Objective evaluation of 3-d radiation treatment plans: A decision-analytic tool incorporating treatment preferences of radiation oncologists
Identifieur interne : 00D992 ( Main/Merge ); précédent : 00D991; suivant : 00D993Objective evaluation of 3-d radiation treatment plans: A decision-analytic tool incorporating treatment preferences of radiation oncologists
Auteurs : Nilesh L. Jain [États-Unis] ; Michael G. Kahn [États-Unis] ; Robert E. Drzymala [États-Unis] ; Bahman E. Emami [États-Unis] ; James A. Purdy [États-Unis]Source :
- International Journal of Radiation Oncology, Biology, Physics [ 0360-3016 ] ; 1993.
English descriptors
- Teeft :
- Annealing, Biol, Bladder, Clinical decision analysis, Clinical endpoint, Clinical endpoints, Clinical features, Clinical issues, Clinical relevance, Clinician, Clinician name, Complete model, Complication, Complication probabilities, Complication probability, Comput, Conformal, Connective tissue, Critical structure, Decision analysis, Dos, Dose distribution, Dose distributions, Endpoint, Femoral, Femur, Generic model, Interactive tool, Intermediate values, Inverse problem, Jain, Linear programming, Mallinckrodt institute, Methodology, National cancer institute, Normal probability, Normal tissue, Normal tissues, Ntcp, Ntcp models, Ntcp values, Objective evaluation, Objective functions, Oncol, Oncologist, Oncology, Optimal treatment plan, Optimization, Optimized, Other issues, Overall utility, Photon, Photon treatment planning, Phys, Preference, Prostate, Prototypical, Prototypical weight, Prototypical weights, Purdy, Qualitative evaluation model, Radiat, Radiation oncologist, Radiation oncologists, Radiation oncology, Radiation therapy, Radiation therapy treatment planning, Radiation treatment, Radiation treatment planning, Radiation treatment plans, Radiology, Radiotherapy, Radiotherapy treatment planning, Ranking, Rectum, Same patient, Same performance, Schultheiss, Symbolic labels, Target volume, Target volumes, Threshold doses, Trad, Treatment failure, Treatment field, Treatment plan, Treatment planner, Treatment planning, Treatment plans, Treatment preferences, Tumor site, Utility thresholds, Weighti, Worksheet.
Abstract
Abstract: Purpose: Selecting the optimal radiation treatment plan from a set of competing plans involves making trade-offs among the doses delivered to the target volumes and normal tissues by the competing plans. Evaluation of 3-dimensional radiation treatment plans is difficult because it requires the review of vast amount of graphical and numerical data. We have developed an objective plan-ranking model based on the concepts of decision analysis.Methods and Materials: Our model ranks a set of tentative radiation treatment plans from best to worst. A figure of merit is computed for each plan based on probabilities of possible clinical complications such as non-eradication of the tumor and radiation induced damage to the nearby healthy normal tissues, and weights which indicate their clinical relevance. This figure of merit is used to rank the plans. Key issues addressed by the model include the incorporation of individual treatment preferences of the radiation oncologist and clinical features of the patient.Results: A methodology has been established for eliciting the treatment preferences of radiation oncologists. Results of this elicitation, and examples of several plan evaluations are presented. An interactive computer-based tool has been developed as one of a set of tools to assist in the evaluation of 3-dimensional radiation treatment plans.Conclusion: The paper presents a decision-analytic model incorporating radiation oncologists' treatment preferences and an interactive computer-based tool for objectively ranking competing radiation treatment plans. The tool can be used by radiation oncologists for the evaluation of competing plans, or as part of a system which tries to automatically generate optimal treatment plans using mathematical or symbolic techniques.
Url:
DOI: 10.1016/0360-3016(93)90213-F
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Drzymala</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis MO 63110</wicri:regionArea><orgName type="university">École de médecine (Université Washington de Saint-Louis)</orgName><placeName><settlement type="city">Saint-Louis (Missouri)</settlement><region type="state">Missouri (État)</region></placeName></affiliation></author><author><name sortKey="Emami, Bahman E" sort="Emami, Bahman E" uniqKey="Emami B" first="Bahman E" last="Emami">Bahman E. 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Purdy</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Division of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis MO 63110</wicri:regionArea><orgName type="university">École de médecine (Université Washington de Saint-Louis)</orgName><placeName><settlement type="city">Saint-Louis (Missouri)</settlement><region type="state">Missouri (État)</region></placeName></affiliation></author></analytic><monogr></monogr><series><title level="j">International Journal of Radiation Oncology, Biology, Physics</title><title level="j" type="abbrev">ROB</title><idno type="ISSN">0360-3016</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1993">1993</date><biblScope unit="volume">26</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="321">321</biblScope><biblScope unit="page" to="333">333</biblScope></imprint><idno type="ISSN">0360-3016</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0360-3016</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Annealing</term><term>Biol</term><term>Bladder</term><term>Clinical decision analysis</term><term>Clinical endpoint</term><term>Clinical endpoints</term><term>Clinical features</term><term>Clinical issues</term><term>Clinical relevance</term><term>Clinician</term><term>Clinician name</term><term>Complete model</term><term>Complication</term><term>Complication probabilities</term><term>Complication probability</term><term>Comput</term><term>Conformal</term><term>Connective tissue</term><term>Critical structure</term><term>Decision analysis</term><term>Dos</term><term>Dose distribution</term><term>Dose distributions</term><term>Endpoint</term><term>Femoral</term><term>Femur</term><term>Generic model</term><term>Interactive tool</term><term>Intermediate values</term><term>Inverse problem</term><term>Jain</term><term>Linear programming</term><term>Mallinckrodt institute</term><term>Methodology</term><term>National cancer institute</term><term>Normal probability</term><term>Normal tissue</term><term>Normal tissues</term><term>Ntcp</term><term>Ntcp models</term><term>Ntcp values</term><term>Objective evaluation</term><term>Objective functions</term><term>Oncol</term><term>Oncologist</term><term>Oncology</term><term>Optimal treatment plan</term><term>Optimization</term><term>Optimized</term><term>Other issues</term><term>Overall utility</term><term>Photon</term><term>Photon treatment planning</term><term>Phys</term><term>Preference</term><term>Prostate</term><term>Prototypical</term><term>Prototypical weight</term><term>Prototypical weights</term><term>Purdy</term><term>Qualitative evaluation model</term><term>Radiat</term><term>Radiation oncologist</term><term>Radiation oncologists</term><term>Radiation oncology</term><term>Radiation therapy</term><term>Radiation therapy treatment planning</term><term>Radiation treatment</term><term>Radiation treatment planning</term><term>Radiation treatment plans</term><term>Radiology</term><term>Radiotherapy</term><term>Radiotherapy treatment planning</term><term>Ranking</term><term>Rectum</term><term>Same patient</term><term>Same performance</term><term>Schultheiss</term><term>Symbolic labels</term><term>Target volume</term><term>Target volumes</term><term>Threshold doses</term><term>Trad</term><term>Treatment failure</term><term>Treatment field</term><term>Treatment plan</term><term>Treatment planner</term><term>Treatment planning</term><term>Treatment plans</term><term>Treatment preferences</term><term>Tumor site</term><term>Utility thresholds</term><term>Weighti</term><term>Worksheet</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Purpose: Selecting the optimal radiation treatment plan from a set of competing plans involves making trade-offs among the doses delivered to the target volumes and normal tissues by the competing plans. Evaluation of 3-dimensional radiation treatment plans is difficult because it requires the review of vast amount of graphical and numerical data. We have developed an objective plan-ranking model based on the concepts of decision analysis.Methods and Materials: Our model ranks a set of tentative radiation treatment plans from best to worst. A figure of merit is computed for each plan based on probabilities of possible clinical complications such as non-eradication of the tumor and radiation induced damage to the nearby healthy normal tissues, and weights which indicate their clinical relevance. This figure of merit is used to rank the plans. Key issues addressed by the model include the incorporation of individual treatment preferences of the radiation oncologist and clinical features of the patient.Results: A methodology has been established for eliciting the treatment preferences of radiation oncologists. Results of this elicitation, and examples of several plan evaluations are presented. An interactive computer-based tool has been developed as one of a set of tools to assist in the evaluation of 3-dimensional radiation treatment plans.Conclusion: The paper presents a decision-analytic model incorporating radiation oncologists' treatment preferences and an interactive computer-based tool for objectively ranking competing radiation treatment plans. The tool can be used by radiation oncologists for the evaluation of competing plans, or as part of a system which tries to automatically generate optimal treatment plans using mathematical or symbolic techniques.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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