We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

We updated the Korean Society of Gynecologic Oncology (KSGO) practice guideline for the management of ovarian cancer as version 5.1. The ovarian cancer guideline team of the KSGO published announced the fifth version (version 5.0) of its clinical practice guidelines for the management of ovarian cancer in December 2023. In version 5.0, the selection of the key questions and the systematic reviews were based on the data available up to December 2022.Therefore, we updated the guidelines version 5.0 with newly accumulated clinical data and added 5 new key questions reflecting the latest insights in the field of ovarian cancer between 2023 and 2024. For each question, recommendation was provided together with corresponding level of evidence and grade of recommendation, all established through expert consensus.

Background: Flow cytometric immunophenotyping of hematolymphoid neoplasms (FCIHLN) is essential for diagnosis, classification, and minimal residual disease (MRD) monitoring. FCI-HLN is typically performed using in-house protocols, raising the need for standardization. Therefore, we surveyed the current status of FCI-HLN in Korea to obtain fundamental data for quality improvement and standardization. Methods: Eight university hospitals actively conducting FCI-HLN participated in our survey.We analyzed responses to a questionnaire that included inquiries regarding test items, reagent antibodies (RAs), fluorophores, sample amounts (SAs), reagent antibody amounts (RAAs), acquisition cell number (ACN), isotype control (IC) usage, positiveegative criteria, and reporting. Results: Most hospitals used acute HLN, chronic HLN, plasma cell neoplasm (PCN), and MRD panels. The numbers of RAs were heterogeneous, with a maximum of 32, 26, 12, 14, and 10 antibodies used for acute HLN, chronic HLN, PCN, ALL-MRD, and multiple myeloma-MRD, respectively. The number of fluorophores ranged from 4 to 10. RAs, SAs, RAAs, and ACN were diverse. Most hospitals used a positive criterion of 20%, whereas one used 10% for acute and chronic HLN panels. Five hospitals used ICs for the negative criterion. Positiveegative assignments, percentages, and general opinions were commonly reported. In MRD reporting, the limit of detection and lower limit of quantification were included. Conclusions This is the first comprehensive study on the current status of FCI-HLN in Korea, confirming the high heterogeneity and complexity of FCI-HLN practices. Standardization of FCI-HLN is urgently needed. The findings provide a reference for establishing standard FCI-HLN guidelines.

Background: This study compared the costs associated with transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) in Korea by utilizing the National Health Insurance Service database. Methods: Between June 2015 and May 2019, 1,468 patients underwent primary isolated transfemoral TAVI, while 2,835 patients received primary isolated SAVR with a bioprosthesis. We assessed the costs of index hospitalization and subsequent healthcare utilization, categorizing the cohort into 6 age subgroups: <70, 70–74, 75–79, 80–84, 85–89, and ≥90 years. The median follow-up periods were 2.5 and 3.0 years in the TAVI and SAVR groups, respectively. Results: The index hospitalization costs were 41.0 million Korean won (KRW) (interquartile range [IQR], 39.1–44.7) for the TAVI group and 24.6 million KRW (IQR, 21.3–30.2) for the SAVR group (p<0.001). The TAVI group exhibited relatively constant index hospitalization costs across different age subgroups. In contrast, the SAVR group showed increasing index hospitalization costs with advancing age. The healthcare utilization costs were 5.7 million KRW per year (IQR, 3.3–14.2) for the TAVI group and 4.0 million KRW per year (IQR, 2.2–9.0) for the SAVR group (p<0.001). Healthcare utilization costs were higher in the TAVI group than in the SAVR group for the age subgroups of <70, 70–74, and 75–79 years, and were comparable in the age subgroups of 80–84, 85–89, and ≥90 years. Conclusion TAVI had much higher index hospitalization costs than SAVR. Additionally, the overall healthcare utilization costs post-discharge for TAVI were also marginally higher than those for SAVR in younger age subgroups.

Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

Objective: Research on fetal mortality due to congenital anomalies is insufficient, particularly that utilizing data specific to South Korea. Thus, we aimed to investigate the characteristics and risk factors for fetal death due to congenital anomalies in Korea. Methods: Fetal deaths registered from 2009-2020 with Statistics Korea were assessed. Fetal charac teristics included gestational age, body weight, sex, and multiple fetuses, while maternal characteri stics included age, educational level, nationality, and place of residence. Risk factors for fetal death were analyzed using simple comparison and logistic regression. Changes in fetal mortality by year were examined using Poisson regression analysis. Results: A total of 37,928 fetal deaths occurred, among which 3,758 were classified as congenital anomaly, 710 as non-congenital anomaly, and 33,460 as unknown cause. Fetal mortality for gesta tional weeks 20 to 27 and ≥28 were 75.3% and 24.7%, respectively. The proportion of congenital anomalies among fetal deaths during these gestational age periods is 11.3% and 5.8%. Multiple fetuses, maternal age of <20 years or ≥40 years were identified as risk factors for fetal death due to congenital anomalies. Among the top 30 causes, covering 97.5% of all deaths, unspecified causes were 88.2%, congenital malformations 8.2%, and other causes 2.0%, respectively. Fetal mortality and deaths from congenital anomalies exhibited downward trends. Conclusion Fetal deaths due to congenital anomalies showed a decreasing trend, but the risks, such as multiple fetuses and advanced maternal age are increasing in Korea. Therefore, careful monitoring of fetal deaths due to congenital anomalies are essential.