AIMC Topic: Magnetic Resonance Imaging
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Diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors: A systematic review and meta-analysis.
European journal of radiology
Jun 7, 2025
RATIONALE AND OBJECTIVES: This systematic review and meta-analysis aimed to assess the diagnostic accuracy of radiomics in risk stratification of gastrointestinal stromal tumors (GISTs). It focused on evaluating radiomic models as a non-invasive tool...
Contribution of Labrum and Cartilage to Joint Surface in Different Hip Deformities: An Automatic Deep Learning-Based 3-Dimensional Magnetic Resonance Imaging Analysis.
The American journal of sports medicine
Jun 7, 2025
BACKGROUND: Multiple 2-dimensional magnetic resonance imaging (MRI) studies have indicated that the size of the labrum adjusts in response to altered joint loading. In patients with hip dysplasia, it tends to increase as a compensatory mechanism for ...
Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma.
Scientific reports
Jun 6, 2025
Posterior pituitary tumors (PPTs) are rare neoplasms, but easily misdiagnosed as pituitary neuroendocrine tumor (PitNET) and craniopharyngioma. This study aimed to differentiate PPTs from PitNET and craniopharyngioma using a machine learning method b...
Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.
BMC medical informatics and decision making
Jun 5, 2025
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.
StrokeNeXt: an automated stroke classification model using computed tomography and magnetic resonance images.
BMC medical imaging
Jun 5, 2025
BACKGROUND AND OBJECTIVE: Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightwe...
A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.
Scientific reports
Jun 5, 2025
In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diagnostic accuracy and therapeutic outcomes, particularly in regions with uneven healthcare resource di...
Epistasis regulates genetic control of cardiac hypertrophy.
Nature cardiovascular research
Jun 5, 2025
Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, u...
Induced Pluripotent Stem Cells
Connectin
Ventricular Remodeling
Myocytes, Cardiac
Humans
Genetic Predisposition to Disease
Hypertrophy, Left Ventricular
Male
Deep Learning
Phenotype
Epistasis, Genetic
Magnetic Resonance Imaging
Middle Aged
Female
Genome-Wide Association Study
Receptor, IGF Type 1
Aged
Gene Regulatory Networks
Heart Failure
Recognition of flight cadets brain functional magnetic resonance imaging data based on machine learning analysis.
PloS one
Jun 5, 2025
The rapid advancement of the civil aviation industry has attracted significant attention to research on pilots. However, the brain changes experienced by flight cadets following their training remain, to some extent, an unexplored territory compared ...
Radiomics-Based Differentiation of Primary Central Nervous System Lymphoma and Solitary Brain Metastasis Using Contrast-Enhanced T1-Weighted Imaging: A Retrospective Machine Learning Study.
Academic radiology
Jun 4, 2025
PURPOSE: To develop and evaluate radiomics-based models using contrast-enhanced T1-weighted imaging (CE-T1WI) for the non-invasive differentiation of primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (SBM), aiming to impro...
The value of machine learning based on magnetic resonance imaging (MRI) and biopsy whole-slide image to predict pathological complete response to breast cancer after neoadjuvant chemotherapy: a two-centre study.
Clinical radiology
Jun 4, 2025
AIM: To develop and validate a combined model based on magnetic resonance imaging (MRI), and whole-slide imaging (WSI) to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.
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