2,314 research outputs found
On Probabilistic Certification of Combined Cancer Therapies Using Strongly Uncertain Models
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.Comment: Submitted to Journal of theoretical Biolog
Chemotherapy drug regimen optimization using deterministic oscillatory search algorithm
Purpose: To schedule chemotherapy drug delivery using Deterministic Oscillatory Search algorithm, keeping the toxicity level within permissible limits and reducing the number of tumor cells within a predefined time period.Methods: A novel metaheuristic algorithm, deterministic oscillatory search, has been used to optimize the Gompertzian model of the drug regimen problem. The model is tested with fixed (fixed interval variable dose, FIVD) and variable (variable interval variable dose, VIVD) interval schemes and the dosage presented for 52 weeks. In the fixed interval, the treatment plan is fixed in such a way that doses are given on the first two days of every seven weeks such as day 7, day 14, etc.Results: On comparing the two schemes, FIVD provided a higher reduction in the number of tumor cells by 98 % compared to 87 % by VIVD after the treatment period. Also, a significant reduction in the number was obtained half way through the regimen. The dose level and toxicity are also reduced in the FIVD scheme. The value of drug concentration is more in FIVD scheme (50) compared to VIVD (41); however, it is well within the acceptable limits of concentration. The results proved the effectiveness of the proposed technique in terms of reduced drug concentration, toxicity, tumor size and drug level within a predetermined time period.Conclusion: Artificial intelligent techniques can be used as a tool to aid oncologists in the effective treatment of cancer through chemotherapy.Keywords: Deterministic Oscillatory Search, Chemotherapy scheduling, Drug schedule, Artificial intelligenc
Chemotherapy planning and multi-appointment scheduling: formulations, heuristics and bounds
The number of new cancer cases is expected to increase by about 50% in the
next 20 years, and the need for chemotherapy treatments will increase
accordingly. Chemotherapy treatments are usually performed in outpatient cancer
centers where patients affected by different types of tumors are treated. The
treatment delivery must be carefully planned to optimize the use of limited
resources, such as drugs, medical and nursing staff, consultation and exam
rooms, and chairs and beds for the drug infusion. Planning and scheduling
chemotherapy treatments involve different problems at different decision
levels. In this work, we focus on the patient chemotherapy multi-appointment
planning and scheduling problem at an operational level, namely the problem of
determining the day and starting time of the oncologist visit and drug infusion
for a set of patients to be scheduled along a short-term planning horizon. We
use a per-pathology paradigm, where the days of the week in which patients can
be treated, depending on their pathology, are known. We consider different
metrics and formulate the problem as a multi-objective optimization problem
tackled by sequentially solving three problems in a lexicographic
multi-objective fashion. The ultimate aim is to minimize the patient's
discomfort. The problems turn out to be computationally challenging, thus we
propose bounds and ad-hoc approaches, exploiting alternative problem
formulations, decomposition, and -opt search. The approaches are tested on
real data from an Italian outpatient cancer center and outperform
state-of-the-art solvers.Comment: 28 pages, 3 figure
Modeling Three-dimensional Invasive Solid Tumor Growth in Heterogeneous Microenvironment under Chemotherapy
A systematic understanding of the evolution and growth dynamics of invasive
solid tumors in response to different chemotherapy strategies is crucial for
the development of individually optimized oncotherapy. Here, we develop a
hybrid three-dimensional (3D) computational model that integrates
pharmacokinetic model, continuum diffusion-reaction model and discrete cell
automaton model to investigate 3D invasive solid tumor growth in heterogeneous
microenvironment under chemotherapy. Specifically, we consider the effects of
heterogeneous environment on drug diffusion, tumor growth, invasion and the
drug-tumor interaction on individual cell level. We employ the hybrid model to
investigate the evolution and growth dynamics of avascular invasive solid
tumors under different chemotherapy strategies. Our simulations reproduce the
well-established observation that constant dosing is generally more effective
in suppressing primary tumor growth than periodic dosing, due to the resulting
continuous high drug concentration. In highly heterogeneous microenvironment,
the malignancy of the tumor is significantly enhanced, leading to inefficiency
of chemotherapies. The effects of geometrically-confined microenvironment and
non-uniform drug dosing are also investigated. Our computational model, when
supplemented with sufficient clinical data, could eventually lead to the
development of efficient in silico tools for prognosis and treatment strategy
optimization.Comment: 41 pages, 8 figure
Stochastic modelling of eukaryotic cell cycle
Stochastic models are developed to capture the inherent stochasticity of the biochemical networks associated to many biological processes. The objective of the present thesis is to present a detailed picture of stochastic approach for the mathematical modeling of eukaryotic cell cycle, to demonstrate an important application of such model in chemotherapy and to present a methodology for selecting the model parameters. The stochastic cell cycle model, developed using stochastic chemical kinetics approach, leads to the formation of an infinite dimensional differential equation in probabilities of system being in a specific state. Using Monte Carlo simulations of this model, dynamics of populations of eukaryotic cells such as yeasts or mammalian cells are obtained. Simulations are stochastic in nature and therefore exhibit variability among cells that is similar to the variability observed in natural populations. The model’s capability to predict heterogeneities in cell populations is used as a basis to implement it in a chemotherapic modeling framework to demonstrate how the model can be used to assist in the drug development stage by investigating drug administration strategies that can have different killing effect on cancer cells and healthy cells. Finally, basic cell cycle model is refined in a systematic way to make it more suitable for describing the population characteristics of budding yeast. Selection of model parameters using an evolutionary optimization strategy referred to as insilico evolution is described. The benefits of this approach lie in the fact that it generates an initial guess of reasonable set of parameters which in turn can be used in the least squares fitting of model to the steady state distributions obtained from flow cytometry measurements. The Insilco evolution algorithm serves as a tool for sensitivity analysis of the model parameters and leads to a synergistic approach of model and experiments guiding each other. To conclude, the stochastic model based on single cell kinetics will be useful for predicting the population distribution on whole organism level. Such models find applications in wide areas of biological and biomedical applications. Evolutionary optimization strategies can be used in parameter estimation methods based on steady state distributions
A stochastic programming approach for chemotherapy appointment scheduling
Chemotherapy appointment scheduling is a challenging problem due to the
uncertainty in pre-medication and infusion durations. In this paper, we
formulate a two-stage stochastic mixed integer programming model for the
chemotherapy appointment scheduling problem under limited availability and
number of nurses and infusion chairs. The objective is to minimize the expected
weighted sum of nurse overtime, chair idle time, and patient waiting time. The
computational burden to solve real-life instances of this problem to optimality
is significantly high, even in the deterministic case. To overcome this burden,
we incorporate valid bounds and symmetry breaking constraints. Progressive
hedging algorithm is implemented in order to solve the improved formulation
heuristically. We enhance the algorithm through a penalty update method, cycle
detection and variable fixing mechanisms, and a linear approximation of the
objective function. Using numerical experiments based on real data from a major
oncology hospital, we compare our solution approach with several scheduling
heuristics from the relevant literature, generate managerial insights related
to the impact of the number of nurses and chairs on appointment schedules, and
estimate the value of stochastic solution to assess the significance of
considering uncertainty
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