Brain Cancer

Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.

OBJECTIVES: Little research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide.

Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy.

Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introd...

Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them.

In radiation oncology, Machine Learning classification publications are typically related to two outcome classes, e.g. the presence or absence of dist...

Quantitative Thermal Imaging Biomarkers to Detect Acute Skin Toxicity From Breast Radiation Therapy Using Supervised Machine Learning.

PURPOSE: Radiation-induced dermatitis is a common side effect of breast radiation therapy (RT). Current methods to evaluate breast skin toxicity inclu...

A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy.

PURPOSE: Beam orientation selection, whether manual or protocol-based, is the current clinical standard in radiation therapy treatment planning, but i...

DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet.

PURPOSE: Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. PsP exhibits ...

Publication Landscape Analysis on Gliomas: How Much Has Been Done in the Past 25 Years?

The body of glioma-related literature has grown significantly over the past 25 years. Despite this growth in the amount of published research, glioma...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the ...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patien...

A Multiple-Instance Learning-Based Convolutional Neural Network Model to Detect the Mutation in the Histopathology Images of Glioma Tissues.

Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutiona...

Cardiac substructure segmentation with deep learning for improved cardiac sparing.

PURPOSE: Radiation dose to cardiac substructures is related to radiation-induced heart disease. However, substructures are not considered in radiation...

Intracatheter Tissue Plasminogen Activator for Chronic Subdural Hematomas after Failed Bedside Twist Drill Craniostomy: A Retrospective Review.

Introduction Chronic subdural hematomas (cSDH) are common in neurosurgery with various symptoms and significant morbidity and mortality. Treatment var...

Prediction of lower-grade glioma molecular subtypes using deep learning.

INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagno...

Efficient identification of novel anti-glioma lead compounds by machine learning models.

Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumor. Pharmacological treatment of this malignanc...

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas.

BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, target...

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs)...

An investigation of machine learning methods in delta-radiomics feature analysis.

PURPOSE: This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malig...

Development of a robot-assisted ultrasound-guided radiation therapy (USgRT).

PURPOSE: Radiation treatment is improved by the use of image-guided workflows. This work pursues the approach of using ultrasound (US) as a real-time ...

Twin Robotic X-Ray System for 3D Cone-Beam CT of the Wrist: An Evaluation of Image Quality and Radiation Dose.

The purpose of this study was to assess image quality and radiation dose of a novel twin robotic x-ray system's 3D cone-beam CT (CBCT) function for t...