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.

Background: Ultrasonographic soft markers are normal variants, rather than fetal abnormalities, and guidelines recommend a detailed survey of fetal anatomy to determine the necessity of antenatal karyotyping. Anecdotal reports have described cases with ultrasonographic soft markers in which chromosomal microarray analysis (CMA) revealed pathogenic copy number variants (CNVs) despite normal results on conventional karyotyping, but CMA for ultrasonographic soft markers remains a matter of debate. In this systematic review, we evaluated the clinical significance of CMA for pregnancies with isolated ultrasonographic soft markers and a normal fetal karyotype. Methods: An electronic search was conducted by an experienced librarian through the MEDLINE, Embase, and Cochrane CENTRAL databases. We reviewed 3,338 articles (3,325 identified by database searching and 13 by a hand search) about isolated ultrasonographic soft markers, and seven ultrasonographic markers (choroid plexus cysts, echogenic bowel, echogenic intracardiac focus, hypoplastic nasal bone, short femur [SF], single umbilical artery, and urinary tract dilatation) were included for this study. Results: Seven eligible articles were included in the final review. Pathogenic or likely pathogenic CNVs were found in fetuses with isolated ultrasonographic soft markers and a normal karyotype. The overall prevalence of pathogenic or likely pathogenic CNVs was 2.0% (41 of 2,048). The diagnostic yield of CMA was highest in fetuses with isolated SF (9 of 225, 3.9%). Conclusion CMA could aid in risk assessment and pregnancy counseling in pregnancies where the fetus has isolated ultrasonographic soft markers along with a normal karyotype.

Background: Preeclampsia (PE) is a hypertensive pregnancy disorder linked to placental dysfunction, often involving pathological lesions like acute atherosis, decidual vasculopathy, accelerated villous maturation, and fibrinoid deposition. However, there is no gold standard for the pathological diagnosis of PE and this limits the ability of clinicians to distinguish between PE and non-PE pregnancies. Recent advances in computational pathology have provided the opportunity to automate pathological analysis for diagnosis, classification, prediction, and prediction of disease progression. In this study, we assessed whether computational pathology could be used to identify PE placentas. Methods: A total of 168 placental whole-slide images (WSIs) of patients from Seoul National University Hospital (comprising 84 PE cases and 84 normal controls) were used for model development and internal validation. For external validation of the model, 76 placental slides (including 38 PE cases and 38 normal controls) were obtained from the Boramae Medical Center (BMC). To establish standard criteria for diagnosing PE and distinguishing it from controls using placental WSIs, patch characteristics and quantification of terminal and intermediate villi were employed. In unsupervised learning, K-means clustering was conducted as a feature obtained through an Auto Encoder to extract the ratio of each cluster for each WSI. For supervised learning, quantitative assessments of the villi were obtained using a U-Net-based segmentation algorithm. The prediction model was developed using an ensemble method and was compared with a clinical feature model developed by using placental size features. Results: Using ensemble modeling, we developed a model to identify PE placentas.The model showed good performance (area under the precision-recall curve [AUPRC], 0.771; 95% confidence interval [CI], 0.752–0.790), with 77.3% of sensitivity and 71.1% of specificity, whereas the clinical feature model showed an AUPRC 0.713 (95% CI, 0.694–0.732) with 55.6% sensitivity and 86.8% specificity. External validation of the predictive model employing the BMC-derived set of placental slides also showed good discrimination (AUPRC, 0.725; 95% CI, 0.720–0.730). Conclusion The proposed computational pathology model demonstrated a strong ability to identify preeclamptic placentas. Computational pathology has the potential to improve the identification of PE placentas.

This report presents the latest statistics on the stroke population in South Korea, sourced from the Clinical Research Collaborations for Stroke in Korea-National Institute for Health (CRCS-K-NIH), a comprehensive, nationwide, multicenter stroke registry. The Korean cohort, unlike western populations, shows a male-to-female ratio of 1.5, attributed to lower risk factors in Korean women. The average ages for men and women are 67 and 73 years, respectively.Hypertension is the most common risk factor (67%), consistent with global trends, but there is a higher prevalence of diabetes (35%) and smoking (21%). The prevalence of atrial fibrillation (19%) is lower than in western populations, suggesting effective prevention strategies in the general population. A high incidence of large artery atherosclerosis (38%) is observed, likely due to prevalent intracranial arterial disease in East Asians and advanced imaging techniques.There has been a decrease in intravenous thrombolysis rates, from 12% in 2017–2019 to 10% in 2021, with no improvements in door-to-needle and door-to-puncture times, worsened by the coronavirus disease 2019 pandemic. While the use of aspirin plus clopidogrel for noncardioembolic stroke and direct oral anticoagulants for atrial fibrillation is well-established, the application of direct oral anticoagulants for non-atrial fibrillation cardioembolic strokes in the acute phase requires further research. The incidence of early neurological deterioration (13%) and the cumulative incidence of recurrent stroke at 3 months (3%) align with global figures. Favorable outcomes at 3 months (63%) are comparable internationally, yet the lack of improvement in dependency at 3 months highlights the need for advancements in acute stroke care.

Purpose: This multi-center, retrospective study was conducted to evaluate the long-term survival in patients who underwent surgical resection for small cell lung cancer (SCLC) and to identify the benefit of adjuvant therapy following surgery. Materials and Methods: The data of 213 patients who underwent surgical resection for SCLC at four institutions were retrospectively reviewed. Patients who received neoadjuvant therapy or an incomplete resection were excluded. Results: The mean patient age was 65.29±8.93 years, and 184 patients (86.4%) were male. Lobectomies and pneumonectomies were performed in 173 patients (81.2%), and 198 (93%) underwent systematic mediastinal lymph node dissections. Overall, 170 patients (79.8%) underwent adjuvant chemotherapy, 42 (19.7%) underwent radiotherapy to the mediastinum, and 23 (10.8%) underwent prophylactic cranial irradiation. The median follow-up period was 31.08 months (interquartile range, 13.79 to 64.52 months). The 5-year overall survival (OS) and disease-free survival were 53.4% and 46.9%, respectively. The 5-year OS significantly improved after adjuvant chemotherapy in all patients (57.4% vs. 40.3%, p=0.007), and the survival benefit of adjuvant chemotherapy was significant in patients with negative node pathology (70.8% vs. 39.7%, p=0.004). Adjuvant radiotherapy did not affect the 5-year OS (54.6% vs. 48.5%, p=0.458). Age (hazard ratio [HR], 1.032; p=0.017), node metastasis (HR, 2.190; p < 0.001), and adjuvant chemotherapy (HR, 0.558; p=0.019) were associated with OS. Conclusion Adjuvant chemotherapy after surgical resection in patients with SCLC improved the OS, though adjuvant radiotherapy to the mediastinum did not improve the survival or decrease the locoregional recurrence rate.

Purpose: Outcome analysis of urachal cancer (UraC) is limited due to the scarcity of cases and different staging methods compared to urothelial bladder cancer (UroBC). We attempted to assess survival outcomes of UraC and compare to UroBC after stage-matched analyses. Materials and Methods: Total 203 UraC patients from a multicenter database and 373 UroBC patients in single institution from 2000 to 2018 were enrolled (median follow-up, 32 months). Sheldon stage conversion to corresponding TNM staging for UraC was conducted for head-to-head comparison to UroBC. Perioperative clinical variables and pathological results were recorded. Stage-matched analyses for survival by stage were conducted. Results: UraC patients were younger (mean age, 54 vs. 67 years; p < 0.001), with 163 patients (80.3%) receiving partial cystectomy and 23 patients (11.3%) radical cystectomy. UraC was more likely to harbor ≥ pT3a tumors (78.8% vs. 41.8%). While 5-year recurrence-free survival, cancer-specific survival (CSS) and overall survival were comparable between two groups (63.4%, 67%, and 62.1% in UraC and 61.5%, 75.9%, and 67.8% in UroBC, respectively), generally favorable prognosis for UraC in lower stages (pT1-2) but unfavorable outcomes in higher stages (pT4) compared to UroBC was observed, although only 5-year CSS in ≥ pT4 showed statistical significance (p=0.028). Body mass index (hazard ratio [HR], 0.929), diabetes mellitus (HR, 1.921), pathologic T category (HR, 3.846), and lymphovascular invasion (HR, 1.993) were predictors of CSS for all patients. Conclusion Despite differing histology, UraC has comparable prognosis to UroBC with relatively favorable outcome in low stages but worse prognosis in higher stages. The presented system may be useful for future grading and risk stratification of UraC.

Background: The purpose of this study was to evaluate the effect of vanishing twin (VT) on maternal serum marker concentrations and nuchal translucency (NT). Methods: This is a secondary analysis of a multicenter prospective cohort study in 12 institutions. Serum concentrations of pregnancy-associated plasma protein-A in the first trimester and alpha-fetoprotein (AFP), total human chorionic gonadotrophin, unconjugated estriol, and inhibin A in the second trimester were measured, and NT was measured between 10 and 14 weeks of gestation. Results: Among 6,793 pregnant women, 5,381 women were measured for serum markers in the first or second trimester, including 65 cases in the VT group and 5,316 cases in the normal singleton group. The cases in the VT group had a higher median multiple of the median value of AFP and inhibin A than the normal singleton group. The values of other serum markers and NT were not different between the two groups. After the permutation test with adjustment,AFP and inhibin A remained significant differences. The frequency of abnormally increased AFP was also higher in the VT group than in the normal singleton group. Conclusion VT can be considered as an adjustment factor for risk assessment in the secondtrimester serum screening test.

Patient blood management is an evidence-based concept that seeks to minimize blood loss by maintaining adequate hemoglobin levels and optimizing hemostasis during surgery. Since the coronavirus disease 2019 pandemic, patient blood management has gained significance due to fewer blood donations and reduced amounts of blood stored for transfusion. Recently, the prevalence of postpartum hemorrhage (PPH), as well as the frequency of PPH-associated transfusions, has steadily increased. Therefore, proper blood transfusion is required to minimize PPH-associated complications while saving the patient’s life. Several guidelines have attempted to apply this concept to minimize anemia during pregnancy and bleeding during delivery, prevent bleeding after delivery, and optimize recovery methods from anemia. This study systematically reviewed various guidelines to determine blood loss management in pregnant women.