Brain Cancer

Automatic Segmentation Using Deep Learning to Enable Online Dose Optimization During Adaptive Radiation Therapy of Cervical Cancer.

PURPOSE: This study investigated deep learning models for automatic segmentation to support the development of daily online dose optimization strategies, eliminating the need for internal target volume expansions and thereby reducing toxicity events of intensity modulated radiation therapy for cervical cancer.

Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks.

Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However, PET imaging requires not only additional computed tomography (CT) imaging to provide detailed anatomical information but also attenuation correction (AC) maps calculated from CT images for precise PET quantification, which inevitably demands that p...

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.

OBJECTIVES: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is...

Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.

PURPOSE: This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy...

A machine learning analysis of a "normal-like" IDH-WT diffuse glioma transcriptomic subgroup associated with prolonged survival reveals novel immune and neurotransmitter-related actionable targets.

BACKGROUND: Classification of primary central nervous system tumors according to the World Health Organization guidelines follows the integration of h...

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.

PURPOSE: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck ...

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI.

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, mos...

The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective.

As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern ana...

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disc...

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer v...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lun...

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma.

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This s...

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma.

INTRODUCTION: Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demon...

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and ...

Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis.

Methylation of the O-methylguanine methyltransferase (MGMT) gene promoter is correlated with the effectiveness of the current standard of care in glio...

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 applications within neuroradiology. The improvement ...

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 classification happen concurrently. This articles prov...

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 prediction of these tumors, particularly gliomas which are ...

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 difficulties in the collection, labeling and management ...

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-consuming task. In this study, we aim to develop a...