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19 July 2018 Photo Supplied
AEVGI advances Next-Generation Sequencing in Africa
Prof George Armah, Noguchi Memorial Institute for Medical Research, Ghana; Prof Carl Kirkwood, Bill and Melinda Gates Foundation, USA; Cornelius Hagenmeier, Director: Internationalisation, UFS; Prof Gert van Zyl, Dean: Health Sciences; Dr Martin Nyaga, Senior Lecturer in the NGS Unit; Prof Joyce Tsoka-Gwegweni, Vice-Dean: Health Sciences; Dr Glen Taylor, Senior Director: Research and Development; Prof Jeffrey Mphahlele, Vice-President, South African Medical Research Council.

The inaugural edition of the University of the Free State (UFS) Next-Generation Sequencing (NGS) Data and Bioinformatics Workshop, hosted by the UFS-NGS Unit in the UFS Faculty of Health Sciences, marked a new beginning for the advancement of NGS in Africa under the auspices of the African Enteric Viruses Genome Initiative (AEVGI), which was recently funded by the Bill and Melinda Gates Foundation.

The AEVGI will generate rotavirus genomes at the UFS-NGS Unit to investigate the long-term effects of the introduction of the monovalent RV1 vaccine in three African countries – Ghana, Malawi, and South Africa.

The workshop attracted over 90 participants from 15 national and international institutions, with organisations from seven different countries as well as company representatives attending the event. The workshop kicked off with a courtesy call to the Rector and Vice-Chancellor, Prof Francis Petersen, followed by a stakeholder meeting with the executive management of the UFS.

The funding was secured through an award to the principal investigator, Dr Martin Nyaga, and sub-awards to co-investigators, Dr Khuzwayo Jere, Dr Francis Dennis, and Dr Valentine Ndze. According to attendee evaluations of the workshop, the remarkable performance of the workshop instructors was outstanding. Through practical sessions, participants were equipped with knowledge on how to apply several tools of genetic data analysis, using the rotavirus genome as a model to construct and interpret different genomic datasets.

A total of 65 students attended the hands-on workshop, the majority of which were from South African higher-education institutions. The organisers are grateful to the sponsors, particularly to the Bill and Melinda Gates Foundation and the University of the Free State, for making the workshop a success. Whitehead Scientific and the South African Medical Research Council also played a major role in the success of the workshop. The local organising committee consisted of Dr Martin Nyaga (host, convener and chair), Dr Saheed Sabiu (secretary), and Mr Stephanus Riekert (principal ICT support).

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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