Tumour heterogeneity, as measured in various imaging modalities including MRI, has been reported to useful in prognostication and in the identification of patients who will achieve a complete response to neoadjuvant chemotherapy (NAC). Heterogeneity considers microscopic image ‘texture’, by mapping and modelling grey-level distributions of individual pixels within regions of interest; textural analysis (TA). It has been hypothesised that TA indirectly links to lesion pathology via tissue structure, which influences the contrast in the final MR image.
This project seeks to build on previously published work investigating the usefulness of TA in patients with breast cancer a) in prognostication for patients undergoing primary surgical treatment and b) in improving treatment pathways for women referred for chemotherapy before surgery.
The project will include:
- Investigating the means of measuring heterogeneity with MRI, and comparing different techniques. These will include simplistic and more complex mathematical models of texture and a review of their usefulness and applicability.
- Design and building of a phantom to test various models of texture.
- Application of the techniques to a cohort of patients undergoing pre-treatment MRI for surgical planning; correlation of findings with traditional prognostic factors such as lymphovascular invasion and nodal positivity.
- Application of the technique to a cohort of patients referred for MRI monitoring during routine NAC treatment.
Assessment of lesion heterogeneity changes in different molecular subtypes of breast cancer to identify different markers of response for individual molecular subtypes. Classification models for responders and non-responders will be built and tested prospectively on a separate patient cohort.