Lung Cancer

Prostate Stereotactic Body Radiation Therapy With a Focal Simultaneous Integrated Boost: Acute Toxicity and Dosimetry Results From a Prospective Trial.

PURPOSE: This study aimed to report the early toxicity results of a prospective clinical trial of prostate stereotactic body radiation therapy (SBRT) to the entire prostate with a simultaneous integrated boost (SIB) to magnetic resonance imaging (MRI)-defined focal lesions.

Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.

PURPOSE: Intensity modulated radiation therapy (IMRT) is commonly employed for treating head and neck (H&N) cancer with uniform tumor dose and conformal critical organ sparing. Accurate delineation of organs-at-risk (OARs) on H&N CT images is thus essential to treatment quality. Manual contouring used in current clinical practice is tedious, time-consuming, and can produce inconsistent results. Ex...

PBN11-8, a Cytotoxic Polypeptide Purified from Marine , Suppresses Invasion and Migration of Human Hepatocellular Carcinoma Cells by Targeting Focal Adhesion Kinase Pathways.

The development of antitumor drugs has attracted cancer researchers and the identification of novel antitumor lead compounds is certainly of great int...

A dynamic model for predicting graft function in kidney recipients' upcoming follow up visits: A clinical application of artificial neural network.

BACKGROUND: Predicting the function of transplanted kidneys would help clinicians in individualized medical interventions. We aimed to develop and val...

Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT.

BACKGROUND: Accurate prediction of radiation toxicity of healthy organs-at-risks (OARs) critically determines the radiation therapy (RT) success. The ...

Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks.

PURPOSE: Convolutional neural networks have become rapidly popular for image recognition and image analysis because of its powerful potential. In this...

The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques.

PURPOSE: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appeal...

Machine learning and modeling: Data, validation, communication challenges.

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatme...

RASPELD to Perform High-End Screening in an Academic Environment toward the Development of Cancer Therapeutics.

The identification of compounds for dissecting biological functions and the development of novel drug molecules are central tasks that often require s...

Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks.

BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too l...

Knowledge-Based Planning for Identifying High-Risk Stereotactic Ablative Radiation Therapy Treatment Plans for Lung Tumors Larger Than 5 cm.

PURPOSE: Stereotactic ablative body radiation therapy (SABR) for lung tumors ≥5 cm can be associated with more toxicity than that for smaller tumors. ...

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been...

Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks.

OBJECTIVE:: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sen...

Development of a Ready-to-Use Graphical Tool Based on Artificial Neural Network Classification: Application for the Prediction of Late Fecal Incontinence After Prostate Cancer Radiation Therapy.

PURPOSE: This study was designed to apply artificial neural network (ANN) classification methods for the prediction of late fecal incontinence (LFI) a...

3-D Neural denoising for low-dose Coronary CT Angiography (CCTA).

CCTA has become an important tool for coronary arteries assessment in low and medium risk patients. However, it exposes the patient to significant rad...

Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach.

BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Re...

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We util...

Long-term Glomerular Filtration Rate and Kidney Disease: Improving Global Outcomes Stage Stability After Conversion to Once-Daily Tacrolimus in Kidney Transplant Recipients.

Close monitoring of estimated glomerular filtration rate (eGFR) is important for early recognition of worsening renal function to prevent further dete...

Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?

Artificial intelligence (AI) is emerging as a technology with the power to transform established industries, and with applications from automated manu...

Development of Ligand-based Big Data Deep Neural Network Models for Virtual Screening of Large Compound Libraries.

High-performance ligand-based virtual screening (VS) models have been developed using various computational methods, including the deep neural network...