Holzknecht-Vorlesung

Learning Objectives:

1) Review the importance of quantifying tumour heterogeneity in ovarian cancer
2) Describe the basis of radiomics in ovarian cancer
3) Highlight the potential added value of radiogenomics/AI in prediction of treatment response and outcome in patients with ovarian cancer

Cancer is caused by genetic (DNA) and epigenetic alterations and frequently arises as a clonal growth from a founder cell. The subclonal heterogeneity provides the basis for inter-metastatic heterogeneity which is of utmost clinical importance. New tumor sampling techniques and circulating tumor DNA methods may allow for more comprehensive evaluation of clonal composition. As both primary tumors and metastatic lesions are spatially and temporally heterogeneous they would require multiple biopsies to extract and analyze small portions of tumor tissue, which still doesn’t allow for a complete characterization of the tumor genomic landscape. Therefore, imaging has great potential for a comprehensive evaluation of the entire tumor burden in ovarian cancer as it is noninvasive and is already often repeated during treatment in routine practice, on the contrary of genomics or proteomics, which are still challenging to implement into clinical routine. While initial retrospective studies linking phenotype with genotype in ovarian cancer have shown high prognostic power they do not provide any spatial information as quantitative imaging features are generated and averaged over the entire tumor assuming that tumors are heterogeneous but well mixed. This approach ignores spatial heterogeneity readily apparent on imaging. Indeed recent genomics work has highlighted the presence of intratumor variation in gene mutation and expression. However little effort is any has been put into integrating imaging, histopathology and genomics and thus there is a clear need for well designed prospective studies focused on meaningful integration of phenotype and genotype rather than genomics in isolation.