Contrast-agent free functional lung image-guided therapy: development, validation and application
Lung cancer is the leading cause of cancer deaths and most common cancer worldwide. Although surgery remains the standard of care for early stage lung cancer, the majority of patients suffer impaired lung function due to other, co-existing, pulmonary co-morbidities and thus are inoperable. In this patient group, radiotherapy plays a major treatment role but is limited by the risk of radiation-induced lung injury.
Poor pre-existing lung function is one of the main factors that increases surgical risk. In current clinical practice, lung function assessment often constitutes simple whole-lung airflow measures; thus, more precise measures to aid patient selection are needed. More sophisticated tests, such as multiple breath inert gas washout, do have functional sensitivity to gas exchange and ventilation heterogeneity. Nevertheless, these tests are still whole-lung tests whilst lung diseases are anatomically regional.
Regional lung function has traditionally been assessed by nuclear imaging via radionuclide scintigraphy or single photon emission computed tomography (SPECT) despite poor accuracy, spatial and temporal resolution. Hyperpolarised gas MRI has been proposed as a superior alternative to nuclear imaging techniques and provides images with exquisite spatial and temporal resolution. However, the modality requires a contrast agent, specialised equipment and multi-nuclear scanners inaccessible to most clinical centres. Accordingly, the ability to visualise and quantify regional lung function from routine CT and proton MR images without added contrast is highly desirable.
We hypothesise that:
1) surrogates of regional lung function derived from non-contrast CT and proton MRI data can provide information comparable to direct measures of lung function from established techniques, including contrast-based functional lung imaging modalities such as hyperpolarised gas MRI ventilation and pulmonary function tests.
2) contrast agent free surrogates of regional functional lung can facilitate personalised treatment planning and improved evaluation of treatment response for lung cancer patients undergoing therapeutic interventions such as radiotherapy or surgery.
The primary aim of this PhD studentship is to develop, validate and clinically evaluate novel contrast agent free techniques of imaging lung function for image-guided lung cancer therapy. The scientific objectives are to:
1. develop methods that enable visualisation and quantification of lung function from non-contrast structural CT and proton MR images.
2. validate these methods against established lung function measures from hyperpolarised gas MRI, dynamic contrast enhanced perfusion MRI, spirometry and multi-breath washout.
Once developed, these techniques will be clinically evaluated by analysing several completed and ongoing lung cancer studies. Specifically, the utility of these techniques will be assessed for improved:
1. treatment planning
2. treatment response evaluation
The PhD student will complement a multidisciplinary world-leading team of physicists, engineers, clinicians and physiologists interested in functional and structural assessment of the lungs with advanced imaging techniques.
The Faculty Scholarships for Medicine, Dentistry & Health are 3.5 years in duration and cover fees and stipend at Home/EU level. Overseas students may apply but will need to fund the fee differential between Home and Overseas rate from another source.
Candidates must have a first or upper second class honors degree or significant research experience. A BSc in either Computer Science, Engineering, Physics or Mathematics. strong programming skills in MATLAB, Python, R or Java/C++, prior experience of image processing is desirable.
Interested candidates should in the first instance contact Bilal Tahir, [email protected]
How to apply:
Please complete a University Postgraduate Research Application form available here: www.shef.ac.uk/postgraduate/research/apply
Please clearly state the prospective main supervisor in the respective box and select Oncology and Metabolism as the department.