Multiparametric imaging of glioblastoma tumour heterogeneity for supporting treatment decisions and accurate prognostic estimation

Multiparametric imaging of glioblastoma tumour heterogeneity for supporting treatment decisions and accurate prognostic estimation

Glioblastoma is not only the most frequent but also the most malignant tumour originating in the Central Nervous System. Due to the extremely complex and heterogeneous molecular biology of this tumour “the same treatment for all” approach does not work well in this disease, and standard of care is not always the best option. The intra-patient tumor heterogeneity is one of the responsible factors for the high aggressiveness of these lethal solid tumors and their resistance against effective therapies. In this context, additional efforts are needed for generate novel strategies able to define more tailored personalized treatments that facilitate higher safety and efficacy for relevant sub-populations. GLIOHAB project will develop an artificial intelligence based medical device able to monitor the functional state of the glioblastoma at pixel level. This software will be built on technologies previously co-developed, validated, and patented by the researcher. GLIOHAB will join best experts in the field, in a multidisciplinary and eminently clinical environment, which will provide the optimum conditions for a holistic approach to the solution. This proposal includes both the transfer of knowledge to the host institution and the training of the candidate in new advanced techniques. GLIOHAB results will contribute to monitor the response of highly aggressive tumours to complex treatments, and generate stratification strategies to select treatment responder patients.