Heng Li1, Xiaodong Zhang2, Peter Park2, Wei Liu3, Joe Chang4, Zhongxing Liao4, Steve Frank4, Yupeng Li5, Falk Poenisch2, Radhe Mohan2, Michael Gillin2, Ronald Zhu2. 1. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: hengli@mdanderson.org. 2. Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 3. Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, USA. 4. Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 5. Applied Research, Varian Medical Systems, Palo Alto, CA, USA.
Abstract
BACKGROUND AND PURPOSE: Robust optimization for IMPT takes setup and range uncertainties into account during plan optimization. However, anatomical changes were not prospectively included. The purpose of this study was to examine robustness and dose variation due to setup uncertainty and anatomical change in IMPT of lung cancer. MATERIAL AND METHODS: Plans were generated with multi-field optimization based on planning target volume (MFO-PTV) and worst-case robust optimization (MFO-RO) on simulation computed tomography scans (CT0) for nine patients. Robustness was evaluated on the CT0 by computing the standard deviation of DVH (SD-DVH). Dose variations calculated on weekly CTs were compared with SD-DVH. Equivalent uniform dose (EUD) change from the original plan on weekly dose was also calculated for both plans. RESULTS: SD-DVH and dose variation on weekly CTs were both significantly lower in the MFO-RO plans than in the MFO-PTV plans for targets, lungs, and the esophagus (p<0.05). When comparing EUD for ITV between weekly and planned dose distributions, three patients and 28% of repeated CTs for MFO-RO plans, and six patients and 44% of repeated CTs for MFO-PTV plans, respectively, showed an EUD change of >5%. CONCLUSIONS: RO in IMPT reduces the dose variation due to setup uncertainty and anatomy changes during treatment compared with PTV-based planning. However, dose variation could still be substantial; repeated imaging and adaptive planning as needed are highly recommended for IMPT of lung tumors.
BACKGROUND AND PURPOSE: Robust optimization for IMPT takes setup and range uncertainties into account during plan optimization. However, anatomical changes were not prospectively included. The purpose of this study was to examine robustness and dose variation due to setup uncertainty and anatomical change in IMPT of lung cancer. MATERIAL AND METHODS: Plans were generated with multi-field optimization based on planning target volume (MFO-PTV) and worst-case robust optimization (MFO-RO) on simulation computed tomography scans (CT0) for nine patients. Robustness was evaluated on the CT0 by computing the standard deviation of DVH (SD-DVH). Dose variations calculated on weekly CTs were compared with SD-DVH. Equivalent uniform dose (EUD) change from the original plan on weekly dose was also calculated for both plans. RESULTS:SD-DVH and dose variation on weekly CTs were both significantly lower in the MFO-RO plans than in the MFO-PTV plans for targets, lungs, and the esophagus (p<0.05). When comparing EUD for ITV between weekly and planned dose distributions, three patients and 28% of repeated CTs for MFO-RO plans, and six patients and 44% of repeated CTs for MFO-PTV plans, respectively, showed an EUD change of >5%. CONCLUSIONS: RO in IMPT reduces the dose variation due to setup uncertainty and anatomy changes during treatment compared with PTV-based planning. However, dose variation could still be substantial; repeated imaging and adaptive planning as needed are highly recommended for IMPT of lung tumors.
Authors: Olivier Chapet; Emma Thomas; Marc L Kessler; Benedick A Fraass; Randall K Ten Haken Journal: Int J Radiat Oncol Biol Phys Date: 2005-09-01 Impact factor: 7.038
Authors: Adam D Yock; Arvind Rao; Lei Dong; Beth M Beadle; Adam S Garden; Rajat J Kudchadker; Laurence E Court Journal: Med Phys Date: 2014-05 Impact factor: 4.071
Authors: Richard Y Wu; Amy Y Liu; Terence T Sio; Pierre Blanchard; Cody Wages; Mayankkumar V Amin; Gary B Gunn; Uwe Titt; Rong Ye; Kazumichi Suzuki; Michael T Gillin; Xiaorong R Zhu; Radhe Mohan; Steven J Frank Journal: Int J Part Ther Date: 2017-12-28