Oncology/Hematology

Brain Cancer

Latest AI and machine learning research in brain cancer for healthcare professionals.

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Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

PURPOSE: Radiation pneumonitis is an important adverse event in patients with non-small cell lung ca...

MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach.

PURPOSE: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic ...

Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.

PURPOSE: There has been burgeoning interest in applying machine learning methods for predicting radi...

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform...

Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy.

INTRODUCTION: Cone-beam CT (CBCT) image quality is important for its quantitative analysis in adapti...

Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.

Background Nasopharyngeal carcinoma (NPC) may be cured with radiation therapy. Tumor proximity to cr...

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma.

BACKGROUND: The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging ...

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of...

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the opti...

Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medi...

3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of th...

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating gl...

Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes.

Stereotactic body radiation therapy (SBRT) is a relatively novel treatment modality, with little pos...

Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach.

In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning ...

Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning.

The recent explosion in machine learning efforts in the quality assurance (QA) space has produced a ...

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

BACKGROUND AND PURPOSE: MR imaging-based modeling of tumor cell density can substantially improve ta...

Deep Learning-Based Framework for Identification of Glioblastoma Tumor using Hyperspectral Images of Human Brain.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving a...

The Adaptive Hermite Fractal Tree (AHFT): a novel surgical 3D path planning approach with curvature and heading constraints.

PURPOSE: In the context of minimally invasive neurosurgery, steerable needles such as the one develo...

Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.

PURPOSE: In recent years, health risks concerning high-dose x-ray radiation have become a major conc...

Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.

OBJECTIVE: Isocitrate dehydrogenase 1 (IDH1) mutation status is an independent favorable prognostic ...

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