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This thesis focuses on the research and development of the Hemodynamic TissueSignature (HTS) method: an unsupervised machine learning approach to describethe vascular heterogeneity of glioblastomas by means of perfusion MRI analysis.The HTS builds on the concept of habitats. An habitat is defined as asub-region of the lesion with a particular MRI profile describing a specificphysiological behavior. The HTS method delineates four habitats within theglioblastoma: the High Angiogenic Tumor (HAT) habitat, as the most perfusedregion of the enhancing tumor; the Low Angiogenic Tumor (LAT) habitat, as theregion of the enhancing tumor with a lower angiogenic profile; the potentiallyInfiltrated Peripheral Edema (IPE) habitat, as the non-enhancing regionadjacent to the tumor with elevated perfusion indexes; and the VasogenicPeripheral Edema (VPE) habitat, as the remaining edema of the lesion with thelowest perfusion profile.  The results of this thesis have been published in ten scientificcontributions, including top-ranked journals and conferences in the areas ofMedical Informatics, Statistics and Probability, Radiology & Nuclear Medicine,Machine Learning and Data Mining and Biomedical Engineering. An industrialpatent registered in Spain (ES201431289A), Europe (EP3190542A1) and EEUU(US20170287133A1) was also issued, summarizing the efforts of the thesis togenerate tangible assets besides the academic revenue obtained from researchpublications. Finally, the methods, technologies and original ideas conceivedin this thesis led to the foundation of ONCOANALYTICS CDX, a company framedinto the business model of companion diagnostics for pharmaceutical compounds,thought as a vehicle to facilitate the industrialization of the ONCOhabitatstechnology.

Unsupervised learning for vascular heterogeneity assessment of glioblastoma based on magnetic resonance imaging: The Hemodynamic Tissue Signature