UR IMPRESSIONS of others are determined not alone by information which these others directly provide; the verbal and gestural responses of a "stimulus person" (SP) are typically assessed by a perceiver in relation to the situation in which they occur (Jones & Thibaut, 1958). In many cases, the perceiver himself is a component of this situation. He must therefore consider his own behavior as a condition affecting the other's responses and the meaning he assigns to them. If A, for example, is aware of his own role in the instigation of hostile behavior in B, he is less likely to think of B as hostile or unpleasant, than he would if he were unaware of the provocation he himself provided.It seems to follow that, in the typical social interaction, evaluations of others on the basis of their responses are contingent on the perceiver's evaluation of his own behavior. If the perceiver behaves in a "good" fashion and this elicits a "bad" response (e.g., a negative evaluation, a frown, a critical comment) from the SP, the latter will be judged as personally bad by the perceiver. If the perceiver is unconvinced that his own behavior is good, worthy, or appropriate, he will be less likely to form a negative evaluation of SP. This line of reasoning suggests an experimental situation in which one person (the SP) responds with hostility to the behavior of another (the perceiver) which is or is not highly valued by the perceiver himself.
Purpose: Determine equivalent Organ at Risk (OAR) tolerance dose (TD) constraints for MV x‐rays and particle therapy. Methods: Equivalent TD estimates for MV x‐rays are determined from an isoeffect, regression‐analysis of published and in‐house constraints for various fractionation schedules (n fractions). The analysis yields an estimate of (α/β) for an OAR. To determine equivalent particle therapy constraints, the MV x‐ray TD(n) values are divided by the RBE for DSB induction (RBEDSB) or cell survival (RBES). Estimates of (RBEDSB) are computed using the Monte Carlo Damage Simulation, and estimates of RBES are computed using the Repair‐Misrepair‐Fixation (RMF) model. A research build of the RayStation™ treatment planning system implementing the above model is used to estimate (RBEDSB) for OARs of interest in 16 proton therapy patient plans (head and neck, thorax, prostate and brain). Results: The analysis gives an (α/β) estimate of about 20 Gy for the trachea and heart and 2–4 Gy for the esophagus, spine, and brachial plexus. Extrapolation of MV x‐ray constraints (n = 1) to fast neutrons using RBEDSB = 2.7 are in excellent agreement with clinical experience (n = 10 to 20). When conventional (n > 30) x‐ray treatments are used as the reference radiation, fast neutron RBE increased to a maximum of 6. For comparison to a constant RBE of 1.1, the RayStation™ analysis gave estimates of proton RBEDSB from 1.03 to 1.33 for OARs of interest. Conclusion: The presented system of models is a convenient formalism to synthesize from multiple sources of information a set of self‐consistent plan constraints for MV x‐ray and hadron therapy treatments. Estimates of RBEDSB from the RayStation™ analysis differ substantially from 1.1 and vary among patients and treatment sites. A treatment planning system that incorporates patient and anatomy‐specific corrections in proton RBE would create opportunities to increase the therapeutic ratio. The research build of the RayStation used in the study was made available to the University of Washington free of charge. RaySearch Laboratories did not provide any monetary support for the reported studies.
Purpose: To develop a probabilistic model of outcomes of radiation therapy which includes both dosimetric and non‐dosimetric predictors, and includes a decision‐making component to quantify the balance between disease cure and radiation‐induced side‐effects. This model was implemented to assess IMRT treatment plans for individual patients for head and neck cancer. Materials and Methods: Physicians have available many resources that may not be easily reconcilable to predict patient outcomes. Dosimetric indicators, such as the EUD and NTCP are probabilistic in nature, without explicit representation of the underlying biology. Clinical trials focus on patient and disease characteristics, such as disease location, T‐stage, nodal involvement, Karnofsky performance status, and often include one treatment variable, such as DVH‐cutpoints or chemotherapy regimes. Newly recognized factors, such as HPV positivity, may affect outcome, however, without definitive clinical data, integrating such factors into clinical decision‐making is not straightforward. Finally, experience‐driven beliefs affect treatment choices and may vary between physicians. We combine all of the aforementioned resources using a Bayesian network in order to make an outcome prediction for each IMRT plan. Outcome predictions highlight the stark trade‐off between preventing recurrent disease that generally has a fatal prognosis and preventing radiation‐induced side effects that range from xerostemia to blindness to paralysis. We use a Markov Model to compute a quality‐adjusted life expectancy using patient preferences for health states. Results: Probabilities of local and distant control matched published values well, as did life expectancies. The trade‐offs between quality of life and quantity of life are explored. Sensitivity analysis highlighted physician beliefs that affected treatment choices. Conclusions: Modeling of radiation therapy has grown progressively more sophisticated. We present a method by which probabilities and expected values of clinically relevant outcomes, based on a range of variables, are calculated.
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