AIMC Topic: Radiosurgery

Clear Filters Showing 1 to 10 of 163 articles

Adaptive radiotherapy for gastrointestinal malignancies.

Radiation oncology (London, England)
BACKGROUND: Adaptive radiotherapy (ART) is an advanced form of image-guided radiotherapy that involves the re-contouring and re-planning of a patient's treatment plan, either while the patient is on the table (online) or in between fractions (offline...

The role of artificial intelligence in predicting the clinical outcomes associated with different therapeutic approaches for vestibular schwannoma: A systematic review and meta-analysis.

Neurosurgical review
INTRODUCTION: Vestibular schwannoma is the most common neoplasm located at the skull base. The therapeutic strategy for managing vestibular schwannoma is formulated based on individual patient characteristics and specific imaging findings. Recently, ...

Automated segmentation of brain metastases in magnetic resonance imaging using deep learning in radiotherapy.

Scientific reports
Brain metastases (BMs) are the most common intracranial tumors and stereotactic radiotherapy improved the life quality of patient with BMs, while it requires more time and experience to delineate BMs precisely by oncologists. Deep Learning techniques...

Integrating artificial intelligence with Gamma Knife radiosurgery in treating meningiomas and schwannomas: a review.

Neurosurgical review
Meningiomas and schwannomas are benign tumors that affect the central nervous system, comprising up to one-third of intracranial neoplasms. Gamma Knife radiosurgery (GKRS), or stereotactic radiosurgery (SRS), is a form of radiation therapy. Although ...

Enhancing synthetic pelvic CT generation from CBCT using vision transformer with adaptive fourier neural operators.

Biomedical physics & engineering express
This study introduces a novel approach to improve Cone Beam CT (CBCT) image quality by developing a synthetic CT (sCT) generation method using CycleGAN with a Vision Transformer (ViT) and an Adaptive Fourier Neural Operator (AFNO).A dataset of 20 pro...

Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes.

JCO clinical cancer informatics
PURPOSE: Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health records is a critical but challenging task. Traditional methods of identifying the primary tumor his...

A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy (SBRT) is vertebral compression fracture (VCF). There is interest in the development of patient-specific tools that can predict those at high risk of...

Using deep learning generated CBCT contours for online dose assessment of prostate SABR treatments.

Journal of applied clinical medical physics
Prostate Stereotactic Ablative Body Radiotherapy (SABR) is an ultra-hypofractionated treatment where small setup errors can lead to higher doses to organs at risk (OARs). Although bowel and bladder preparation protocols reduce inter-fraction variabil...

Deep learning for automated segmentation of brain edema in meningioma after radiosurgery.

BMC medical imaging
BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding brain tissue can lead to perifocal edema within a few months after the procedure. Volumetric assessmen...

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

Biomedical physics & engineering express
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...