Biomarkers design

An increasing amount of data from cancer patients is available through different ‘omics’ technologies (genomics, proteomics, transcriptomics, etc.), as well as medical imaging like Magnetic Resonance (MRI) or Positron Emission Tomography (PET). We integrate these data with mathematical models to identify patterns that can serve as biomarkers of prognosis and response. For instance, we develop imaging biomarkers from MRI morphological features associated to the survival of patients with brain metastases. Other examples are topological features in flow cytometry data from leukaemia patients or metabolic footprints of cancer metabolism in PET images. These features provide prognostic tools that aid in patient stratification and are a step towards personalized precision medicine.


Width of the tumor rim in T1-Gd MRI as a measure of survival in glioblastoma.

Differentiation of relapse from radiation necrosis in brain metastases from MRI longitudinal follow-up.

Topological features in flow cytometry data from patients with acute lymphoblastic leukemias.

Identification of recurrence in patients with breast and lung cancer by the location of the maximum activity point in diagnostic PET images.

Labile fraction of glycated hemoglobin as a marker for blood glucose concentration in diabetes.


There are no publications on this topic yet, but they are coming