Cardiovascular

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.

BACKGROUND: Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients.

Clavien-Dindo classification and risk prediction model of complications after robot-assisted radical hysterectomy for cervical cancer.

Although significant progress has been made with surgical methods, the incidence of complications after minimally invasive surgery in patients with cervical cancer remains high. Established as a standardized system, Clavien-Dindo classification (CDC) has been applied in a variety of surgical fields. This study is designed to evaluate the complications after robot-assisted radical hysterectomy (RRH...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose di...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always resul...

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer va...

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

BACKGROUND: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, ...

A deep image-to-image network organ segmentation algorithm for radiation treatment planning: principles and evaluation.

BACKGROUND: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomogra...

The evaluation of the reduction of radiation dose via deep learning-based reconstruction for cadaveric human lung CT images.

To compare the quality of CT images of the lung reconstructed using deep learning-based reconstruction (True Fidelity Image: TFI ™; GE Healthcare) to ...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current s...

Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.

The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistica...

Emphysema Quantification Using Ultra-Low-Dose Chest CT: Efficacy of Deep Learning-Based Image Reconstruction.

Background and Objectives: Although reducing the radiation dose level is important during diagnostic computed tomography (CT) applications, effective ...

Digital skills of therapeutic radiographers/radiation therapists - Document analysis for a European educational curriculum.

INTRODUCTION: It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Rad...

The Effect of Salvage Radiation Therapy on Survival, Functional Outcomes, and Quality of Life in Men with Persistent Prostate-specific Antigen After Robot-Assisted Radical Prostatectomy: Which Patient Benefits More?

PURPOSE: The aim of this study was to evaluate the effect of salvage radiation therapy (sRT) on survival, functional outcomes, and quality of life in ...

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incu...

Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT signif...

MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping.

To the best of our knowledge, artificial intelligence stain generation is an urgent requirement for histopathology images. Pathological examinations u...

Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images.

Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70 % of advanced patients are with ...

Vomiting Management and Effect Prediction after Early Chemotherapy of Lung Cancer with Diffusion-Weighted Imaging under Artificial Intelligence Algorithm and Comfort Care Intervention.

This aim of this research was to explore the evaluation and prediction value of diffusion-weighted imaging (DWI) under artificial intelligence algorit...

Deep learning based analysis of microstructured materials for thermal radiation control.

Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospa...

Liver Attenuation Assessment in Reduced Radiation Chest Computed Tomography.

OBJECTIVE: This study aimed to evaluate the reliability of liver and spleen Hounsfield units (HU) measurements in reduced radiation computed tomograph...