Brain Cancer

Orthopedic Robot-Assisted Femoral Neck System in the Treatment of Femoral Neck Fracture.

Cannulated screw fixation is the main therapy for femoral neck fractures, especially in young patients. The traditional surgical procedure uses C-arm fluoroscopy to place the screw freehand and requires several guide wire adjustments, which increases the operation time and radiation exposure. Repeated drilling can also cause damage to the blood supply and bone quality of the femoral neck, which ca...

Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and...

A Retrospective Study of Puncture and Drainage for Primary Brainstem Hemorrhage With the Assistance of a Surgical Robot.

BACKGROUND: Whether primary brainstem hemorrhage (PBH) should be treated with a conservative treatment or with surgical intervention (such as cranioto...

Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy.

PURPOSE: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in l...

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oroph...

Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow.

. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmenta...

PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images.

Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have bee...

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guided radiation therapy. However, the image quality of...

Reducing both radiation and contrast doses for overweight patients in coronary CT angiography with 80-kVp and deep learning image reconstruction.

PURPOSE: To investigate the use of an 80-kVp tube voltage combined with a deep learning image reconstruction (DLIR) algorithm in coronary CT angiograp...

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.

BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-gui...

Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM).

PURPOSE: The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) is a recently released guideline designed for the optimal reporting meth...

Machine intelligence for radiation science: summary of the Radiation Research Society 67th annual meeting symposium.

The era of high-throughput techniques created big data in the medical field and research disciplines. Machine intelligence (MI) approaches can overcom...

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, h...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learning model for automatic contour propagation in brea...

Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging.

BACKGROUND: Determination of H3 K27M mutation in diffuse midline glioma (DMG) is key for prognostic assessment and stratifying patient subgroups for c...

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques.

BACKGROUND: Detecting brain tumors in their early stages is crucial. Brain tumors are classified by biopsy, which can only be performed through defini...

Review of Machine Learning and Artificial Intelligence (ML/AI) for the Pediatric Neurologist.

Artificial intelligence (AI) and a popular branch of AI known as machine learning (ML) are increasingly being utilized in medicine and to inform medic...

Effectiveness of mobile robots collecting vital signs and radiation dose rate for patients receiving Iodine-131 radiotherapy: A randomized clinical trial.

OBJECTIVE: Patients receiving radionuclide 131I treatment expose radiation to others, and there was no clinical trial to verify the effectiveness and ...

Prostatic urinary tract visualization with super-resolution deep learning models.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been...

A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed ...