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Objective:To propose a markless patient setup workflow based on the optical surface monitoring system (AlignRT) and open-face mask immobilization for whole-course head tumor radiotherapy, assess the setup time and repositioning frequency of the proposed workflow, and conduct a comparative analysis of the differences, correlation, and consistency of the setup errors of the AlignRT and cone beam CT (CBCT) systems.Methods:A retrospective analysis was conducted for the data on the errors of 132 fractionated setup based on open-face mask immobilization of 33 head tumor patients. AlignRT-guided markless patient setup workflow was applied throughout the radiotherapy. Meanwhile, the body structures automatically generated by the treatment planning system were used as body references. The 6-degree-of-freedom (6DoF) setup errors (lateral, vertical, longitudinal, rotation, pitch, roll, and yaw directions), setup time, and repositioning frequency of the AlignRT and CBCT systems were recorded and analyzed. The Wilcoxon and Spearman analyses were used to statistically assess the differences and correlation of the setup errors of the two systems. Moreover, the Bland-Altman analysis was employed to evaluate the consistency of the two systems.Results:The 6DoF setup errors of CBCT were within the clinical tolerance (linear motions: -0.30 to 0.30 cm; rotational motions: -2.0° to 2.0°). The setup time and repositioning frequency of CBCT were (98 ± 31) s and 1.51% (2/132), respectively. There was no significant difference in setup errors between the two systems except those in x-axis ( Z = -3.11, P= 0.002), y-axis ( Z = -7.40, P<0.001), and Pitch ( Z= -4.48, P<0.001). There was a significant positive correlation between the setup errors along lateral ( rs = 0.47, P<0.001) and vertical ( rs = 0.29, P = 0.001) directions, rotation (Rtn; rs = 0.47, P<0.001), pitch (Pitch; rs = 0.28, P = 0.001) and roll (Roll; rs = 0.45, P<0.001) of the two systems. The 95% limits of agreement (95% LoA) of 6DoF setup errors were -0.12 to 0.09 cm, -0.07 to 0.17 cm, -0.19 to 0.20 cm, -1.0° to 0.9 °, -1.0° to 1.5°, and -0.9° to 1.0°, respectively. The 95% confidence interval (95% CI) of 95% LoA was -0.14 to 0.11 cm, -0.09 to 0.19 cm, -0.23 to 0.23 cm, -1.2° to 1.1°, -1.2° to 1.7°, and-1.0° to 1.1°, respectively, all of which were within the permissible error ranges. The 6DoF setup error difference of 3.41% (27/792< 5%) was beyond the 95% LoA. The maximum absolute differences of 6DoF setup errors within the 95% LoA were 0.12, 0.16, 0.19 cm, 0.9°, 1.5°, and 1.0°, respectively. Conclusions:The proposed markless setup workflow based on AlignRT combined with open-face mask immobilization for whole-course head tumor radiotherapy exhibits reasonable agreement and consistency with the patient setup using CBCT, with acceptable clinical efficiency. It can be applied to the first radiotherapy and the real-time monitoring of therapy to improve the safety and thus is of value in clinical applications.
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Objective:To investigate the performance of W2 plastic scintillator in megavolt photon and electron beams.Methods:The photon and electron beam energy provided by linear accelerator was used to collect data of the W2 scintillator. The parameters include the electrometer reading stability, W2 dose and dose rate linearity, and angular response. And the dose uncertainty of the W2 correction factors was also investigated.Results:The standard deviation of the electrometer reading stability was between 0.03 and 0.47. The linear regression factors of W2 dose were all 1.0; the maximum deviation of the dose rates was 0.61%. The Cerenkov light radiation correction factor(CLR) for 6 and 10 MV were 0.741 and 0.746, respectively, and the CLR for 6, 9, 12 and 15 MeV were 0.750, 0.753, 0.757 and 0.757, respectively. The maximum deviation of dose uncertainty for 15 MeV was 3.15%.Conclusions:The signal obtained by the blue and green channel was no angular dependence, the same as the high-energy electron beam, which verified that the Cerenkov radiation correction factor has good linearity. W2 plastic scintillator can be applied to non-coplanar radiotherapy dosimetry.
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Objective:To solve the problems in intensity-modulated radiation therapy (IMRT) planning, such as large labor cost and high dependence on the experience of physicists and great inconsistency in the quality of plan, and to discuss an unsupervised automatic treatment planning procedure of IMRT.Methods:The eclipse scripting application programming interface (ESAPI) within the Eclipse treatment planning system (TPS) 15.6 and optimization parameters tree search algorithm (OPTSA) were used to emulate and realize the whole planning process. Interacted with the TPS through ESAPI, relevant dosimetric parameters were input and output. The OPTSA evaluated the plan qualities based on dosimetric parameters of the targets and organs at risk (OARs) and iteratively adjusted the optimization objective parameters to achieve a progressively improving IMRT plan. In order to verify the effectiveness of the automatic planning, twenty historical rectum cancer cases were selected from the clinical database, and the dose distribution and specific dosimetric parameters were compared between the plans generated by the OPTSA and the manual plans under the same constraints.Results:All the auto plans have met clinical requirements. Furthermore, 90% and 10% of the auto plans were deemed as clinically improved and equally compared with the manual plans, respectively. The average CI for the PTV was 0.88 and 0.80 for the auto and manual plans respectively. Compared with the manual plans, the mean doses of all the OARs in the auto plans were reduced by 11% in average. The average elapsed time of automatic planning and manual planning was (28.15±3.61) and (36.7±4.6) min, respectively.Conclusions:The plans created by the proposed algorithm have been shown to be at least as good as the manual plans. In addition, this method can shorten the labor time in plan designing while ensuring the plan quality and consistency of the plan.
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Objective:To develope an automatic volumetric modulated arc therapy (VMAT) planning for rectal cancer based on a dose-prediction model for organs at risk(OARs) and an iterative optimization algorithm for objective parameter optimization.Methods:Totally 165 VMAT plans of rectal cancer patients treated in Peking University Cancer Hospital & Institute from June 2018 to January 2021 were selected to establish automatic VMAT planning. Among them, 145 cases were used for training the deep-learning model and 20 for evaluating the feasibility of the model by comparing the automatic planning with manual plans. The deep learning model was used to predict the essential dose-volume histogram (DVH) index as initial objective parameters(IOPs) and the iterative optimization algorithm can automatically modify the objective parameters according to the result of protocol-based automatic iterative optimization(PBAIO). With the predicted IOPs, the automatic planning model based on the iterative optimization algorithm was achieved using a program mable interface.Results:The IOPs of OARs of 20 cases were effectively predicted using the deep learning model, with no significantly statistical difference in the conformity index(CI) for planning target volume(PTV)and planning gross tumor volume(PGTV)between automatic and manual plans( P>0.05). The homogeneity index (HI) of PGTV in automatic and manual plans was 0.06 and 0.05, respectively( t=-6.92, P< 0.05). Compared with manual plans, the automatic plans significantly decreased the V30 for urinary bladder by 2.7% and decreased the V20 for femoral head sand auxiliary structure(avoidance)by 8.37% and 15.95%, respectively ( t=5.65, 11.24, P< 0.05). Meanwhile, the average doses to bladder, femoral heads, and avoidance decreased by 1.91, 4.01, and 3.88 Gy, respectively( t=9.29, 2.80, 10.23, P< 0.05) using the automatic plans. The time of automatic VMAT planning was (71.49±25.48)min in 20 cases. Conclusions:The proposed automatic planning based on dose prediction and an iterative optimization algorithm is feasible and has great potential for sparing OARs and improving the utilization rate of clinical resources.
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Objective:To assess the three-dimensional dose distribution in radiotherapy plans using the structural similarity index(SSIM), compare the performance of SSIM with commonly used quality assessment indices, and develop a SSIM-based quality assessment method of multiple prescribed doses.Methods:The SSIM was introduced to providea quality score of various voxels by comparing actual and ideal three-dimensional dose data and combining the spatial location information of the voxels. Then the average value in a region of interest (ROI) was calculated as the quality score of the region. Fifty-three cases of cervical cancer were selected to analyze the correlation of the SSIM with the uniformity index (HI), conformity index (CI) of the dose distribution in various ROIs and to explore the capability of the SSIM to reflect the uniformity and conformity of dose distribution.Two types of quality defects were individually introduced into two of 53 radiotherapy plans. Then the two plans were compared with normal plans to characterize the response of the SSIM.Results:There was no correlation between HI and SSIM in positive lymph nodes(PGTVnd) due to the decrease in the HI sensitivity, while there was a significant negative correlation between them in regions where PGTVnd was removed from the planning target volume(PTV, R=-0.86, P<0.01). Meanwhile, there was a significant positive correlation between CI and SSIM in PGTVnd ( R=0.83, P<0.01). Therefore, the SSIM can be used to identify the artificial design defects in plans by determining abnormal dose gradients. Conclusions:Apart from reducing the defects of previous assessment parameters, the SSIM has the capability to assess the quality of radiotherapy plans by combining the uniformity and conformity of dose distribution and can provide accurate feedback on the spatial locations of quality defects.
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Objective To compare two pretreatment plan QA methods for HalcyonTM accelerator using Portal Dosimetry (PD),and PTW OCTAVIUS 1500 detector array + Octagonal phantom (Oct 1500)respectively.Methods Parallel measurement-based pretreatment QA using two methods was performed for 22 IMRT/VMAT plans (74 fields) that have been used to treat 20 patients recruited in the Halcyon clinical trial.Several γ 2D comparisons were also applied to provide guidelines for Halcyon planning QA.Results Using Oct1500 method,the γ 2D passing rates for 74 fields in 22 Plans were 95.26±3.59,95.01±3.62 (Local Dose),99.05± 1.35,98.57± 1.96 (Max Dose) respectively.Two-related samples non-parametric tests suggested that the differences between the evaluation criteria were of statistical significance (Z =-7.220,-4.108,P＜0.05).For PD method,the γ 2D passing rates were 84.11％ ± 1.35％ (1 mm/1％),99.07％± 1.35％ (2 mm/2％),and 99.86％ ± 1.35％ (3 mm/3％).Two-related samples non-parametric tests suggested that the differences between evaluation criteria of PD method were statistically significant (Z =-7.475,-7.475,-6.906,P＜0.05).For 74 fields and max dose,3 mm/3％ evaluation criteria,two-related samples non-parametric tests suggested that the differences between PD and Oct1500 method were statistically significant (Z=-5.072,P＜0.05).Conclusions Both methods can be used for Halcyon pretreatment plan QA.PD is superior to Oct1500 with respect to efficiency and spatial resolution-induced verification accuracy.The criteria of 2 mm/2％ for PD,and Max Dose/3 mm/3％ for Oct1500 was suggested.