Brain Cancer

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.

To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural networ...

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune i...

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and ma...

Harnessing generative artificial intelligence for meningioma prediction: a correspondence.

Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready.

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are appr...

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

BACKGROUND: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorit...

Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.

OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung ca...

Hierarchical decomposed dual-domain deep learning for sparse-view CT reconstruction.

. X-ray computed tomography employing sparse projection views has emerged as a contemporary technique to mitigate radiation dose. However, due to the ...

Letter: Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases.

Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy - A Proof of Concept.

PURPOSE: The dose deposited outside of the treatment field during external photon beam radiation therapy treatment, also known as out-of-field dose, i...

Employing deep learning and transfer learning for accurate brain tumor detection.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process la...

Machine learning-aided search for ligands of P2Y and other P2Y receptors.

The P2Y receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet...

Transcranial Magnetic Stimulation-Based Machine Learning Prediction of Tumor Grading in Motor-Eloquent Gliomas.

BACKGROUND: Navigated transcranial magnetic stimulation (nTMS) is a well-established preoperative mapping tool for motor-eloquent glioma surgery. Mach...

Application of deep learning in radiation therapy for cancer.

In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has ...

Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma.

Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual respo...

Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.

OBJECTIVE: This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in l...

Comparative analysis of radiation exposure in robot-assisted total knee arthroplasty using popular robotic systems.

Robotic-assisted TKA (RATKA) is a rapidly emerging technique that has been shown to improve precision and accuracy in implant alignment in TKA. Roboti...

Artificial intelligence-based MRI radiomics and radiogenomics in glioma.

The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists...

Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment.

RATIONALE AND OBJECTIVES: To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of h...

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susce...