Oncology/Hematology

Brain Cancer

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

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Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling ...

A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.

Medical ionizing radiation procedures and especially medical imaging are a non negligible source of ...

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia.

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the ...

Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data.

Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disor...

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy.

BACKGROUND: Surface-guided radiation therapy can be used to continuously monitor a patient's surface...

DaNet: dose-aware network embedded with dose-level estimation for low-dose CT imaging.

Many deep learning (DL)-based image restoration methods for low-dose CT (LDCT) problems directly emp...

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

PURPOSE: To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserv...

Performance of deep learning synthetic CTs for MR-only brain radiation therapy.

PURPOSE: To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel...

Diagnostic value of deep learning reconstruction for radiation dose reduction at abdominal ultra-high-resolution CT.

OBJECTIVES: We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiat...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation thera...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-t...

Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.

Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to...

Weight-bearing CT in foot and ankle pathology.

Cone-beam scanners (CBCT) enable CT to be performed under weight-bearing - notably for the foot and ...

Improved Glioma Grading Using Deep Convolutional Neural Networks.

BACKGROUND AND PURPOSE: Accurate determination of glioma grade leads to improved treatment planning....

Impacts of speciation and extinction measured by an evolutionary decay clock.

The hypothesis that destructive mass extinctions enable creative evolutionary radiations (creative d...

Analyzing magnetic resonance imaging data from glioma patients using deep learning.

The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain t...

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung canc...

Estimating Local Cellular Density in Glioma Using MR Imaging Data.

BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the...

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