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  Transforming neuro-pathology with deep learning (ref: SF22/HLS/APP/Schwalbe)

   Faculty of Health and Life Sciences

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  Dr Ed 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 primarily to the delivery of 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.

Tumour histology is currently optimally determined through a consensus opinion from a panel of three experienced neuropathologists. WHO guidelines recognise four histological disease variants, one of which, large-cell/anaplasia, is associated with a poor prognosis; conversely, the desmoplastic/nodular and MBEN variants are associated with favourable outcomes.

Pathologist-derived calls are subjective and are frequently discordant, since tumours are heterogenous and may display elements of >1 histological types. Moreover, in low/middle-income countries, neuro-pathologist support is frequently unavailable and consequently children may receive inappropriate treatment.

There is an urgent and currently unmet need to provide clinicians with improved tools to objectively assign histological disease subtype, suitable as a diagnostic aid.

This project will leverage a large, locally-collected medulloblastoma disease cohort (n>700) with comprehensive clinical and genome-wide annotation (i.e. RNA-seq, DNA sequencing, DNA methylation) coupled with high-resolution digital scans of histological tumour sections.

Using this cohort, the project’s major aims would be:

1)             Train deep learning classifiers from slide images to assess histological type and its spatial distribution and validation in independent test cohorts. A pilot study has shown that feasibility of this approach.

2)             To develop accessible histology classification tools suitable for use by clinicians.

3)             To go beyond current neuropathology to identify and validate novel histological features associated with survival and other molecular disease features.

Working as part of a multi-disciplinary team of scientists and clinicians, the project would suit candidates with an interest in the application of deep learning and bioinformatics to translational oncology. Candidates will receive training in state-of-the-art bioinformatic and deep learning techniques and, most importantly, develop tools that have the potential to directly improve the lives of patients.

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)

•      Appropriate IELTS score, if required

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


Please note: All applications must include a covering letter (up to 1000 words maximum) including why you are interested in this PhD, a summary of the relevant experience you can bring to this project and of your understanding of this subject area with relevant references (beyond the information already provided in the advert). Applications that do not include the advert reference (e.g. SF22/…) will not be considered.


Deadline for applications: Ongoing

Start Date: 1st October and 1st March are the standard cohort start dates each year.

Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our doctoral students. We encourage and welcome applications from all members of the community. The University hold a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Employer, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career development of researchers.

Informal enquiries to Dr. Ed Schwalbe [Email Address Removed]

Biological Sciences (4) Computer Science (8) Mathematics (25) Medicine (26)

Funding Notes

This project is fully self-funded and available to applicants worldwide. Tuition fees will depend on the running cost of the individual project, in line with University fee bands found at The fee will be discussed and agreed at interview stage.
Please note: to be classed as a Home student, candidates must meet the following criteria:
Be a UK National (meeting residency requirements), or
have settled status, or
have pre-settled status (meeting residency requirements), or
have indefinite leave to remain or enter.
If a candidate does not meet the criteria above, they would be classed as an International student.


Medulloblastoma Group3 and 4 Tumors Comprises a Clinically and Biologically Significant Expression Continuum Reflecting Human Cerebellar Development. Williamson D, Schwalbe EC, Hicks D, Aldinger KA, Lindsey JC, Crosier S, Richardson S, Goddard J, Hill RM, Castle J, Grabovska Y, Hacking J, Pizer B, Wharton SB, Jacques TS, Joshi A, Bailey S, Clifford SC. 2022. Biorxiv.
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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