Breast Cancer

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult task for head-and-neck cancer patients where detailed knowledge of the pathways of microscopic tumor spread is necessary. This paper proposes a solution to auto-segment these volumes in oropharyngeal cancer patients using a two-channel 3D U-Net archi...

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual patient tumors. Machine learning (ML) is a computational method particularly suited to the establishment of predictive models of drug response based on genomic profiles of targeted cells. We report here on the application of our previously established o...

The application of artificial intelligence in the IMRT planning process for head and neck cancer.

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI a...

Orthotopic Liver Transplantation After Stereotactic Body Radiotherapy for Pediatric Hepatocellular Carcinoma with Central Biliary Obstruction and Nodal Involvement.

Here we describe the case of a 10-year-old boy with a history of chronic hepatitis B who was diagnosed with hepatocellular carcinoma (HCC) with a larg...

Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation e...

A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning.

BACKGROUND: Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in decision-making for chemotherapy in early-stage hormon...

Dosimetric and isocentric variations due to patient setup errors in CT-based treatment planning for breast cancer by electronic portal imaging.

BACKGROUND: Inaccuracies in treatment setup during radiation therapy for breast cancers may increase risks to surrounding normal tissue toxicities, i....

Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs.

The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scint...

STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation.

Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image ...

Feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation frameworks for prostate cancer radiotherapy.

This study aimed to investigate the feasibility of anatomical feature points for the estimation of prostate locations in the Bayesian delineation fram...

Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network.

Objective: Breast Cancer is the most invasive disease and fatal disease next to lung cancer in human. Early detection of breast cancer is accomplished...

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) ...

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 conform...

Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.

Several computer aided diagnosis (CAD) systems have been developed for mammography. They are widely used in certain countries such as the U.S. where m...

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 ...

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab ...

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer.

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath...

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...

Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.

BACKGROUND: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet ...