Brain Cancer

Detection of Hemodynamically Significant Coronary Stenosis: CT Myocardial Perfusion versus Machine Learning CT Fractional Flow Reserve.

Background Direct intraindividual comparison of dynamic CT myocardial perfusion imaging (MPI) and machine learning (ML)-based CT fractional flow reserve (FFR) has not been explored for diagnosing hemodynamically significant coronary artery disease. Purpose To investigate the diagnostic performance of dynamic CT MPI and ML-based CT FFR for functional assessment of coronary stenosis. Materials and M...

Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm.

PURPOSE: Patients with 1p/19q codeleted low-grade glioma (LGG) have longer overall survival and better treatment response than patients with 1p/19q intact tumors. Therefore, it is relevant to know the 1p/19q status. To investigate whether the 1p/19q status can be assessed prior to tumor resection, we developed a machine learning algorithm to predict the 1p/19q status of presumed LGG based on preop...

Single patient convolutional neural networks for real-time MR reconstruction: a proof of concept application in lung tumor segmentation for adaptive radiotherapy.

Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. I...

How Much Was the Elective Lymph Node Region Covered in Involved-Field Radiation Therapy for Locally Advanced Pancreatic Cancer? Evaluation of Overlap Between Gross Target Volume and Celiac Artery-Superior Mesenteric Artery Lymph Node Regions.

PURPOSE: The purpose of this study was to investigate the overlaps between gross target volume (GTV) and the celiac artery (CA) and superior mesenteri...

Artificial Intelligence in Radiation Oncology.

The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initia...

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning.

We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultr...

Radiation-Induced Hypothyroidism After Radical Intensity Modulated Radiation Therapy for Oropharyngeal Carcinoma.

PURPOSE: To evaluate 2 published normal tissue complication probability models for radiation-induced hypothyroidism (RHT) on a large cohort of orophar...

3D cone-beam CT of the ankle using a novel twin robotic X-ray system: Assessment of image quality and radiation dose.

PURPOSE: To evaluate image quality (IQ) and radiation dose in cone-beam computed tomography (CBCT) of the ankle using a novel twin robotic X-ray syste...

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also determinis...

Assessment of Polyethylene Glycol Hydrogel Spacer and Its Effect on Rectal Radiation Dose in Prostate Cancer Patients Receiving Proton Beam Radiation Therapy.

PURPOSE: To assess the efficacy of placing a polyethylene glycol (PEG) spacing hydrogel in patients undergoing proton beam radiation therapy for prost...

Spatial Radiation Dose Influence on Xerostomia Recovery and Its Comparison to Acute Incidence in Patients With Head and Neck Cancer.

PURPOSE: Radiation-induced xerostomia is one of the most prevalent symptoms during and after head and neck cancer radiation therapy (RT). We aimed to ...

Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the...

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric mo...

Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

BACKGROUND AND PURPOSE: Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed i...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-...

Automated brain extraction of multisequence MRI using artificial neural networks.

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influenc...

Personalized oncology with artificial intelligence: The case of temozolomide.

PURPOSE: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patien...

Machine learning applications in prostate cancer magnetic resonance imaging.

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic...

Machine learning and glioma imaging biomarkers.

AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment res...

Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.

PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed ...