Validation and transfer of oncological data and biomarkers based on mathematical models (AFTERbioMATH)

MOLAB has 15 years of experience in the mathematical modeling of cancer. During this time, our results have led to the development of biomarkers that identify characteristics of the disease by studying data routinely taken in the clinics. Also, the close collaboration with clinicians has led to the collection of large databases of high-quality cancer patients’ scans which may be very valuable for other research groups. This project intends to bring our research to the next level by enabling implementation of previous results into the clinical practice. We will generate software tools to help the clinician in decision-making. First, we will develop an app to predict relapse in acute lymphoblastic leukemia after first-line treatment. Second, we will implement our results regarding the differentiation of radio necrosis from relapse in brain metastases into a user-friendly program to assist medical doctors. Finally, we will release our database of MRI scans from brain metastases patients to enable further work from other research groups.

Why Mathematics?

The current process of digitalization taking place in the medical sciences has brought an explosion of available data that is yet to be fully explored. As mathematicians, these data are invaluable to us, since they provide quantitative information about the diseases that we want to model. Moreover, advanced data analysis tools guided by predictions from mathematical models, provide an innovative perspective with results that are readily transferable to the clinics for medical use. With this project we want to take to hospitals impactful outcomes from our research, as well as enable other researchers to use data from patients that we have gathered along the years.

The Team

The team is composed of MOLAB members, with multidisciplinary profiles in the area of applied mathematics, and a vast network of clinical collaborators. For the line on acute lymphoblastic leukemias we have integrated Manuel Ramírez Orellana (MD, PhD, Hospital del Niño Jesús de Madrid), José Luis Fuster Soler (MD, PhD, Hospital Virgen de la Arrixaca), Cristina Blazquez Goñi (MD, Hospital Virgen del Rocío de Sevilla) and Juan Francisco Ramírez (PhD, Hospital de Jérez). For the line on brain metastases, we rely on Estanislao Arana (MD, PhD, Valencian Institute of Oncology Foundation), Beatriz Asenjo (MD, PhD, Hospital Regional Universitario de Málaga), Carlos Velásquez (MD, PhD, Hospital Marqués de Valdecilla), Ana Ramos (MD, PhD, Hospital 12 de Octubre de Madrid), Ana Ortiz de Mendivil (MD, PhD, Hospital Universitario Sanchinarro) and Luis Pérez Romasanta (MD, PhD, Hospital General Universitario de Salamanca).


This project provides new tools for the clinicians in the decision making for two important medical problems. The identification of patients with higher chances of relapse after the treatment of acute lymphoblastic leukemias will allow the doctors to direct the efforts to those children with higher risks. Furthermore, non-invasively distinguishing between radio necrosis and relapse in patients with brain metastases after the application of radiation therapy is a very important problem whose solution can avoid unnecessary surgeries. Finally, releasing a new database of high-quality MRIs from patients with metastases will allow further radiological, mathematical and artificial intelligence research, with an important impact on the years to come.

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