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Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.
J Imaging Inform Med
January 2025
Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur, India.
Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades.
View Article and Find Full Text PDFMRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies.
Acad Radiol
December 2024
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104 (D.A.T.). Electronic address:
Article Synopsis
- Prostate cancer is a prevalent and serious health issue for men, and this study aims to evaluate how effective radiomics is in predicting the cancer grade.
- The research systematically reviewed 43 studies involving nearly 10,000 patients, using advanced imaging techniques and established quality assessment tools to analyze data.
- Results indicate that radiomics models show high accuracy in predicting prostate cancer grades, suggesting they could enhance traditional diagnostic methods and improve clinical decision-making.
Separating dementia with Lewy bodies from Alzheimer's disease dementia using a volumetric MRI classifier.
Eur Radiol
December 2024
Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Objectives: Distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) dementia, particularly in patients with DLB and concomitant AD pathology (DLB/AD+), can be challenging and there is no specific MRI signature for DLB. The aim of this study is to examine the additional value of MRI-based brain volumetry in separating patients with DLB (AD+/-) from patients with AD and controls.
Methods: We included 1518 participants from four cohorts (ADC, ADNI, PDBP and PredictND); 147 were patients with DLB (n = 76, DLB/AD+; n = 71, DLB/AD-), 668 patients with AD dementia, and 703 controls.
Clinical and Radiological Features of Perianal Fistula: An MRI-Based Study in Addis Ababa, Ethiopia.
Ethiop J Health Sci
October 2024
St. Paul Millennium Medical College, Department of Radiology, Addis Ababa, Ethiopia.
Background: Perianal fistula refers to an abnormal connection between the anal canal and the perianal skin or perineum. Magnetic Resonance Imaging (MRI) plays a crucial role in accurately characterizing perianal fistulas, which informs surgical strategies and helps minimize recurrence.
Methods: This cross-sectional study was conducted at a single diagnostic imaging center in Addis Ababa, utilizing retrospectively collected data from May 2023 to June 2024.
Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities.
J Imaging
November 2024
2nd Department of Radiology, Medical School, Attikon University Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection.
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