Breast Cancer

Magnetic nanoparticles and nanocomposites for remote controlled therapies.

This review highlights the state-of-the-art in the application of magnetic nanoparticles (MNPs) and their composites for remote controlled therapies. Novel macro- to nano-scale systems that utilize remote controlled drug release due to actuation of MNPs by static or alternating magnetic fields and magnetic field guidance of MNPs for drug delivery applications are summarized. Recent advances in con...

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan ...

Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning.

Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We inv...

Successful radiotherapy in postoperative recurrence of a primary mediastinal yolk sac tumor: A case report.

A woman in her 60s was evaluated for anterior chest pain. Computed tomography (CT) revealed a 50 mm mass with irregular contrast enhancement in the an...

Intrawound Vancomycin Powder for Spine Tumor Surgery.

Study Design Retrospective evaluation of prospectively collected data. Objective To evaluate infection rates following intrawound vancomycin powder ap...

Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer.

BACKGROUND: Traditional cancer treatments have centered on cytotoxic drugs and general purpose chemotherapy that may not be tailored to treat specific...

Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer.

PURPOSE: Adjuvant chemotherapy (ACT) is used after surgery to prevent recurrence or metastases. However, ACT for non-small cell lung cancer (NSCLC) is...

Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts: validation of a KBRT algorithm for prostate cancer treatment planning.

BACKGROUND: A knowledge-based radiation therapy (KBRT) treatment planning algorithm was recently developed. The purpose of this work is to investigate...

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities.

PURPOSE: Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily ...

Clinical evaluation of postoperative chemotherapy based on genetic testing in patients with stage IIIA non-small cell lung cancer.

BACKGROUND: We performed a retrospective analysis to evaluate whether a postoperative chemotherapy selection method based on four tumoral gene express...

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.

PURPOSE: To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate ...

A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications.

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly af...

Proliferative activity of a blend of Echinacea angustifolia and Echinacea purpurea root extracts in human vein epithelial, HeLa, and QBC-939 cell lines, but not in Beas-2b cell lines.

Echinacea is used for its immunostimulating properties and may have a role in modulating adverse immune effects of chemotherapy (i.e., use of 5-fluoro...

HemOnc.org: A Collaborative Online Knowledge Platform for Oncology Professionals.

PURPOSE: Cancer care involves extensive knowledge about numerous chemotherapy drugs and chemotherapy regimens. This information is constantly evolving...

Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer ...

An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps.

BACKGROUND: There is a growing demand for women to be classified into different risk groups of developing breast cancer (BC). The focus of the reporte...

Three- and four-class classification models for P-glycoprotein inhibitors using counter-propagation neural networks.

P-glycoprotein (P-gp) is an ATP binding cassette (ABC) transporter that helps to protect several certain human organs from xenobiotic exposure. This e...

Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the co...

Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masse...

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammogr...