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• W2052740943 abstract "Purpose/Objective(s)Molecular imaging now permits monitoring of the local cellular environment during the course of treatment. Radiation and pharmacologic delivery can be adapted during treatment to improve tumor cell kill without exceeding tissue tolerances. The gain is restricted to the period after a response is observed, which may be late into treatment. A stronger approach is to revise treatment ab initio based on predicted response using population models. A barrier to their use is that treatments based on predicted tumor evolution can lead to inferior outcomes if the expected change is unrealized. The purpose of this work is to develop an approach to constructing a robust treatment plan that will exploit predicted change, but be hedged against conditions that remain fixed.Materials/MethodsA set of equations characterizing the model predictive environment were constructed. The system describes a predicted change in response to multiple agents over the course of therapy. The course is divided into two discrete periods. Tumors are probed after the first period, and the relative strengths of the administered agents changed. The goal is to maximize tumor cell kill given limits on agent intensities alone and combined that preserve normal cell populations.ResultsThe dynamic program is made robust against blocks in tumor evolution by holding the strength of the administered agents in the first period below the levels that maximize kill when treatment is fixed to initial conditions. Proliferation is suppressed by maintaining the rate of cell kill in a given period. The tumor kill given by four planning strategies were evaluated under alternative conditions. These were predictive (planning for observed change during treatment course); robust (planning for change, but allowing for no evolution); adaptive (revising treatment at the point of observed change) and fixed. In general, the system leads to a robust solution, such that tumor kill under a predictive model > robust solution > adaptive solution > fixed treatment when predictions are realized, but (robust = fixed = adaptive) kill > predictive model kill when tumor evolution is absent.ConclusionA dynamic robust model predictive system can be created to describe predicted cellular evolution. The model exploits population derived predictions, but is robust against individual cases that fail to show expected evolution. Purpose/Objective(s)Molecular imaging now permits monitoring of the local cellular environment during the course of treatment. Radiation and pharmacologic delivery can be adapted during treatment to improve tumor cell kill without exceeding tissue tolerances. The gain is restricted to the period after a response is observed, which may be late into treatment. A stronger approach is to revise treatment ab initio based on predicted response using population models. A barrier to their use is that treatments based on predicted tumor evolution can lead to inferior outcomes if the expected change is unrealized. The purpose of this work is to develop an approach to constructing a robust treatment plan that will exploit predicted change, but be hedged against conditions that remain fixed. Molecular imaging now permits monitoring of the local cellular environment during the course of treatment. Radiation and pharmacologic delivery can be adapted during treatment to improve tumor cell kill without exceeding tissue tolerances. The gain is restricted to the period after a response is observed, which may be late into treatment. A stronger approach is to revise treatment ab initio based on predicted response using population models. A barrier to their use is that treatments based on predicted tumor evolution can lead to inferior outcomes if the expected change is unrealized. The purpose of this work is to develop an approach to constructing a robust treatment plan that will exploit predicted change, but be hedged against conditions that remain fixed. Materials/MethodsA set of equations characterizing the model predictive environment were constructed. The system describes a predicted change in response to multiple agents over the course of therapy. The course is divided into two discrete periods. Tumors are probed after the first period, and the relative strengths of the administered agents changed. The goal is to maximize tumor cell kill given limits on agent intensities alone and combined that preserve normal cell populations. A set of equations characterizing the model predictive environment were constructed. The system describes a predicted change in response to multiple agents over the course of therapy. The course is divided into two discrete periods. Tumors are probed after the first period, and the relative strengths of the administered agents changed. The goal is to maximize tumor cell kill given limits on agent intensities alone and combined that preserve normal cell populations. ResultsThe dynamic program is made robust against blocks in tumor evolution by holding the strength of the administered agents in the first period below the levels that maximize kill when treatment is fixed to initial conditions. Proliferation is suppressed by maintaining the rate of cell kill in a given period. The tumor kill given by four planning strategies were evaluated under alternative conditions. These were predictive (planning for observed change during treatment course); robust (planning for change, but allowing for no evolution); adaptive (revising treatment at the point of observed change) and fixed. In general, the system leads to a robust solution, such that tumor kill under a predictive model > robust solution > adaptive solution > fixed treatment when predictions are realized, but (robust = fixed = adaptive) kill > predictive model kill when tumor evolution is absent. The dynamic program is made robust against blocks in tumor evolution by holding the strength of the administered agents in the first period below the levels that maximize kill when treatment is fixed to initial conditions. Proliferation is suppressed by maintaining the rate of cell kill in a given period. The tumor kill given by four planning strategies were evaluated under alternative conditions. These were predictive (planning for observed change during treatment course); robust (planning for change, but allowing for no evolution); adaptive (revising treatment at the point of observed change) and fixed. In general, the system leads to a robust solution, such that tumor kill under a predictive model > robust solution > adaptive solution > fixed treatment when predictions are realized, but (robust = fixed = adaptive) kill > predictive model kill when tumor evolution is absent. ConclusionA dynamic robust model predictive system can be created to describe predicted cellular evolution. The model exploits population derived predictions, but is robust against individual cases that fail to show expected evolution. A dynamic robust model predictive system can be created to describe predicted cellular evolution. The model exploits population derived predictions, but is robust against individual cases that fail to show expected evolution." @default.
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• W2052740943 date "2012-11-01" @default.
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• W2052740943 title "Generalized Approach to Planning Multimodality Therapy Adapted to Periodically Obtained Maps of Tumor Evolution Which Will be Robust to Prediction Error" @default.
• W2052740943 doi "https://doi.org/10.1016/j.ijrobp.2012.07.2013" @default.
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