Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: This study aimed to compare and identify factors affecting delirium nursing stress among nurses in comprehensive nursing care service wards and general wards. Methods: Using structured questionnaires, data were collected from 230 nurses working in two tertiary university hospitals. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson's correlation coefficient, and multiple regression analysis using SPSS/WIN 27.0. Results: The average delirium nursing stress score for nurses in comprehensive nursing care service wards was 2.98±0.30 out of 4 points, which was significantly higher than 2.89±0.29 points for nurses in general wards (t=2.17, p=.031).Factors influencing delirium nursing stress among comprehensive nursing care service ward nurses included nursing work environment (β=-.58, p<.001) and conflicts with medical staff and other departments related to delirium patients (β=.24, p=.006), explaining power of 44% (F=12.13, p<.001). For general ward nurses, the nursing work environment(β=-.39, p<.001) was the main influencing factor, explaining power of 17% (F=3.93, p<.001). Conclusion Both types of nurses require improvements in their work environment. Strategies to reduce conflict between medical staff and other departments are essential in comprehensive nursing care service wards.

Purpose: This study aimed to compare and identify factors affecting delirium nursing stress among nurses in comprehensive nursing care service wards and general wards. Methods: Using structured questionnaires, data were collected from 230 nurses working in two tertiary university hospitals. Data were analyzed using descriptive statistics, t-tests, ANOVA, Pearson's correlation coefficient, and multiple regression analysis using SPSS/WIN 27.0. Results: The average delirium nursing stress score for nurses in comprehensive nursing care service wards was 2.98±0.30 out of 4 points, which was significantly higher than 2.89±0.29 points for nurses in general wards (t=2.17, p=.031).Factors influencing delirium nursing stress among comprehensive nursing care service ward nurses included nursing work environment (β=-.58, p<.001) and conflicts with medical staff and other departments related to delirium patients (β=.24, p=.006), explaining power of 44% (F=12.13, p<.001). For general ward nurses, the nursing work environment(β=-.39, p<.001) was the main influencing factor, explaining power of 17% (F=3.93, p<.001). Conclusion Both types of nurses require improvements in their work environment. Strategies to reduce conflict between medical staff and other departments are essential in comprehensive nursing care service wards.

Purpose: To assess the prevalence of dry eye disease (DED) and ocular surface conditions in patients before cataract surgery at a tertiary hospital in South Korea. Methods: This retrospective study included 96 eyes of 96 patients scheduled for cataract surgery from March to June 2023. The primary metrics of DED, including the Symptom Assessment in Dry Eye (SANDE) score, tear film breakup time (TBUT), tear secretion (Schirmer test), and ocular staining score (OSS, Oxford grading), were investigated. The proportion of patients with DED was determined according to the Asia Dry Eye Society (ADES) and revised Korean DED guidelines. Results: The patients’ mean age was 65.9 ± 8.3 years, with a mean SANDE score of 55.6 ± 30.5. The mean TBUT, OSS, and tear secretion were 3.5 ± 1.2 seconds, 1.3 ± 0.8 points, and 7.0 ± 5.4 mm, respectively. Among the patients, 85.4% exhibited a SANDE score of 20 or higher, 99% had a TBUT of < 7 seconds, and 88.4% exhibited one or more points on the OSS. According to the ADES and revised South Korean guidelines, 79.2% and 86.5% of patients were diagnosed with DED, respectively. Furthermore, all 96 patients exhibited at least one abnormal dry eye sign, regardless of symptoms. Conclusions The prevalence of DED in patients before cataract surgery was > 80%, with all patients exhibiting at least one ocular surface abnormality. Therefore, identifying and managing ocular surface abnormalities before cataract surgery is imperative.

Background: The Korea Expert Committee on Immunization Practices (KECIP) is a key advisory body the government to develop guidelines and provide technical advisory activities on immunization policies in Korea. A recent policy study, inspired by global best practices, aims to enhance KECIP's functionality for providing timely and transparent recommendations in the face of evolving vaccine science and emerging infectious diseases like COVID-19. Methods: This study reviewed the current status of KECIP and collected expert opinions through surveys and consultations. Among the 40 panel members who were surveyed, 19 responded to a questionnaire specifically designed to assess the potential areas of improvement within KECIP. Results: The majority of respondents favored maintaining the current member count and emphasized the need for a subcommittee. Opinions varied on issues such as the length of KECIP’s term, the representation of vaccine manufacturers’ perspectives, and the chairperson’s role. However, there was a consensus on the importance of expertise, transparency, and fair proceedings within the committee. Conclusion This study underscores the pivotal role of KECIP in shaping national immunization policies, emphasizing the necessity for informed guidance amidst evolving vaccine science and emerging infectious diseases. Furthermore, it stressed the importance of enhancing KECIP’s capacity to effectively address evolving public health challenges and maintain successful immunization programs in South Korea.