AIMC Topic: Brain Neoplasms

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Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Scientific reports
Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the ...

A deep convolutional neural network for the automatic segmentation of glioblastoma brain tumor: Joint spatial pyramid module and attention mechanism network.

Artificial intelligence in medicine
This study proposes a deep convolutional neural network for the automatic segmentation of glioblastoma brain tumors, aiming sat replacing the manual segmentation method that is both time-consuming and labor-intensive. There are many challenges for au...

Medical image synthesis via conditional GANs: Application to segmenting brain tumours.

Computers in biology and medicine
Accurate brain tumour segmentation is critical for tasks such as surgical planning, diagnosis, and analysis, with magnetic resonance imaging (MRI) being the preferred modality due to its excellent visualisation of brain tissues. However, the wide int...

Evolutionary gravitational neocognitron neural network optimized with marine predators optimization algorithm for MRI brain tumor classification.

Electromagnetic biology and medicine
Magnetic resonance imaging (MRI) is a powerful tool for tumor diagnosis in human brain. Here, the MRI images are considered to detect the brain tumor and classify the regions as meningioma, glioma, pituitary and normal types. Numerous existing method...

Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.

Scientific reports
Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We aimed to identify different tear statuses from preoperative MRI using deep learning-based radiomics with sex and age. We reviewed 862 patients with preo...

A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.

European radiology experimental
BACKGROUND: To establish a predictive model based on multisequence magnetic resonance imaging (MRI) using deep learning to identify wild-type (WT) epidermal growth factor receptor (EGFR), EGFR exon 19 deletion (19Del), and EGFR exon 21-point mutation...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tom...

Detection and classification of brain tumor using hybrid deep learning models.

Scientific reports
Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diag...

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy ...