Brain Cancer

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

There have been many recently published studies exploring machine learning (ML) and deep learning ap...

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Deep learning represents end-to-end machine learning in which feature selection from images and clas...

GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images.

Brain tumors are one of the major common causes of cancer-related death, worldwide. Growth predictio...

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

PURPOSE: Deep learning has shown great efficacy for semantic segmentation. However, there are diffic...

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-...

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 da...

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...

Use of artificial intelligence in computed tomography dose optimisation.

The field of artificial intelligence (AI) is transforming almost every aspect of modern society, inc...

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 a...

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 i...

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 major...

Artificial intelligence and soft skills in radiation oncology: Data versus wisdom.

Artificial intelligence in radiation oncology.

Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised....

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 g...

Technical Note: Deep Learning approach for automatic detection and identification of patient positioning devices for radiation therapy.

PURPOSE: Automatic detection and identification of setup devices, using a deep convolutional neural ...

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...

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 e...

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 radiat...

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 distribut...

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 ...

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 ...