Magnetic resonance lymphography in gynaecological malignancies

Radiotherapy planning using MRI

The use of magnetic resonance imaging (MRI) in radiotherapy (RT) planning is rapidly expanding. We review the wide range of image contrast mechanisms available to MRI and the way they are exploited for RT planning. However a number of challenges are also considered: the requirements that MR images are acquired in the RT treatment position, that they are geometrically accurate, that effects of patient motion during the scan are minimized, that tissue markers are clearly demonstrated, that an estimate of electron density can be obtained. These issues are discussed in detail, prior to the consideration of a number of specific clinical applications. This is followed by a brief discussion on the development of real-time MRI-guided RT.

Superparamagnetic iron oxide nanoparticles for in vivo molecular and cellular imaging

In the last decade, the biomedical applications of nanoparticles (NPs) (e.g. cell tracking, biosensing, magnetic resonance imaging (MRI), targeted drug delivery, and tissue engineering) have been increasingly developed. Among the various NP types, superparamagnetic iron oxide NPs (SPIONs) have attracted considerable attention for early detection of diseases due to their specific physicochemical properties and their molecular imaging capabilities. A comprehensive review is presented on the recent advances in the development of in vitro and in vivo SPION applications for molecular imaging, along with opportunities and challenges.

A random forest classifier for lymph diseases

Machine learning-based classification techniques provide support for the decision-making process in many areas of health care, including diagnosis, prognosis, screening, etc. Feature selection (FS) is expected to improve classification performance, particularly in situations characterized by the high data dimensionality problem caused by relatively few training examples compared to a large number of measured features. In this paper, a random forest classifier (RFC) approach is proposed to diagnose lymph diseases. Focusing on feature selection, the first stage of the proposed system aims at constructing diverse feature selection algorithms such as genetic algorithm (GA), Principal Component Analysis (PCA), Relief-F, Fisher, Sequential Forward Floating Search (SFFS) and the Sequential Backward Floating Search (SBFS) for reducing the dimension of lymph diseases dataset. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the RFC for efficient classification. It was observed that GA-RFC achieved the highest classification accuracy of 92.2%. The dimension of input feature space is reduced from eighteen to six features by using GA.