Oncology/Hematology

Brain Cancer

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

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Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.

AIM: To investigate machine learning based models combining clinical, radiomic, and molecular inform...

Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.

PURPOSE: Surgery is the predominant treatment modality of human glioma but suffers difficulty on cle...

Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections.

The opportunistic exchange of information between vehicles can significantly contribute to reducing ...

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to pred...

The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.

Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tu...

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation.

The two-dimensional nature of mammography makes estimation of the overall breast density challenging...

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one...

A New Era of Neuro-Oncology Research Pioneered by Multi-Omics Analysis and Machine Learning.

Although the incidence of central nervous system (CNS) cancers is not high, it significantly reduces...

Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Gliomas are one of the most devastating primary brain tumors which impose significant management cha...

Simulating reference crop evapotranspiration with different climate data inputs using Gaussian exponential model.

Obtaining accurate data on reference crop evapotranspiration (ET) is important for agricultural wate...

Current applications of deep-learning in neuro-oncological MRI.

PURPOSE: Magnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detec...

A machine learning-based survival prediction model of high grade glioma by integration of clinical and dose-volume histogram parameters.

PURPOSE: Glioma is the most common type of primary brain tumor in adults, and it causes significant ...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often un...

Radiation therapy dose prediction for left-sided breast cancers using two-dimensional and three-dimensional deep learning models.

PURPOSE: To develop a deep learning model capable of producing clinically acceptable dose distributi...

Development and Validation of a Deep Learning-Based Model to Distinguish Glioblastoma from Solitary Brain Metastasis Using Conventional MR Images.

BACKGROUND AND PURPOSE: Differentiating glioblastoma from solitary brain metastasis preoperatively u...

Radiation dose calculation in 3D heterogeneous media using artificial neural networks.

PURPOSE: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of...

CycleGAN for interpretable online EMT compensation.

PURPOSE: Electromagnetic tracking (EMT) can partially replace X-ray guidance in minimally invasive p...

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundar...

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