At ONCOhabitats we want to help health professionals, researchers and companies to discover the true potential of artificial intelligence. Our services have been retrospectively evaluated in hospitals in Spain, Italy, Belgium and Norway in the NCT03439332 Clinical Study. Alvarez-Torres, Maria del Mar. et al. (2020) ‘Robust association between vascular habitats and patient prognosis in glioblastoma: An international multicenter study’, Journal of Magnetic Resonance Imaging, 51(5), pp. 1478–1486. Available at: https://doi.org/10.1002/jmri.26958.
ONCOhabitats has been carried out within the ALBATROSS (PID2019-104978RB-I00) and SINUE (INNEST/2022/87) projects.
Currently, ONCOhabitats is being validadated by the clinical study “Evaluación de la viabilidad de ONCOhabitats para ayudar a la planificación de la cirugía y el tratamiento en pacientes con Glioblastoma, tipo IDH-wild (INNEST/2022/87)”. This clinical validation has been funded by ALBATROSS project (PID2019-104978RB-100 – Agencia Estatal de Investigación) and SINUÉ project (INNEST/2022/87 – Agencia Valenciana de la Innovación)
Our solutions
Segmentation services
MRI preprocessing in combination with automated segmentation of enhancing tumor, necrotic core and edema based on Convolutional Neural Networks.
Biomarker quantification services
Functional assessment of biological imaging markers from perfusion and diffusion magnetic resonance sequences for the quantitative evaluation of tumor physiological features.
Vascular Heterogeneity Assessment
Delineation of functional habitats within the glioblasotma that describe the biomarker heterogeneity of the tumor. Includes all the modules of the Anatomical service
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We are the BDSLab
The Biomedical Data Science Lab (BDSLab) is an interdisciplinary research line of the ITACA institute at Universitat Politècnica de València (UPV) committed to biomedical computer science since its creation in 2000. With more than 13 years of experience, the BDSLab focuses its research on real problems in the field of biomedical data mining by means of advanced pattern recognition and machine learning techniques, computational prediction and development of tools to support health care professionals and their patients.
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