RESUMO
Objective To evaluate the impact of respiratory motion for dose of target and organ at risk during external-beam partial breast irradiation (EB-PBI).Methods 4D-CT scan sets were acquired for 20 patients who underwent EB-PBI.The volume of the tumour bed (TB) was determined based on seroma or surgical clips on the ten sets of 4D-CT images.For each patient a conventional 3D conformal plan (3D-CRT) was generated based on the 4D-CT end inhalation phase images,then copied and applied to the other phases.The following parameters were calculated to analyse:mean dose (D),homogeneity index (HI),conformal index (CI),and the volumes that received ≥ x Gy (Vx).Results During free breathing,the TB centroid motion was 0.90,0.75 and 0.80 mm in the lateral,anteroposterior and superior-inferior directions,respectively.The medium spatial motion vector was 0.95 mm.In the superiorinferior direction,TB motion significantly correlated with D HI,and CI of PTV (r =-0.458,-0.451 and 0.462,P ＜ 0.05),as well as D V20 and V30 received by the ipsilateral normal breast (r=0.527,0.488 and0.526,P ＜0.05).And in the motion vector,the D V5,V10,V20 of the ipsilateral lung all correlated with TB motion (r =0.416,0.503,0.522 and 0.498,P ＜ 0.05).A correlation also existed between dose and percent volume of heart and volume variation of heart (Dmean,V5 and V10) (r =0.727,0.704 and 0.695,P ＜ 0.05).Conclusions Small TB motion caused by respiratory motion during free breathing result in dosimetric variation of the target and potential dosimetric off-target or suboptimal dose coverage for EB-PBI.The doses of lung during free breathing were relatively sensitive to TB motion and thorax expansion,while heart doses were not influenced notably.
RESUMO
Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.
Assuntos
Humanos , Aprendizagem , Respiração , AtletismoRESUMO
Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.