Brain Cancer

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.

The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for delineation of gross tumor volume (GTV) regions of lung cancer for stereotactic body radiation therapy. The morphological and metabolic features for G...

Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning.

Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar basis rather than inoculum availability....

Robotics in percutaneous cardiovascular interventions.

The fundamental technique of performing percutaneous cardiovascular (CV) interventions has remained unchanged and requires operators to wear heavy lea...

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep feat...

Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients.

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with bette...

The pervasive crisis of diminishing radiation therapy access for vulnerable populations in the United States, part 2: American Indian patients.

INTRODUCTION: American Indian/Alaska Native (AI/AN) patients with cancer disproportionally present with more advanced stages of disease and have the w...

Brain tumor segmentation using holistically nested neural networks in MRI images.

PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used te...

Intranasal bevacizumab treatment on epistaxis in hereditary haemorrhagic telangiectasia: a case report.

Hereditary haemorrhagic telangiectasia (HHT), also known as Osler-Weber-Rendu disease, is a rare, vascular, autosomal dominant disorder. The purpose o...

Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy source...

Robotic technology in interventional cardiology: Current status and future perspectives.

Robotic technology has been utilized in cardiovascular medicine for over a decade, and over that period, its use has been expanded to percutaneous cor...

Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection.

 Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and m...

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

Shortwave solar radiation is an important component of the surface energy balance and provides the principal source of energy for terrestrial ecosyste...

Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma.

Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performa...

Prostate-Specific Membrane Antigen Positron Emission Tomography-Computed Tomography for Prostate Cancer: Distribution of Disease and Implications for Radiation Therapy Planning.

PURPOSE: To explore the prostate-specific membrane antigen (PSMA)-avid distribution of prostate cancer (PC) on positron emission tomography (PET), bot...

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the cli...

DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients...

Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients?

BACKGROUND: Knowledge-based planning (KBP) is a promising technique that can improve plan quality and increase planning efficiency. However, no attemp...

Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT.

Machine learning applications for personalized medicine are highly dependent on access to sufficient data. For personalized radiation oncology, datase...

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

BACKGROUND AND PURPOSE: Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid un...

[Artificial intelligence applied to radiation oncology].

Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the...