title: On probabilistic certification of combined cancer therapies using strongly uncertain models.
abstract: This paper proposes a general framework for probabilistic certification of cancer therapies. The certification is defined in terms of two key issues which are the tumor contraction and the lower admissible bound on the circulating lymphocytes which is viewed as indicator of the patient health. The certification is viewed as the ability to guarantee with a predefined high probability the success of the therapy over a finite horizon despite of the unavoidable high uncertainties affecting the dynamic model that is used to compute the optimal scheduling of drugs injection. The certification paradigm can be viewed as a tool for tuning the treatment parameters and protocols as well as for getting a rational use of limited or expensive drugs. The proposed framework is illustrated using the specific problem of combined immunotherapy/chemotherapy of cancer.
authors: CNRS, GIPSA-lab, Control Systems Department, University of Grenoble, 11 Rue des Mathématiques, 38400 Saint Martin d׳Hères, France. Electronic address: mazen.alamir@grenoble-inp.fr.
authors: Mazen
authors: M
authors: Alamir
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D002568:Certification; D003131:Combined Modality Therapy; D006801:Humans; D007167:Immunotherapy; D008954:Models, Biological; D009369:Neoplasms / Q000473:pathology / Q000628:therapy*; D035501:Uncertainty
chemical_list: D000970:Antineoplastic Agents
keywords: Cancer therapy; Certification of therapy success; Feedback design; Randomized method; Uncertain models