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<!DOCTYPE html>
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<h1>RadioPathomic Integrated Artificial Intelligence System to Predict Salivary Gland Cancers</h1>
<h2><i>University of West London – School of Computing and Engineering<i></h2>
<h3><h3>
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<h1> <b> Supervisory Team </b></h1>
<h6> <a href="https://www.uwl.ac.uk/staff/Neda-Azarmehr" target="_blank" rel="noopener noreferrer"> Dr Neda Azarmehr </a></h6>
<h6> <a href="https://www.uwl.ac.uk/staff/massoud-zolgharni" target="_blank" rel="noopener noreferrer"> Professor Massoud Zolgharni </a></h6>
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<h1>RadioPathomic Integrated Artificial Intelligence System to Predict Salivary Gland Cancers</h1>
<h3> Salivary gland cancers (SGC) are categorised as head and neck cancers and account for approximately 0.5% of all malignant tumours; but present complex malignancies with more than 35 subtypes and there is a significant overlap in features between 15 benign SGT and 20 malignant SGCs. This complexity presents significant diagnostic and therapeutic challenges.</h3>
<br>
<h3> Radiological investigation such as MRI is a standard of care in the diagnosis and treatment planning of SGTs. MRI scans can help determine the exact location and extent of a tumour (for example, if it is growing into nearby tissues). However, radiological interpretation is subjective, and it is not enough to accurately predict the type of behaviour of cancer on radiology alone. Histopathological examination supplements the radiological information through analysis of several pathological features including morphology, grade. </h3>
<br>
<h3> Artificial intelligence (AI) algorithms are being used to facilitate cancer diagnosis and prediction by improving efficiency and providing quantitative outputs for treatment decisions. However, the application of AI algorithms regarding SGC has been limited. This is essential as SGC are rare (compared to the more common cancers), and under-investigated research area with poor existing knowledge of the mechanisms of disease progression. </h3>
<br>
<h3>This PhD proposal aim to develop a novel, automated RadioPathomic integrated Artificial Intelligence system to provide identification, subtyping of Salivary Gland Cancers and predict prognosis.<h3>
<h3>For more information about the project, please contact <a href="https://www.uwl.ac.uk/staff/Neda-Azarmehr" target="_blank" rel="noopener noreferrer">Dr Neda Azarmehr </a> </h3>
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