  Tumorigenesis is a complex process that is heterogeneous and affected by
numerous sources of variability. This study presents a stochastic extension of
a biologically grounded tumor growth model, referred to as the
Norton-Simon-Massagu\'e (NSM) tumor growth model. We first study the
uncontrolled version of the model where the effect of chemotherapeutic drug
agent is absent. Conditions on the model's parameters are derived to guarantee
the positivity of the tumor volume and hence the validity of the proposed
stochastic NSM model. To calibrate the proposed model we utilize a maximum
likelihood-based estimation algorithm and population mixed-effect modeling
formulation. The algorithm is tested by fitting previously published tumor
volume mice data. Then, we study the controlled version of the model which
includes the effect of chemotherapy treatment. Analysis of the influence of
adding the control drug agent into the model and how sensitive it is to the
stochastic parameters is performed both in open-loop and closed-loop viewpoints
through different numerical simulations.
