Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images.

AIM: To investigate the effect of image processing on cancer detection in mammography. METHODS AND MATERIALS: An observer study was performed using 349 digital mammography images of women with normal breasts, calcification clusters, or soft-tissue lesions including 191 subtle cancers. Images underwent two types of processing: FlavourA (standard) and FlavourB (added enhancement). Six observers located features in the breast they suspected to be cancerous (4,188 observations). Data were analysed using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis. Characteristics of the cancers detected with each image processing type were investigated. RESULTS: For calcifications, the JAFROC figure of merit (FOM) was equal to 0.86 for both types of image processing. For soft-tissue lesions, the JAFROC FOM were better for FlavourA (0.81) than FlavourB (0.78); this difference was significant (p=0.001). Using FlavourA a greater number of cancers of all grades and sizes were detected than with FlavourB. FlavourA improved soft-tissue lesion detection in denser breasts (p=0.04 when volumetric density was over 7.5%) CONCLUSIONS: The detection of malignant soft-tissue lesions (which were primarily invasive) was significantly better with FlavourA than FlavourB image processing. This is despite FlavourB having a higher contrast appearance often preferred by radiologists. It is important that clinical choice of image processing is based on objective measures.

MEDXVIEWER: PROVIDING A WEB-ENABLED WORKSTATION ENVIRONMENT FOR COLLABORATIVE AND REMOTE MEDICAL IMAGING VIEWING, PERCEPTION STUDIES AND READER TRAINING.

MedXViewer (Medical eXtensible Viewer) has been developed to address the need for workstation-independent, picture archiving and communication system (PACS)-less viewing and interaction with anonymised medical images. The aim of this paper is to describe the design and features of MedXViewer as well as to introduce the new features available in the latest release (version 1.2). MedXViewer currently supports digital mammography and tomosynthesis. The flexible software design used to develop MedXViewer allows it to be easily extended to support other imaging modalities. Regions of interest can be drawn by a user, and any associated information about a mark, an image or a study can be added. The questions and settings can be easily configured depending on the need of the research allowing both ROC and FROC studies to be performed. Complex tree-like questions can be asked where a given answer presents the user to new questions. The hanging protocol can be specified for each study. Panning, windowing, zooming and moving through slices are all available while modality-specific features can be easily enabled, e.g. quadrant zooming in digital mammography and tomosynthesis studies. MedXViewer can integrate with a web-based image database OPTIMAM Medical Image Database allowing results and images to be stored centrally. The software can, alternatively, run without a network connection where the images and results can be encrypted and stored locally on a machine or external drive. MedXViewer has been used for running remote paper-less observer studies and is capable of providing a training infrastructure and coordinating remote collaborative viewing sessions.

A Pilot Study on the Development of Remote Quality Control of Digital Mammography Systems in the NHS Breast Screening Programme.

In the UK, physicists and radiographers perform routine quality control (QC) of digital mammography equipment at daily, weekly and monthly intervals. The tests performed and tolerances are specified by standard protocols. The manual nature of many of the tests introduces variability due to the positioning of regions of interest (ROIs) and can be time consuming. The tools on workstations provided by manufacturers limit the range of analysis that radiographers can perform and do not allow for a standard set of tools and analysis because they are specific to a given manufacturer. Automated software provides a means of reducing the variability in the analysis and also provides the possibility of additional, more complex analysis than is currently performed on the daily, weekly and monthly checks by radiographers. To this end, a set of tools has been developed to analyse the routine images taken by radiographers. As well as automatically reproducing the usual measurements by radiographers more complex analysis is provided. A QC image collection system has been developed which automatically routes QC data from a clinical site to a centralised server for analysis. A Web-based interface has been created that allows the users to view the performance of the mammographic equipment. The pilot system obtained over 3000 QC images from seven X-ray units at a single screening centre over 2 years. The results show that these tools and methods of analysis can highlight changes in a detector over time that may otherwise go unnoticed with the conventional analysis.

SiPep: a system for the prediction of tissue-specific minor histocompatibility antigens.

Approximately 50 years ago it was found that inbred strains of mice were able to reject tumours and skin grafts from major histocompatibility complex (MHC) identical donors. They proposed that additional transplantation antigens must exist outside the MHC. These were described as minor histocompatibility antigens (mHAgs). Since then, related studies in humans have identified 16 human mHAgs. The aim of this work is to increase the number of known mHAgs by prediction of candidate minor histocompatibility loci by identifying coding single nucleotide polymorphisms (SNPs) where the amino acid variation lies within an MHC-binding peptide and alters the ability of that peptide to bind. We have developed an algorithm called SiPep which uses peptide sequences derived from the flanking regions of known non-synonymous SNPs, various MHC-binding and proteolytic cleavage evaluation methods and protein expression data to predict mHAgs. We have processed 45094 SNPs using the SiPep algorithm and have stored the results in a database called SNPBinder. The facilities to process submitted proteins through the SiPep algorithm as well as the SNPBinder database are available to the public. A set of peptides that are predicted as possible mHAgs by the SiPep algorithm have been tested using refolding assays and gel filtration and the results are presented in this paper. The SiPep tools and SNPBinder database are available free of charge via the internet. An HTML interface providing search facilities can be found at the following address: http://www.sipep.org/.