Cardiovascular

[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data].

INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response to neoadjuvant chemotherapy determines continuation of treatment and is also a powerful prognostic factor. There are currently no reliable ways to evaluate it early. The aim is to develop a method to predict the chemotherapy response using radiomics from pre-treatment MRI.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to...

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 reser...

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...

Designing combination therapies with modeling chaperoned machine learning.

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of eff...

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...

Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification.

Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So far, they have been recognized as potential biomar...

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 ...

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...

Multigene signatures of responses to chemotherapy derived by biochemically-inspired machine learning.

Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinati...

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-...

Machine learning-based prediction of breast cancer growth rate in vivo.

BACKGROUND: Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for tre...

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.

PURPOSE: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the p...

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...