A data-driven decision support system for optimizing prostate biopsy techniques
PhDs and postgraduate research
Funded PhD Project
Operations and Systems Management
4 May 2021
Successful candidates will receive a scholarship to cover tuition fees at an international rate for three years and a stipend in line with the UKRI rate (£16,062 for 2022/23). As part of the bursary, the Faculty of Business and Law may fund fieldwork expenses (currently £2,000) over the total period of PhD study. We also offer funding to attend conferences, training and a work-based placement.
The work on this project could involve:
- Benefit / Cost analysis of available and prospective prostate biopsy techniques
- Development of a multi-objective model for optimization of MRI-Guided Biopsy for prostate cancer
- Design of a data-driven clustering methodology to identify clinically similar patient subgroups
- An artificial-intelligence (AI)-based decision support system design for optimizing prostate biopsy technique
Prostate cancer (PCa) is the most common cancer in men, and its aggressiveness can significantly change from indolent to lethal. Prostate biopsy is the clinical method to diagnose PCa. The standard biopsy method involved taking 12 cores from certain locations of the prostate (systematic biopsies (SB)). However, this method is associated with missing a portion of clinically significant cancers and overdiagnosis of clinically insignificant ones.
In the last decade, Magnetic Resonance Imaging (MRI) has had substantial attention in the diagnostic workup of PCa, by identifying and locating potential cancerous areas in the prostate. Further, a new method of biopsy utilising software to fuse previously acquired MRI images with the real-time ultrasound (US) has become available. This allows clinicians to sample the suspicious areas more accurately (so called as targeted biopsies (TB)). This method can also be employed cognitively (without software), especially for larger lesions.
Although TBs have been shown to be more accurate than SBs, the optimal number of biopsy cores is yet to be determined. Typically software fusion biopsies are performed under general anaesthesia (GA), however, there is a trend towards using local anaesthesia (LA), accelerated by the COVID-19 pandemic, in an outpatient setting thereby avoiding the extra resources required for the use of an operating theatre. There are then a number of uncertainties regarding the best approach for an individual patient: TB vs SB, GA vs LA, cognitive vs software fusion, and number of cores required. This project will address the problem of optimizing prostate biopsy technique and design a data-driven decision support system that utilises artificial-intelligence techniques to generate patient-specific plans.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
We welcome applications from highly motivated prospective students with a background in Operational Research (e.g. industrial engineering, business and management, computer science, mathematics and other relevant disciplines) with an interest in multi-criteria decision making and machine learning. A familiarity with multi-objective optimisation and clustering are desirable (not essential). We encourage prospective students to design their own research strategies depending on their interest and core skills.
How to apply
We’d encourage you to contact Dr Banu Lokman (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.
Please also include a research proposal of 1,000 words outlining the main features of your proposed research design – including how it meets the stated objectives, the challenges this project may present, and how the work will build on or challenge existing research in the above field.
If you want to be considered for this funded PhD opportunity you must quote project code O&SM7040522 when applying.
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