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Validation of artificial intelligence prediction models in cancer diagnostics


   Barts and The London School of Medicine and Dentistry

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  Dr Judith Offman, Dr Oleg Blyuss  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Applications are invited from graduates with a BSc (First or Upper Second) or MSc (Merit or Distinction), or equivalent, to work within the Wolfson Institute of Population Health (https://www.qmul.ac.uk/wiph/). This 4-year studentship will commence in September 2022 (or January 2023 if preferred) and will be based at the Charterhouse Square Campus. This is an exciting opportunity for a graduate from disciplines related to epidemiology, statistics, IT sciences and healthcare use.

Project description

Background

Around 375,000 new cancers are diagnosed in the UK every year (2016-18) and only about 50% of cancer patients survive for ten years or more1. Diagnosing cancer earlier, when it is easier to treat effectively and with fewer side effects, can improve patient survival and quality of life. Artificial intelligence has the potential to revolutionise cancer diagnosis by allowing earlier detection of cancers from medical images, reducing workforce bottlenecks, providing diagnostic support and using multi-modality approaches, including big data to identify patients at highest risk of developing cancer. New machine learning models for cancer diagnostics are being developed and published at an increasing rate. However, hardly any of these models have been evaluated extensively enough to demonstrate real-world medical utility. Furthermore, where external evaluation has been carried out, these studies have been at high risk of bias and of low generalisability2-3.

Aims and Objectives

This PhD studentship is aimed at understanding how to best validate new machine learning models in cancer diagnostics following the initial developmental phase up to first implementation in a clinical setting.

As part of this PhD you will:

1)    Review of published studies on artificial intelligence model validation in cancer early diagnosis with specific focus on biases (either focus on specific area or more general)

2)    Design and carry out an early-stage retrospective validation study of a machine learning model for medical images.

3)    Design and carry out a pilot study of a prospective trial of a machine learning model for clinical data, for example in primary care (GP surgeries).

You will furthermore contribute to the creation of a framework for the validation of machine learning models in computational pathology as part of a multidisciplinary team.

Due to the fast-moving nature of the field, the two machine learning algorithms at different stages of validation for these studies will be identified as part of existing collaborations with artificial intelligence experts Dr Oleg Blyuss and Dr Jan Lukas Robertus (PhD co-supervisors).

You will be supervised by an interdisciplinary supervisory team and will work closely with experts in epidemiology, medical statistics, machine learning, pathology and primary care. You will be based at the Centre for Prevention, Detection and Diagnosis, which is at the forefront of research internationally into the prevention, detection and control of cancer. We have particularly active programmes in research studying breast, cervical, colorectal, lung, pancreatic and prostate cancer prevention, cancer screening, awareness and early diagnosis, and statistical methods for clinical trials and epidemiology.

Informal enquiries can be made via email to:

Dr Judith Offman ([Email Address Removed])

Dr Oleg Blyuss ([Email Address Removed])

How to apply

Your application should consist of a CV and contact details of two academic referees. You must also include a personal statement (1,000 words maximum) describing your suitability for the selected project including how your research experience and interests relate to the project.

Please submit your application to: Patrick Mullan ([Email Address Removed]).

Successfully shortlisted candidates will be invited to an interview.


Funding Notes

This 4-year PhD studentship is funded by Barts Charity and comes with a tax-free stipend of £24,278. It is open to UK Nationals, and EU nationals that have EU Settlement Status and have been ordinarily resident in the UK or EEA for three years. University tuition fees (at Home rate) will be met by the funding body.

References

1. Cancer Research UK: Cancer Statistics for the UK [Accessed on 06.06.2022 2022].
2. Anderson AW, Marinovich ML, Houssami N, Lowry KP, Elmore JG, Buist DSM, Hofvind S, Lee CI: Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review. Journal of the American College of Radiology 2022, 19(2):259-273.
3. Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, Taylor-Phillips S: Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ 2021, 374:n1872.

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