Brain Cancer

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head and neck intensity-modulated radiation therapy (IMRT) compared with the water density override method.

System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high...

Use of artificial intelligence in computed tomography dose optimisation.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, including medical imaging. In computed tomography (CT...

Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy.

Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (...

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this mod...

Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI.

PURPOSE: To investigate the prediction performance of radiomic models based on multiparametric MRI in predicting the meningioma grade.

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the s...

Artificial intelligence in radiation oncology.

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex pattern...

A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signatur...

Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques.

BACKGROUND: This study aims to identify robust radiomic features for Magnetic Resonance Imaging (MRI), assess feature selection and machine learning m...

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach.

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astr...

A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy.

BACKGROUND: To investigate the feasibility of a knowledge-based automated intensity-modulated radiation therapy (IMRT) planning technique for locally ...

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy.

PURPOSE: Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models...

Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors.

Advances in computerized image analysis and the use of artificial intelligence-based approaches for image-based analysis and construction of predictio...

Artificial intelligence in radiotherapy: a technological review.

Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-...

Radiomics and Deep Learning from Research to Clinical Workflow: Neuro-Oncologic Imaging.

Imaging plays a key role in the management of brain tumors, including the diagnosis, prognosis, and treatment response assessment. Radiomics and deep ...

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.

PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, ac...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Im...

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning ha...

A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas.

Glioblastoma is the most common malignant brain parenchymal tumor yet remains challenging to treat. The current standard of care-resection and chemora...