  In this work, we develop a kinetic model of tumour growth taking into account
the effects of clinical uncertainties characterising the tumours' progression.
The action of therapeutic protocols trying to steer the tumours' volume towards
a target size is then investigated by means of suitable selective-type controls
acting at the level of cellular dynamics. By means of classical tools of
statistical mechanics for many-agent systems, we are able to prove that it is
possible to dampen clinical uncertainties across the scales. To take into
account the scarcity of clinical data and the possible source of error in the
image segmentation of tumours' evolution, we estimated empirical distributions
of relevant parameters that are considered to calibrate the resulting model
obtained from real cases of primary glioblastoma. Suitable numerical methods
for uncertainty quantification of the resulting kinetic equations are discussed
and, in the last part of the paper, we compare the effectiveness of the
introduced control approaches in reducing the variability in tumours' size due
to the presence of uncertain quantities.

Optimization and Control;Adaptation and Self-Organizing Systems;Biological Physics;Populations and Evolution;Quantitative Methods