In clinical reality, the need of quantitative tumour growth and progression predictions is pivotal for designing individualized therapies. To achieve this a plethora of examinations is conducted to assess the tumour lesion state, spanning from blood sample analysis, clinical imaging (e.g. CT, MRI), biopsy sampling, -omics screening etc. Such medical data correspond to snapshots in time of the patient’s state and in the current standard of care (SoC) their collection relies on patient’s clinical presentation. This implies that we cannot acquire many data timepoints hampering the personalized calibration of mathematical models and their corresponding prediction potential. Moreover, many clinical data types are not useful in informing phenotypic plasticity models hindering their clinical applicability. In a nutshell, the use of phenotypic plasticity models in the current cancer SoC faces the following challenges: (C1) data collection is sparse in time since it relies on patient’s clinical presentation, (C2) we lack the knowledge of the precise pathways involved in regulating phenotypic plasticity mechanisms, and (C3) medical data cannot always inform mathematical models.
Overcoming the afore-mentioned challenges to predict the future of a disease and propose an appropriate treatment (e.g., choice of a drug targeting proteins expressed in the tumour) is a formidable but not impossible task. In this talk I will present a novel methodology that combines mechanistic modelling and machine learning in delivering clinically relevant tumour growth predictions.
Speaker:
Haralampos Hatzikirou, Khalifa University, UAE
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