Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Purpose: To investigate the outcomes of brolucizumab reinjection after intraocular inflammation (IOI) development. Methods: This retrospective study analyzed patients with brolucizumab injections from April 2021 to January 2024. Patients who developed IOI after brolucizumab were included and categorized into subgroups depending on reinjection, discontinuation, and further IOI development. Results: A total of 472 eyes of 432 patients received brolucizumab injections. Thirty-eight cases developed IOI at least once, and 25 continued brolucizumab. Sixteen cases had no more IOI events, and nine experienced a second or more IOI events. Among the nine cases, three maintained brolucizumab injections despite IOI recurrence. The incidence of IOI was 8.1% based on the number of eyes (38 of 472 eyes) and 2.0% based on the number of brolucizumab injections (50 of 2,468 injections). The incidence of occlusive retinal vasculitis was 0.2% (1 of 472 eyes). The recurrence rate was 23.7% (9 of 38 eyes). The average number of injections between the first brolucizumab injection and the injection date on which IOI first developed was 2.15 times in the no-reinjection group, 3.44 times in the no-IOI-recurrence group, and 2.0 times in the second-IOI-episode group. Time to IOI occurrence in cases with first IOI episode was 18.60 ± 16.73 days, with 15 cases developing IOI within 1 week. Conclusions This study elucidates the real-world incidence of brolucizumab associated IOIs, with a description of information related to reinjections after the IOI episodes. A comprehensive understanding of brolucizumab reinjection is essential for its optimal utilization.

Background: and Purpose Treatments for neuromyelitis optica spectrum disorder (NMOSD) such as eculizumab, ravulizumab, satralizumab, and inebilizumab have significantly advanced relapse prevention, but they remain expensive. Rituximab is an off-label yet popular alternative that offers a cost-effective solution, but its real-world efficacy needs better quantification for guiding the application of newer approved NMOSD treatments (ANTs). This study aimed to determine real-world rituximab failure rates to anticipate the demand for ANTs and aid in resource allocation. Methods: We conducted a nationwide retrospective study involving 605 aquaporin-4-antibody-positive NMOSD patients from 22 centers in South Korea that assessed the efficacy and safety of rituximab over a median follow-up of 47 months. Results: The 605 patients treated with rituximab included 525 (87%) who received continuous therapy throughout the follow-up period (median=47 months, interquartile range=15–87 months). During this period, 117 patients (19%) experienced at least 1 relapse. Notably, 68 of these patients (11% of the total cohort) experienced multiple relapses or at least 1 severe relapse.Additionally, 2% of the patients discontinued rituximab due to adverse events, which included severe infusion reactions, neutropenia, and infections. Conclusions This study has confirmed the efficacy of rituximab in treating NMOSD, as evidenced by an 87% continuation rate among patients over a 4-year follow-up period. Nevertheless, the occurrence of at least one relapse in 19% of the cohort, including 11% who experienced multiple or severe relapses, and a 2% discontinuation rate due to adverse events highlight the urgent need for alternative therapeutic options.

Purpose: This study evaluated the postoperative quality of life (QoL) after various types of gastrectomy for gastric cancer. Materials and Methods: A multicenter prospective observational study was conducted in Korea using the Korean Quality of Life in Stomach Cancer Patients Study (KOQUSS)-40, a new QoL assessment tool focusing on postgastrectomy syndrome. Overall, 496 patients with gastric cancer were enrolled, and QoL was assessed at 5 time points: preoperatively and at 1, 3, 6, and 12 months after surgery. Results: Distal gastrectomy (DG) and pylorus-preserving gastrectomy (PPG) showed significantly better outcomes than total gastrectomy (TG) and proximal gastrectomy (PG) with regard to total score, indigestion, and dysphagia. DG, PPG, and TG also showed significantly better outcomes than PG in terms of dumping syndrome and worry about cancer. Postoperative QoL did not differ significantly according to anastomosis type in DG, except for Billroth I anastomosis, which achieved better bowel habit change scores than the others. No domains differed significantly when comparing double tract reconstruction and esophagogastrostomy after PG. The total QoL score correlated significantly with postoperative body weight loss (more than 10%) and extent of resection (P<0.05 for both).Reflux as assessed by KOQUSS-40 did not correlate significantly with reflux observed on gastroscopy 1 year postoperatively (P=0.064). Conclusions Our prospective observation using KOQUSS-40 revealed that DG and PPG lead to better QoL than TG and PG. Further study is needed to compare postoperative QoL according to anastomosis type in DG and PG.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.