate of the 110 patients was 65.3%. The survival rates in patients who did not meet the Baltimore criteria and in those who did were 86.8% and 53.7%, respectively (p = 0.001). The day +100 survival rate of patients treated with defibrotide was 50.5%. Among the patients receiving defibrotide, those whose creatinine levels were more than 1.2 times the baseline had a significantly lower survival rate at 26.7% (p = 0.014). On multivariate regression analysis, the hazard ratio of satisfaction of the Baltimore criteria was 4.54 (95% confidence interval [CI], 1.69 to 12.21; p = 0.003). In patients treated with defibrotide, the hazard ratio was 8.70 (95% CI, 2.26 to 33.45; p = 0.002), when creatinine was more than 1.2 times the baseline on administration. Conclusions The day +100 survival rate was significantly lower when the Baltimore criteria were satisfied, and when there was an increase in creatinine at the time of defibrotide administration.

Purpose: Acute promyelocytic leukemia (APL) is a rare disease in children and there are some different characteristics between children and adult. We aimed to evaluate incidence, clinical characteristics and treatment outcomes of pediatric APL in Korea. Materials and Methods: Seventy-nine pediatric APL patients diagnosed from January 2009 to December 2016 in 16 tertiary medical centers in Korea were reviewed retrospectively. Results: Of 801 acute myeloid leukemia children, 79 (9.9%) were diagnosed with APL. The median age at diagnosis was 10.6 years (range, 1.3 to 18.0). Male and female ratio was 1:0.93. Thirty patients (38.0%) had white blood cell (WBC) count greater than 10×109/L at diagnosis. All patients received induction therapy consisting of all-trans retinoic acid and chemotherapy. Five patients (6.6%) died during induction chemotherapy and 66 patients (86.8%) achieved complete remission (CR) after induction chemotherapy. The causes of death were three intracranial hemorrhage, one cerebral infarction, and one sepsis. Five patients (7.1%) suffered a relapse during or after maintenance chemotherapy. The estimated 4-year event-free survival and overall survival (OS) rates were 82.1%±4.4%, 89.7%±5.1%, respectively. The 4-year OS was significantly higher in patients with initial WBC < 10×109/L than in those with initial WBC ≥ 10×109/L (p=0.020). Conclusion This study showed that the CR rates and survival outcomes in Korean pediatric APL patients were relatively good. The initial WBC count was the most important prognostic factor and most causes of death were related to serious bleeding in the early stage of treatment.