PURPOSE: The purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging. METHODS: From June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve. RESULTS: Of the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect. CONCLUSION: S-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.
Breast* ; Decision Making ; Female ; Humans ; Information Systems ; Retrospective Studies ; Sensitivity and Specificity ; Ultrasonography* ; Ultrasonography, Mammary

ABO-blood typing typically involves both front typing (cell typing) and back typing (serum typing) to ensure accurate determination of blood type. However, in neonates, back typing is frequently omitted due to their immature immune system. If an institution chooses red blood cells (RBCs) that match the neonate's blood type, omitting back typing could lead to unexplained cross-matching incompatibilities. Therefore, blood banks should be cautious with neonatal blood typing and transfusion practices to ensure transfusion safety.

ABO-blood typing typically involves both front typing (cell typing) and back typing (serum typing) to ensure accurate determination of blood type. However, in neonates, back typing is frequently omitted due to their immature immune system. If an institution chooses red blood cells (RBCs) that match the neonate's blood type, omitting back typing could lead to unexplained cross-matching incompatibilities. Therefore, blood banks should be cautious with neonatal blood typing and transfusion practices to ensure transfusion safety.

ABO-blood typing typically involves both front typing (cell typing) and back typing (serum typing) to ensure accurate determination of blood type. However, in neonates, back typing is frequently omitted due to their immature immune system. If an institution chooses red blood cells (RBCs) that match the neonate's blood type, omitting back typing could lead to unexplained cross-matching incompatibilities. Therefore, blood banks should be cautious with neonatal blood typing and transfusion practices to ensure transfusion safety.

ABO-blood typing typically involves both front typing (cell typing) and back typing (serum typing) to ensure accurate determination of blood type. However, in neonates, back typing is frequently omitted due to their immature immune system. If an institution chooses red blood cells (RBCs) that match the neonate's blood type, omitting back typing could lead to unexplained cross-matching incompatibilities. Therefore, blood banks should be cautious with neonatal blood typing and transfusion practices to ensure transfusion safety.

Rhinovirus infection is typically associated with the common cold and has rarely been reported as a cause of severe pneumonia in immunocompetent adults. A 55-year-old previous healthy woman, who consumed half a bottle of alcohol daily, presented with respiratory failure after one week of upper respiratory infection symptoms. Radiography revealed bilateral, diffuse ground glass opacity with patchy consolidation in the whole lung field; bronchoalveolar lavage fluid analysis indicated that rhinovirus was the causative organism. After five days of conservative support, the symptoms and radiographic findings began to improve. We report this rare case of rhinovirus pneumonia in an otherwise healthy host along with a review of references.
Adult* ; Alcohol Drinking ; Bronchoalveolar Lavage Fluid ; Common Cold ; Female ; Glass ; Humans ; Lung ; Middle Aged ; Pneumonia ; Radiography ; Respiratory Insufficiency* ; Rhinovirus*

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and Methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.

Background: Pretransfusion testing is vital for safe transfusion. However, in situations without time to perform sufficient testing, all or part of the pretransfusion testing may be skipped to issue blood quickly. This study evaluated the safety of red blood cell (RBC) transfusion released by an emergency blood transfusion protocol through retrospective analysis at a tertiary hospital for eight years. Methods: All RBC transfusions following the emergency blood transfusion protocol from 2011 to 2018 at Seoul National University Hospital were included in the study. Crossmatching and unexpected antibody screening test results conducted after RBC release and the occurrence of hemolytic transfusion reactions were analyzed. Results: A total of 1,541 cases (5,299 RBCs issued) of emergency blood transfusion were identified. RBCs were issued after performing the immediate spin crossmatch without an unexpected antibody screening test in most cases (1,443; 93.64%), while RBCs were issued with no pretransfusion testing in 98 cases (6.36%). Antibody screening tests performed after the issue of RBCs showed that 17 (1.1%) cases were positive. Two units of RBCs from two different cases showed positive antiglobulin crossmatch test results. However, none of them were suspected to be associated with a hemolytic transfusion reaction. Conclusion The incidence of incompatible RBC release was very low in patients receiving RBC transfusion through the emergency blood transfusion protocol suggesting it can be used safely with minimal risk of hemolytic transfusion reactions caused by incompatible blood transfusions.

Objective: A prolonged length of stay in the emergency department (EDLOS) is known to be associated with poorer outcomes in critically ill patients. However, this has not been proven in patients who visit the emergency department (ED) due to acute heart failure (AHF). We aimed to find out whether prolonged EDLOS is associated with major in-hospital complications in patients with AHF. Methods: This is a retrospective cohort study of AHF patients who were admitted to intensive care units (ICU) via the ED of a single academic hospital from January 2015 to December 2019. We divided the patients into two groups: EDLOS <24 hours and EDLOS≥24 hours. The primary outcome was major in-hospital complications, which included in-hospital death, application of continuous renal replacement therapy, or extracorporeal membrane oxygenation treatment. Secondary outcomes included in-hospital death, prolonged ICU stay (≥10 days), and prolonged hospital stay (≥14 days) excluding ED stay. Results: A total of 265 patients were enrolled. Of these 163 patients stayed in the ED for over 24 hours. The multivariable logistic analysis demonstrated that EDLOS ≥24 hours was independently associated with major in-hospital complications (odds ratio [OR], 3.296; 95% confidence interval [CI], 1.291-8.413; P=0.013). Analysis of the secondary outcomes showed that EDLOS ≥24 hours was associated with in-hospital death (OR, 2.607; 95% CI, 1.005-6.759; P=0.049) and prolonged hospital stay ≥14 days (OR, 2.458; 95% CI, 1.303-4.636; P=0.006). Conclusion Our study showed that in patients with AHF who visited ED and were admitted to the ICU, prolonged EDLOS was associated with major in-hospital complications.