AIMC Topic: Magnetic Resonance Imaging

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Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.

BMC medical imaging
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...

Enhancing meningioma tumor classification accuracy through multi-task learning approach and image analysis of MRI images.

PloS one
BACKGROUND: Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potentia...

High-resolution mapping of alcohol-related brain connectivity in adults using 7T fMRI and multivoxel pattern classification.

Psychiatry research. Neuroimaging
BACKGROUND: Emerging evidence suggests that alcohol use disrupts large-scale brain network interactions, particularly within the triple network model-comprising the Salience Network (SN), Default Mode Network (DMN), and Frontoparietal Network (FPN). ...

Improving early detection of Alzheimer's disease through MRI slice selection and deep learning techniques.

Scientific reports
Alzheimer's disease is a progressive neurodegenerative disorder marked by cognitive decline, memory loss, and behavioral changes. Early diagnosis, particularly identifying Early Mild Cognitive Impairment (EMCI), is vital for managing the disease and ...

Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.

Scientific reports
Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models-including Clinical mode...

Deep Learning-aided H-MR Spectroscopy for Differentiating between Patients with and without Hepatocellular Carcinoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Among patients with hepatitis B virus-associated liver cirrhosis (HBV-LC), there may be differences in the hepatic parenchyma between those with and without hepatocellular carcinoma (HCC). Proton MR spectroscopy (H-MRS) is a well-established...

FGDN: A Federated Graph Convolutional Network framework for multi-site major depression disorder diagnosis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The vast amount of healthcare data is characterized by its diversity, dynamic nature, and large scale. It is a challenge that directly training a Graph Convolutional Neural Network (GCN) in a multi-site dataset poses to protecting the privacy of Majo...

Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.

Scientific reports
Accurate detection of brain tumors remains a significant challenge due to the diversity of tumor types along with human interventions during diagnostic process. This study proposes a novel ensemble deep learning system for accurate brain tumor classi...

BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.

Scientific reports
Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonan...

MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

International journal of colorectal disease
PUROPOSE: Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively impro...