Cardiovascular

Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks.

Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However, PET imaging requires not only additional computed tomography (CT) imaging to provide detailed anatomical information but also attenuation correction (AC) maps calculated from CT images for precise PET quantification, which inevitably demands that p...

Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast can...

Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.

PURPOSE: This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy...

Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach.

PURPOSE: To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck ...

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (p...

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines...

The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective.

As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern ana...

Integration of AI and Machine Learning in Radiotherapy QA.

The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disc...

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer v...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lun...

Bioethics and healthcare policies. The benefit of using genetic tests of BRCA 1 and BRCA 2 in elderly patients.

This study focuses on the role of bioethics in designing public healthcare policies towards elderly patients with cancer. The general overview of publ...

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and ...

A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea.

BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are ...

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.

BACKGROUND: Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotat...

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

PURPOSE: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management ...

Robot-assisted Nipple-sparing Mastectomy With Immediate Breast Reconstruction: An initial Experience of the Korea Robot-endoscopy Minimal Access Breast Surgery Study Group (KoREa-BSG).

OBJECTIVE: The aim of this study was to present the results of early experience of robot-assisted nipple sparing mastectomy (RANSM).

Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we ut...

Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a...

Deep learning-based metal artifact reduction using cycle-consistent adversarial network for intensity-modulated head and neck radiation therapy treatment planning.

PURPOSE: To develop a deep learning-based metal artifact reduction (DL-MAR) method using unpaired data and to evaluate its dosimetric impact in head a...