Sponsors

• William Hsu

New developments in AI for Integrating imaging and genomic data

• Olivier Gevaert, Stanford University, United States

Quantitative imaging & radiomics

• Philippe Lambin

• Evis Sala

Cancer is caused by genetic (DNA) and epigenetic alterations and frequently arises as a clonal growth from a founder cell. The sub clonal heterogeneity provides the basis for inter-metastatic heterogeneity which is of utmost clinical importance. New tumour sampling techniques and circulating tumour DNA methods may allow for more comprehensive evaluation of clonal composition. As both primary tumours and metastatic lesions are spatially and temporally heterogeneous they would require multiple biopsies to extract and analyse small portions of tumour tissue, which still doesn’t allow for a complete characterization of the tumour genomic landscape. Therefore, imaging has great potential for a comprehensive evaluation of the entire tumour burden in ovarian cancer as it is non-invasive 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 tumour assuming that tumours are heterogeneous but well mixed. This approach ignores spatial heterogeneity readily apparent on imaging. Indeed, recent genomics work has highlighted the presence of intra-tumour 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.

• Arvind Rao

• Faisal Mahmood

• Lee Cooper

• Anant Madabhushi

• all speakers

panel Q&A