TY - JOUR
T1 - Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy
AU - Song, Ting
AU - Staub, David
AU - Chen, Mingli
AU - Lu, Weiguo
AU - Tian, Zhen
AU - Jia, Xun
AU - Li, Yongbao
AU - Zhou, Linghong
AU - Jiang, Steve B.
AU - Gu, Xuejun
PY - 2015/10/8
Y1 - 2015/10/8
N2 - In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.
AB - In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.
KW - bagging ensemble learning
KW - dosimetric endpoints
KW - IMRT plan quality control
UR - http://www.scopus.com/inward/record.url?scp=84946086626&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84946086626&partnerID=8YFLogxK
U2 - 10.1088/0031-9155/60/21/8213
DO - 10.1088/0031-9155/60/21/8213
M3 - Article
C2 - 26447829
AN - SCOPUS:84946086626
SN - 0031-9155
VL - 60
SP - 8213
EP - 8227
JO - Physics in Medicine and Biology
JF - Physics in Medicine and Biology
IS - 21
ER -