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Digital Pathology – transforming paediatric brain tumour classification through machine learning (Ref: SF20/APP/SCHWALBE)

Faculty of Health and Life Sciences

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Dr E Schwalbe Applications accepted all year round Self-Funded PhD Students Only

About the Project

Medulloblastoma is the most common central nervous system tumour of childhood. Although advances in treatment have raised survival to ~75%, there remains significant numbers of patients who die of their disease. Survivors have to contend with long term, life-limiting side-effects of their treatments, due to radiotherapy to the developing brain. Through stratification determined by histology and molecular disease features, patients are given risk-adapted treatments aimed at ensuring a cure and maximising quality-of-life after survival.
Histology is currently determined through a consensus opinion from a panel of three experienced neuropathologists. Current WHO guidelines recognise large-cell/anaplastic variants, associated with a poor prognosis and desmoplastic/nodular disease, associated with favourable outcomes. Histological subtype can affect treatment and is subjective (3 pathologists are used to ensure a majority decision). There is also potential for classification to be further improved by integration of genome-wide molecular data. There is a currently unmet need to provide neuropathologists with improved tools which objectively assign histological disease subtype.
The project would utilise a large, clinically well-annotated cohort of medulloblastomas, for which histological data (high-resolution slide scans) and molecular data (methylation array, RNA-seq, targeted sequencing data, from bulk tumour and single-cell approaches), are available.
Using this cohort, the project’s major aims would be:
• Train classifiers to identify histological subtype using AI from slide images
• The assessment of classifiers in independent validation cohorts
• The creation and assessment of classifiers using additional integration of molecular data
• To make classification tools suitable for use in the classification of histology in low to middle income countries, which lack access to suitable neuropathologists
The project would therefore suit candidates with an interest in the application of machine learning and bioinformatics to translational oncology. Candidates would receive training in state-of-the-art bioinformatic and AI techniques that would equip interested applicants for careers in analysis of omics data.

Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications should include a covering letter that includes a short summary (500 words max.) of a relevant piece of research that you have previously completed and the reasons you consider yourself suited to the project. Applications that do not include the advert reference (e.g. SF20/…) will not be considered.

Deadline for applications: 1st July for October start, or 1st December for March start
Start Date: October or March
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

For enquiries, contact Dr Ed Schwalbe ([Email Address Removed])

Funding Notes

Please note, this is a self-funded project and does not include tuition fees or stipend; the studentship is available to Students Worldwide. Fee bands are available at . A relevant fee band will be discussed at interview based on project running costs


Second-generation molecular subgrouping of medulloblastoma: an international meta-analysis of Group 3 and Group 4 subtypes. Sharma T*, Schwalbe EC*, Williamson D, Sill M, Hovestadt V, Mynarek M, Rutkowski S, Robinson GW, Gajjar A, Cavalli F, Ramaswamy V, Taylor MD, Lindsey JC, Hill RM, Jäger N, Korshunov A, Hicks D, Bailey S, Kool M, Chavez L, Northcott PA, Pfister SM, Clifford SC. 2019. Acta Neuropathologica. 138:309-326.
Prognostic impact of whole chromosomal aberration signatures in standard-risk medulloblastoma: a retrospective molecular analysis of the HIT-SIOP-PNET4 clinical trial. Goschzik T*, Schwalbe EC*, Hicks D, Smith A, zur Muehlen A, Figarella-Branger D, Doz F, Rutkowski S, Lannering B, Pietsch T, Clifford SC. 2018. Lancet Oncology. 19(12):1602-16
Minimal methylation classifier (MIMIC): A novel method for derivation and rapid diagnostic detection of disease-associated DNA methylation signatures. Schwalbe, EC, Hicks D, Rafiee G, Bashton M, Gohlke H, Enshaei A, Potluri S, Matthiesen J, Mather M, Taleongpong P, Chaston R, Scott K, Silmon A, Curtis A, Lindsey JC, Crosier S, Smith AJ, Goschzik T, Doz F, Rutkowski S, Lannering B, Pietsch T, Bailey S, Williamson D, Clifford SC. 2017. Scientific Reports. 7(1):13421
Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Schwalbe EC, Lindsey JC, Nakjang S, Crosier S, Smith AJ, Hicks D, Rafiee G, Hill RM, Iliasova A, Stone T, Pizer B, Michalski A, Joshi A, Wharton SB, Jacques TJ, Bailey S, Williamson D, Clifford SC. 2017. Lancet Oncology.18(7):958-971

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