Cardiovascular

Effect of Long-Term Hormonal Therapy (vs Short-Term Hormonal Therapy): A Secondary Analysis of Intermediate-Risk Prostate Cancer Patients Treated on NRG Oncology RTOG 9202.

PURPOSE: NRG Oncology RTOG 9202 was a randomized trial testing long-term adjuvant androgen deprivation (LTAD) versus initial androgen deprivation only (STAD) with external beam radiation therapy (RT) in mostly high-risk and some intermediate-risk prostate cancer patients. RTOG 9408 found an overall survival (OS) advantage in patients with cT1b-T2b disease and prostate-specific antigen (PSA) <20 ng...

Mammogram Enhancement Using Intuitionistic Fuzzy Sets.

OBJECTIVE: Conventional mammogram enhancement methods use transform-domain filtering, which possibly produce some artifacts or not well highlight all local details in images. This paper presents a new enhancement method based on intuitionistic fuzzy sets.

Sentinel Lymph Node Dissection to Select Clinically Node-negative Prostate Cancer Patients for Pelvic Radiation Therapy: Effect on Biochemical Recurrence and Systemic Progression.

PURPOSE: To assess the efficacy of robotic-assisted laparoscopic sentinel lymph node (SLN) dissection (SLND) to select those patients with prostate ca...

Long-term results of adjuvant versus early salvage postprostatectomy radiation: A large single-institutional experience.

PURPOSE: The purpose of this study was to evaluate freedom from biochemical failure (FFBF), freedom from androgen deprivation therapy (FFADT), freedom...

System Integration and In Vivo Testing of a Robot for Ultrasound Guidance and Monitoring During Radiotherapy.

We are developing a cooperatively controlled robot system for image-guided radiation therapy (IGRT) in which a clinician and robot share control of a ...

Prediction of anti-cancer drug response by kernelized multi-task learning.

MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer...

Computer-assisted framework for machine-learning-based delineation of GTV regions on datasets of planning CT and PET/CT images.

We have proposed a computer-assisted framework for machine-learning-based delineation of gross tumor volumes (GTVs) following an optimum contour selec...

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning.

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The pre...

Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

BACKGROUND: The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical...

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.

OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) ...

Hypofractionated image guided radiation therapy followed by prostate seed implant boost for men with newly diagnosed intermediate and high risk adenocarcinoma of the prostate: Preliminary results of a phase 2 prospective study.

PURPOSE: A phase 2 protocol was designed and implemented to assess the toxicity and efficacy of hypofractionated image guided intensity modulated radi...

Modeling the reflection of Photosynthetically active radiation in a monodominant floodable forest in the Pantanal of Mato Grosso State using multivariate statistics and neural networks.

The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that r...

Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.

To develop a patient-specific 'big data' clinical decision tool to predict pneumonitis in stage I non-small cell lung cancer (NSCLC) patients after st...

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnos...

Receiver operating curves and dose-volume analysis of late toxicity with stereotactic body radiation therapy for prostate cancer.

PURPOSE: The purpose of this study was to evaluate a receiver operating characteristic (ROC) curve method to determine dose thresholds with late genit...

Radiation Dose Estimation by Automated Cytogenetic Biodosimetry.

The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple...

Hybrid-Actuating Macrophage-Based Microrobots for Active Cancer Therapy.

Using macrophage recruitment in tumors, we develop active, transportable, cancer theragnostic macrophage-based microrobots as vector to deliver therap...

Big Data and machine learning in radiation oncology: State of the art and future prospects.

Precision medicine relies on an increasing amount of heterogeneous data. Advances in radiation oncology, through the use of CT Scan, dosimetry and ima...

Ontology-based collection, representation and analysis of drug-associated neuropathy adverse events.

BACKGROUND: Neuropathy often occurs following drug treatment such as chemotherapy. Severe instances of neuropathy can result in cessation of life-savi...

Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automa...