Anticoagulation is crucial to reducing the risk of cardioembolic strokes, particularly in vulnerable populations such as patients with atrial fibrillation, artificial heart valves, or left ventricular thrombus. Though successful, anticoagulation failure (the occurrence of an ischemic stroke or systemic embolism while receiving therapy) remains a major stroke-care issue. The reason for anticoagulation failure can be below the required threshold, inability to follow up, drug-drug interactions, preexisting hypercoagulable states, or anticoagulant resistance. This failure undermines stroke prevention and requires tailored management, often requiring more drastic or alternative interventions. This review examines what drives anticoagulation failure and explores predictors of this failure in clinical, imaging, and laboratory data. It also discusses current management techniques for improving control and points to new treatments and possible futures, such as high-resolution imaging and personalized medicine based on biomarkers, to help tackle this critical clinical problem.

Anticoagulation is crucial to reducing the risk of cardioembolic strokes, particularly in vulnerable populations such as patients with atrial fibrillation, artificial heart valves, or left ventricular thrombus. Though successful, anticoagulation failure (the occurrence of an ischemic stroke or systemic embolism while receiving therapy) remains a major stroke-care issue. The reason for anticoagulation failure can be below the required threshold, inability to follow up, drug-drug interactions, preexisting hypercoagulable states, or anticoagulant resistance. This failure undermines stroke prevention and requires tailored management, often requiring more drastic or alternative interventions. This review examines what drives anticoagulation failure and explores predictors of this failure in clinical, imaging, and laboratory data. It also discusses current management techniques for improving control and points to new treatments and possible futures, such as high-resolution imaging and personalized medicine based on biomarkers, to help tackle this critical clinical problem.

Anticoagulation is crucial to reducing the risk of cardioembolic strokes, particularly in vulnerable populations such as patients with atrial fibrillation, artificial heart valves, or left ventricular thrombus. Though successful, anticoagulation failure (the occurrence of an ischemic stroke or systemic embolism while receiving therapy) remains a major stroke-care issue. The reason for anticoagulation failure can be below the required threshold, inability to follow up, drug-drug interactions, preexisting hypercoagulable states, or anticoagulant resistance. This failure undermines stroke prevention and requires tailored management, often requiring more drastic or alternative interventions. This review examines what drives anticoagulation failure and explores predictors of this failure in clinical, imaging, and laboratory data. It also discusses current management techniques for improving control and points to new treatments and possible futures, such as high-resolution imaging and personalized medicine based on biomarkers, to help tackle this critical clinical problem.

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.

A patient showed signs of fever and Hemovac insertion site discharge 8 days after surgery and was admitted. Abdominal paracentesis found milky ascites with triglyceride levels of the peritoneal fluid as high as 1,603 g/mL. Diagnosed as chylous ascites, symptomatic therapy such as empirical antibodies and diuretics was administered with paracentesis before being discharged. The ascites volume increased again, and the patient was re-admitted. The patient was treated with orlistat, octreotide, total parenteral nutrition administration, ascites drainage, and diuretics. Ascites levels increased further and intraperitoneal Viscum was administered. Clear ascites was observed, and the patient was discharged. We reported a case where conventional treatment for chylous ascites that occurred after pancreaticoduodenectomy was shown to be ineffective while Viscum extracted from mistletoe was able to manage chylous ascites.

Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression.Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated.Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods.

This study evaluated the test-retest repeatability and measurement variability of ultrasonographic shear wave elastography (SWE) for liver stiffness in a rat liver fibrosis model. Methods: In 31 Sprague-Dawley rats divided into three groups (high-dose, low-dose, and control), liver fibrosis was induced by intraperitoneal administration of thioacetamide for 8 weeks. A dedicated radiographer performed SWE to measure liver stiffness in kilopascals in two sessions at a 3-day interval. We calculated correlations between liver stiffness and histopathologic results, measurement variability in each session using coefficients of variation (CoVs) and interquartile/median (IQR/M), and test-retest repeatability between both sessions using the repeatability coefficient. Results: Different levels of liver fibrosis in each group were successfully induced in the animal model. The mean liver stiffness values were 8.88±1.48 kPa in the control group, 11.62±1.70 kPa in the low-dose group, and 11.91±1.73 kPa in the high-dose group. The correlation between collagen areas and liver stiffness values was moderate (r=0.6). In all groups, the second session yielded lower CoVs (i.e., more reliable results) for liver stiffness than the first session, suggesting a training effect for the operator. The mean IQR/M values were also lower in the second session than in the first session, which had four outliers (0.21 vs. 0.12, P<0.001). The test-retest repeatability coefficient was 3.75 kPa and decreased to 2.82 kPa after removing the four outliers. Conclusion: The use of ultrasonographic SWE was confirmed to be feasible and repeatable for evaluating liver fibrosis in preclinical trials. Operator training might reduce variability in liver stiffness measurements.

OBJECTIVE: To evaluate the design characteristics of studies that evaluated the performance of artificial intelligence (AI) algorithms for the diagnostic analysis of medical images. MATERIALS AND METHODS: PubMed MEDLINE and Embase databases were searched to identify original research articles published between January 1, 2018 and August 17, 2018 that investigated the performance of AI algorithms that analyze medical images to provide diagnostic decisions. Eligible articles were evaluated to determine 1) whether the study used external validation rather than internal validation, and in case of external validation, whether the data for validation were collected, 2) with diagnostic cohort design instead of diagnostic case-control design, 3) from multiple institutions, and 4) in a prospective manner. These are fundamental methodologic features recommended for clinical validation of AI performance in real-world practice. The studies that fulfilled the above criteria were identified. We classified the publishing journals into medical vs. non-medical journal groups. Then, the results were compared between medical and non-medical journals. RESULTS: Of 516 eligible published studies, only 6% (31 studies) performed external validation. None of the 31 studies adopted all three design features: diagnostic cohort design, the inclusion of multiple institutions, and prospective data collection for external validation. No significant difference was found between medical and non-medical journals. CONCLUSION: Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms.
Artificial Intelligence ; Case-Control Studies ; Cohort Studies ; Data Collection ; Feasibility Studies ; Machine Learning ; Prospective Studies

BACKGROUND AND PURPOSE: Cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) could be misleading in idiopathic normal-pressure hydrocephalus (iNPH). We therefore investigated the CSF biomarkers in 18F-florbetaben amyloid-negative positron-emission tomography (PET) [amyloid PET(−)] iNPH, amyloid-positive PET [amyloid PET(+)] AD, and cognitively normal (CN) subjects. METHODS: Ten amyloid PET(+) AD patients (56.7±5.6 years old, mean±standard deviation), 10 amyloid PET(−) iNPH patients (72.8±4.5 years old), and 8 CN subjects (61.2±6.5 years old) were included. We measured the levels of β-amyloid (Aβ)40, Aβ42, total tau (t-tau) protein, and phosphorylated tau (p-tau) protein in the CSF using enzyme-linked immunosorbent assays. RESULTS: The level of Aβ42 and the Aβ42/Aβ40 ratio in the CSF were significantly lower in AD than in iNPH or CN subjects. The Aβ40 level did not differ significantly between AD and iNPH (p=1.000), but it did between AD and CN subjects (p=0.032). The levels of both t-tau and p-tau were higher in AD than in iNPH or CN subjects. The levels of Aβ42, Aβ40, t-tau, and p-tau were lower in iNPH than in CN subjects, but there was no significant difference after controlling for age. CONCLUSIONS: Our results suggest that the mechanism underlying low CSF Aβ levels differs between amyloid PET(−) iNPH and amyloid PET(+) AD subjects. The lower levels of all CSF biomarkers in iNPH patients might be due to reduced clearances from extracellular fluid and decreased brain metabolism of the periventricular zone in iNPH resulting from glymphatic dysfunction.
Alzheimer Disease ; Amyloid ; Biomarkers ; Brain ; Cerebrospinal Fluid ; Enzyme-Linked Immunosorbent Assay ; Extracellular Fluid ; Humans ; Hydrocephalus ; Metabolism ; Positron-Emission Tomography

OBJECTIVE: To evaluate the feasibility of robot-assisted laparoscopic myomectomy in multiple myomas over 10. METHODS: A retrospective study was conducted for 662 patients who underwent robot-assisted laparoscopic myomectomy and open myomectomy by a single operator in a tertiary university hospital. RESULTS: A total of 30 women underwent removal of 10 or more uterine myomas by robotics and 13 patients were selected for this study. The average number of myomas removed was 13.7 (range 10–20). The maximum diameter of the myomas was 6.8 cm (range 5.0–10.0 cm). The sum of the diameters of each myoma was 34.7 cm (range 20.0–54.5 cm) and the mass of resected myomas for each case was 229.1 g (range 106.8–437.9 g). In no case was the robotic procedure converted into conventional laparoscopy or laparotomy, and all patients recovered without any major complications. In comparison with 13 cases of open myomectomy during the same period, robotic surgery took longer time than open surgery (360.5 vs. 183.8 minutes; P=0.001) but had shorter postoperative hospital days after surgery (mean 2.5 vs. 3.5 days; P=0.003). CONCLUSION: Robot-assisted laparoscopic myomectomy could be an alternative to laparotomic myomectomy for numerous myomas over 10 in number.
Female ; Humans ; Laparoscopy ; Laparotomy ; Leiomyoma ; Minimally Invasive Surgical Procedures ; Myoma ; Retrospective Studies ; Robotic Surgical Procedures ; Robotics ; Uterine Myomectomy