There are no scoring methods for optimal treatment of patients with aneurysmal subarachnoid hemorrhage (aSAH). We developed a scoring model to predict clinical outcomes according to aSAH risk factors using data from the Japan Stroke Data Bank (JSDB). Of 5344 patients initially registered in the JSDB, 3547 met the inclusion criteria. Patients had been diagnosed with aSAH and treated with surgical clipping or endovascular coiling between 1998 and 2013. We performed multivariate logistic regression for poor outcomes at discharge, indicated by a modified Rankin Scale (mRS) score >2, and in-hospital mortality for both treatment methods. Based on each risk factor, we developed a scoring model assessing its validity using another dataset of our institution. In the surgical clipping group, scoring criteria for aSAH were age >72 years, history of more than once stroke, World Federation of Neurological Societies (WFNS) grades II-V, aneurysmal size >15 mm, and vertebrobasilar artery (VBA) aneurysm location. In the endovascular coiling group, scoring criteria were age >80 years, history of stroke, WFNS grades III-V, computed tomography (CT) Fisher group 4, and aneurysmal location in the middle cerebral artery (MCA) and anterior cerebral artery (ACA). The rates of poor outcome of mRS score >2 in an isolated dataset using these scoring criteria were significantly correlated with our model's scores, so this scoring model was validated. This scoring model can help in the more objective treatment selection in patients with aSAH.
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http://dx.doi.org/10.2176/nmc.oa.2020-0262 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
J Cancer Educ
January 2025
Université de Reims Champagne-Ardenne, CRESTIC, Reims, France.
Cancer remains a leading cause of mortality worldwide, requiring physicians to understand multidisciplinary treatments. This study assessed the impact of a clinical rotation in a cancer center on medical students' knowledge of cancer treatments from a multidisciplinary perspective. A traditional single-department rotation was compared to a multidisciplinary rotation to determine whether broader exposure enhances knowledge and prepares students for multidisciplinary care.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA.
Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.
This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Software Convergence, Seoul Women's University, Hwarango 621, Nowongu, Seoul, 01797, Republic of Korea.
In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix.
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