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Therapy optimization
in gliomas
(TOG)

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Biomarkers and mathematical
models for prognosis and prediction in
brain metastasis (METMATH)

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Labile hemoglobin as a diagnostic
and monitoring biomarker in diabetes:
A mathematical approach (DIAMATH)

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Mathematical-based pretreatment
imaging biomarkers for
glioblastoma (GLIOMAT)

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Mathematical models for the
prediction of resistances and treatment
optimization in pediatric lymphoblastic leukemias (LLAMAT)

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Scaling laws and fractal measures
on high-resolution tumor images
(SCALFRACT)

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Metabolic Scaling
Laws in Cancer
(METLAWS)

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Extended ADAG formula by
incorporating the role of glycation
at the bone marrow (GLYBMATH)

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In silico therapy
optimization

Clinical trials are expensive and take years to complete. We use mathematical modeling and computer simulations to carry out virtual clinical trials that identify optimum ways to apply a treatment.

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Biomarkers
design

Different types of explorations provide a plethora of data from cancer patients. Mathematical models help make sense of those data and identify patterns associated with the prognosis.

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Immunity and
Immunotherapies

Cancer immunotherapies use the patient’s immune system against tumor cells. Mathematical models help to understand the actions of immunotherapies and to design more effective therapeutic approaches.

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Mathematical models for the digital transition in neuro-oncology: In-silico design of a clinical trial for glioblastoma (ISGBM)

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Optimization of CAR T cells treatments of hematological malignancies: An integrated human data-based approach using mathematical models

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Mathematics against fibrous dysplasia: Biomarker discovery and treatment optimization

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Sponsors

© Mathematical Oncology Laboratory – MOLAB
Universidad de Castilla-La Mancha

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