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TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT
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| Zeitschriftentitel: | Medical Physics |
|---|---|
| Personen und Körperschaften: | |
| In: | Medical Physics, 39, 2012, 6Part30, S. 4008-4008 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
| author_facet |
Zhang, X Zhang, X |
|---|---|
| author |
Zhang, X |
| spellingShingle |
Zhang, X Medical Physics TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT General Medicine |
| author_sort |
zhang, x |
| spelling |
Zhang, X 0094-2405 2473-4209 Wiley General Medicine http://dx.doi.org/10.1118/1.4736349 <jats:p>In intensity‐modulated radiotherapy (IMRT), including intensity modulated X‐ray therapy (IMXT), volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and it is often the most time‐consuming aspect of the RT process. There is also a long learning curve to design the “optimal” plan for the newest technology such as IMPT. However, recent optimization algorithm technology development, especially automatically planning based on some kind of machine learning, have reached a point in treatment planning process where treatment plan can be designed without or with minimum human intervention. In this work, we describe the methodology and validation results of mdaccAutoPlan system: this system has the potential to automate IMXT/VMAT/IMPT treatment planning to the extent that human intervention is minimal. For every new patient, the mdaccAutoPlan system could automatically generate plans (the autoplans) that meet or exceed the institution's dose‐volume constraints by one button click in the TPS. The methodology 1) automatically sets beam angles based on a data mining of previously plans designed by experienced dosimetristsand a beam angle automation algorithm, 2) judiciously designs the planning structures, which were shown to be effective for all the patient cases we studied, and 3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from the plans created by the experienced dosimetrists. The “autoplans” were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. This lecture will provide an overview of overall methodology in the “autoplan” system and its validation on the treatment planning process in IMRT, VMAT, IMPT and adaptive radiotherapy planning (ART).</jats:p><jats:p>Learning Objectives:</jats:p><jats:p>1. Understand the bottleneck of current inverse planning and evaluation process</jats:p><jats:p>2. Understand the importance of data mining of pre‐existing treatment plans</jats:p><jats:p>3. Understand the impact of automation on reducing the learning curve of new technologies</jats:p><jats:p>4. Understand the importance of automatic planning on improving quality and consistence of radiation therapy</jats:p><jats:p>5. Understand the clinical workflow of automatic planning</jats:p> TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT Medical Physics |
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| title |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_unstemmed |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_full |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_fullStr |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_full_unstemmed |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_short |
TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_sort |
th‐e‐brcd‐03: automatic treatment planning for imrt, vmat and impt |
| topic |
General Medicine |
| url |
http://dx.doi.org/10.1118/1.4736349 |
| publishDate |
2012 |
| physical |
4008-4008 |
| description |
<jats:p>In intensity‐modulated radiotherapy (IMRT), including intensity modulated X‐ray therapy (IMXT), volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and it is often the most time‐consuming aspect of the RT process. There is also a long learning curve to design the “optimal” plan for the newest technology such as IMPT. However, recent optimization algorithm technology development, especially automatically planning based on some kind of machine learning, have reached a point in treatment planning process where treatment plan can be designed without or with minimum human intervention. In this work, we describe the methodology and validation results of mdaccAutoPlan system: this system has the potential to automate IMXT/VMAT/IMPT treatment planning to the extent that human intervention is minimal. For every new patient, the mdaccAutoPlan system could automatically generate plans (the autoplans) that meet or exceed the institution's dose‐volume constraints by one button click in the TPS. The methodology 1) automatically sets beam angles based on a data mining of previously plans designed by experienced dosimetristsand a beam angle automation algorithm, 2) judiciously designs the planning structures, which were shown to be effective for all the patient cases we studied, and 3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from the plans created by the experienced dosimetrists. The “autoplans” were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. This lecture will provide an overview of overall methodology in the “autoplan” system and its validation on the treatment planning process in IMRT, VMAT, IMPT and adaptive radiotherapy planning (ART).</jats:p><jats:p>Learning Objectives:</jats:p><jats:p>1. Understand the bottleneck of current inverse planning and evaluation process</jats:p><jats:p>2. Understand the importance of data mining of pre‐existing treatment plans</jats:p><jats:p>3. Understand the impact of automation on reducing the learning curve of new technologies</jats:p><jats:p>4. Understand the importance of automatic planning on improving quality and consistence of radiation therapy</jats:p><jats:p>5. Understand the clinical workflow of automatic planning</jats:p> |
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| description | <jats:p>In intensity‐modulated radiotherapy (IMRT), including intensity modulated X‐ray therapy (IMXT), volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and it is often the most time‐consuming aspect of the RT process. There is also a long learning curve to design the “optimal” plan for the newest technology such as IMPT. However, recent optimization algorithm technology development, especially automatically planning based on some kind of machine learning, have reached a point in treatment planning process where treatment plan can be designed without or with minimum human intervention. In this work, we describe the methodology and validation results of mdaccAutoPlan system: this system has the potential to automate IMXT/VMAT/IMPT treatment planning to the extent that human intervention is minimal. For every new patient, the mdaccAutoPlan system could automatically generate plans (the autoplans) that meet or exceed the institution's dose‐volume constraints by one button click in the TPS. The methodology 1) automatically sets beam angles based on a data mining of previously plans designed by experienced dosimetristsand a beam angle automation algorithm, 2) judiciously designs the planning structures, which were shown to be effective for all the patient cases we studied, and 3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from the plans created by the experienced dosimetrists. The “autoplans” were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. This lecture will provide an overview of overall methodology in the “autoplan” system and its validation on the treatment planning process in IMRT, VMAT, IMPT and adaptive radiotherapy planning (ART).</jats:p><jats:p>Learning Objectives:</jats:p><jats:p>1. Understand the bottleneck of current inverse planning and evaluation process</jats:p><jats:p>2. Understand the importance of data mining of pre‐existing treatment plans</jats:p><jats:p>3. Understand the impact of automation on reducing the learning curve of new technologies</jats:p><jats:p>4. Understand the importance of automatic planning on improving quality and consistence of radiation therapy</jats:p><jats:p>5. Understand the clinical workflow of automatic planning</jats:p> |
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| spelling | Zhang, X 0094-2405 2473-4209 Wiley General Medicine http://dx.doi.org/10.1118/1.4736349 <jats:p>In intensity‐modulated radiotherapy (IMRT), including intensity modulated X‐ray therapy (IMXT), volumetric modulated arc therapy (VMAT) and intensity modulated proton therapy (IMPT), the quality of the treatment plan, which is highly dependent upon the treatment planner's level of experience, greatly affects the potential benefits of the radiotherapy (RT). Furthermore, the planning process is complicated and requires a great deal of iteration, and it is often the most time‐consuming aspect of the RT process. There is also a long learning curve to design the “optimal” plan for the newest technology such as IMPT. However, recent optimization algorithm technology development, especially automatically planning based on some kind of machine learning, have reached a point in treatment planning process where treatment plan can be designed without or with minimum human intervention. In this work, we describe the methodology and validation results of mdaccAutoPlan system: this system has the potential to automate IMXT/VMAT/IMPT treatment planning to the extent that human intervention is minimal. For every new patient, the mdaccAutoPlan system could automatically generate plans (the autoplans) that meet or exceed the institution's dose‐volume constraints by one button click in the TPS. The methodology 1) automatically sets beam angles based on a data mining of previously plans designed by experienced dosimetristsand a beam angle automation algorithm, 2) judiciously designs the planning structures, which were shown to be effective for all the patient cases we studied, and 3) automatically adjusts the objectives of the objective function based on a parameter automation algorithm. We compared treatment plans created in this system (mdaccAutoPlan) based on the overall methodology with plans from the plans created by the experienced dosimetrists. The “autoplans” were consistently better, or no worse, than the plans produced by experienced medical dosimetrists in terms of tumor coverage and normal tissue sparing. This lecture will provide an overview of overall methodology in the “autoplan” system and its validation on the treatment planning process in IMRT, VMAT, IMPT and adaptive radiotherapy planning (ART).</jats:p><jats:p>Learning Objectives:</jats:p><jats:p>1. Understand the bottleneck of current inverse planning and evaluation process</jats:p><jats:p>2. Understand the importance of data mining of pre‐existing treatment plans</jats:p><jats:p>3. Understand the impact of automation on reducing the learning curve of new technologies</jats:p><jats:p>4. Understand the importance of automatic planning on improving quality and consistence of radiation therapy</jats:p><jats:p>5. Understand the clinical workflow of automatic planning</jats:p> TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT Medical Physics |
| spellingShingle | Zhang, X, Medical Physics, TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT, General Medicine |
| title | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_full | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_fullStr | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_full_unstemmed | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_short | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| title_sort | th‐e‐brcd‐03: automatic treatment planning for imrt, vmat and impt |
| title_unstemmed | TH‐E‐BRCD‐03: Automatic Treatment Planning for IMRT, VMAT and IMPT |
| topic | General Medicine |
| url | http://dx.doi.org/10.1118/1.4736349 |