| Zusammenfassung |
Research Context Cutaneous melanoma is curable if resected early but higher stage tumours commonly behave aggressively. Melanoma continues to increase in incidence. Currently, all diagnoses are made by histopathologists. This assessment is highly subjective and in difficult areas like melanoma diagnosis, there is wide variation in opinions between histopathologists, resulting in a large proportion requiring specialist review and 1 in 5 diagnoses being revised. Moreover, unfortunately there is often also disagreement between specialist dermatopathologists. Recently, new technologies have enabled the 'digitisation' of stained tissue slides, allowing histopathologists and researchers to view the tumour tissue at a cellular level on a computer screen. This has allowed the use of artificial intelligence (AI) algorithms to provide objective assessments of the tissue. We hypothesise that the application of these technologies may be able to provide a second histopathological "opinion" (both more objective and reproducible) to improve the accuracy of diagnosis and prognosis estimation in melanoma, with the potential to identify new prognostic biomarkers. Pilot Work Through collaboration with experts in melanoma tumour biology, digital pathology and computer vision, as well as dermatologists, mathematicians and dermatopathologists I have been able to develop and configure AI algorithms to a proof of concept sample of melanoma digital slides. We have a large pre-existing dataset of melanomas of a range of subtypes from the Leeds Melanoma Cohort (n=2184) which has comprehensive pre-existing clinical and somatic genetic data, which is ideal for training AI algorithms. Aims and Objectives The aims of the research are to: (1) develop AI algorithms for the extraction of morphological features at a cellular level to assist the histopathologist in their assessment by providing an automated, reproducible and objective second "opinion" for improved diagnostic accuracy and prognosis estimation and (2) explore the use of these morphological features to generate new prognostic biomarkers. To achieve these aims, we have identified the following objectives: 1. To derive an accurate, objective and reproducible method of analysing the melanoma phenotype through detection and sub-classification of cells within melanoma whole slide images of a range of subtypes. The data derived will be validated against manual annotation. 2. To visualise and analyse this quantitative morphometric data in 3D for improved understanding of tumour shape in relation to surrounding structures and to test physiological models of the key drivers of melanoma metastasis by comparing the data to computer-generated models of tumour growth. 3. To analyse the data with respect to specified tumour driver mutations (including BRAF/NRAS, p16, kit, NF-1) and survival (time to death). Potential Applications and Benefits We postulate that this novel research will lead to the development of the technology for use in analysing the appearance of melanocytic lesions in clinical practice to provide more accurate diagnoses/prognosis estimation and therefore prevent patients from being over or under treated. The general principles established will further inform the development of similar tools for other pathologies. Second, this technology permits the generation of objective morphological data that cannot be captured by other means, which may provide new insights into how tumours develop, organise and spread. It will also help us understand how tumour cells interact with the body's normal cells (including the surrounding skin as well as the patient's immune system) and how the underlying somatic genetic drivers impact the on the growth patterns of the tumour. Ultimately, this project aims to deliver increased understanding of the melanoma pathology to help provide more information of increased quality to clinicians for the benefit of patients. |