TY - GEN
T1 - Controlled and Uncontrolled Stochastic Norton-Simon-Massagué Tumor Growth Models
AU - Belkhatir, Zehor
AU - Pavon, Michele
AU - Mathews, James C.
AU - Pouryahya, Maryam
AU - Deasy, Joseph O.
AU - Norton, Larry
AU - Tannenbaum, Allen R.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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-Massague (NSM) tumor growth model. We first studý 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. A closed-loop control strategy that relies on model predictive control (MPC) combined with extended Kalman filter (EKF) is proposed to solve an optimal cancer therapy planning problem.
AB - 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-Massague (NSM) tumor growth model. We first studý 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. A closed-loop control strategy that relies on model predictive control (MPC) combined with extended Kalman filter (EKF) is proposed to solve an optimal cancer therapy planning problem.
UR - http://www.scopus.com/inward/record.url?scp=85082471932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082471932&partnerID=8YFLogxK
U2 - 10.1109/CDC40024.2019.9029755
DO - 10.1109/CDC40024.2019.9029755
M3 - Conference contribution
AN - SCOPUS:85082471932
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 7530
EP - 7535
BT - 2019 IEEE 58th Conference on Decision and Control, CDC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 58th IEEE Conference on Decision and Control, CDC 2019
Y2 - 11 December 2019 through 13 December 2019
ER -