Background And Purpose: Symptoms in glioma patients are distinctly different from symptoms in patients with other types of cancer and have a high impact on quality of life. In this study, a stepwise approach of developing a glioma module for assessment of symptoms, based on a Dutch adapted and validated version of the Edmonton Symptom Assessment System, is described.
Methods: Three phases of instrument development were conducted: a systematic literature review and a focus group interview with experts were performed (phase I) to generate relevant symptoms and construct a preliminary module (phase II). In phase III, the preliminary module was evaluated (n = 25) and pretested (n = 45) in glioma patients representing all phases of the disease.
Results: Our glioma module contains 11 generic and 6 neurologic symptoms. Patients completed the glioma module in a median of 5 minutes, and 56% of the patients required some assistance to complete the instrument.
Conclusion: The glioma module has initial validity and will benefit from prospective validation in a larger cohort of patients with glioma.
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http://dx.doi.org/10.1097/JNN.0000000000000400 | DOI Listing |
Objective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
Comput Methods Programs Biomed
January 2025
School of Information Science and Technology, Fudan University, Shanghai, 200433, China; Key Laboratory of Medical Imaging, Computing and Computer Assisted Intervention, Shanghai, 200433, China. Electronic address:
Background And Objective: Utilizing AI to mine tumor microenvironment information in whole slide images (WSIs) for glioma molecular subtype and prognosis prediction is significant for treatment. Existing weakly-supervised learning frameworks based on multi-instance learning have potential in WSIs analysis, but the large number of patches from WSIs challenges the effective extraction of key local patch and neighboring patch microenvironment info. Therefore, this paper aims to develop an automatic neural network that effectively extracts tumor microenvironment information from WSIs to predict molecular typing and prognosis of glioma.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Rehabilitation, Greater Poland Cancer Centre, 61-866 Poznan, Poland.
Understanding the role of personality traits in shaping treatment outcomes is crucial given the multifaceted challenges posed by brain tumors and the significant adverse impact of radiotherapy (RT) on patients' well-being. This study aimed to provide insights into how personality traits affect psychosocial well-being and quality of life during RT in patients with high-grade malignant brain tumors. Personality traits in patients with high-grade glioma were assessed using the Eysenck Personality Questionnaire-Revised (EPQ-R).
View Article and Find Full Text PDFMed Image Anal
December 2024
University Lyon, INSA Lyon, CNRS, Inserm, CREATIS UMR5220, U1206, Lyon 69621, France.
Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack of interpretable biomarkers. How to accurately predict the glioma histological grade and molecular subtypes at the same time and provide reliable imaging biomarkers is still challenging.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurosurgery, West China Hospital, Sichuan University, 37 Guoxue Avenue, Chengdu, 610041, People's Republic of China.
Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glioma diagnosis. We analyzed 472 whole slide images (WSIs) from 226 patients in West China Hospital (WCH) and 1604 WSIs from 880 patients in The Cancer Genome Atlas (TCGA).
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