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Meningioma classification using machine-learning approaches

Project Description

Meningiomas are the most common intracranial tumours comprising around the 36% of all the primary central nervous system tumours. Originally, they are located in one of the three protective layers of the brain and spinal cord, the arachnoid cap cells, known as the meninges. Strictly based on histological and cytomorphological criteria, the World Health Organization (WHO) classified Meningiomas into three grades. The majority of Meningiomas are classified as grade I (80% approximately) and they are characterized as benign. Grade II Meningiomas are characterised as atypical and grade III as anaplastic, covering the 15-20% and 1-2%, respectively.

In the era of big data, with the advent of technologies such as microarrays and Next Generation Sequencing (NGS) we are able to produce massive amount of data in small time and low costs. The resent invasion of Artificial Intelligence (AI) technologies, such as deep learning in biology, promises new ways of addressing fundamental biological problems such as the identification and the prediction of mutations across human’s genome and more accurate classification of dysfunctional genes, correlating them with clinical symptoms of specific diseases.

By taking advantage of the massive amount of publicly available and in house “omics” data and machine-learning approaches, the project aims to build a model based on neural networks architectures that will be able to: a) provide accurate classification of the grades and the subgrades within Meningiomas and b) predict Meningioma recurrence after surgery resection.

Applicants should use the links provided in each topic or project area to the Research Centres and Research Groups identified, or to the named supervisors for each project, to ensure that their application and proposal fits with the research interests and topics defined in the studentships offered.

For academic enquiries, please contact .

For administrative enquiries about your application, contact . Post application, please contact .

Funding Notes

The studentship will cover tuition fees and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applications are welcome from strong UK, EU and International students.

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