Purpose: To investigate the feasibility of applying ANOVA newly proposed by Yukinori to verify the setup errors, PTV (Planning Target Volume) margins, DVH for lung cancer with SBRT. Methods: 20 patients receiving SBRT...Purpose: To investigate the feasibility of applying ANOVA newly proposed by Yukinori to verify the setup errors, PTV (Planning Target Volume) margins, DVH for lung cancer with SBRT. Methods: 20 patients receiving SBRT to 50 Gy in 5 fractions with a Varian iX linear acceleration were selected. Each patient was scanned with kV-CBCT before the daily treatment to verify the setup position. Two other error calculation methods raised by Van Herk and Remeijer were also compared to discover the statistical difference in systematic errors (Σ), random errors (σ), PTV margins and DVH. Results: Utilizing two PTV margin calculation formulas (Stroom, Van Herk), PTV calculated by Yukinori method in three directions were (5.89 and 3.95), (5.54 and 3.55), (3.24 and 0.78) mm;Van Herk method were (6.10 and 4.25), (5.73 and 3.83), (3.51 and 1.13) mm;Remeijer method were (6.39 and 4.57), (5.98 and 4.10), (3.69 and 1.33) mm. The volumes of PTV using Yukinori method were significantly smaller (P < 0.05) than Van Herk method and Remeijer method. However, dosimetric indices of PTV (D98, D50, D2) and for OARs (Mean Dose, V20, V5) had no significant difference (P > 0.05) among three methods. Conclusions: In lung SBRT treatment, due to fraction reduction and high level of dose per fraction, ANOVA was able to offset the effect of random factors in systematic errors, reducing the PTV margins and volumes. However, no distinct dose distribution improvement was founded in target volume and organs at risk.展开更多
A radiotherapy treatment margin formula has been analytically derived when a standard deviation (SD) of systematic positioning errors Ʃis relatively small compared to an SD of random positioning errors &s...A radiotherapy treatment margin formula has been analytically derived when a standard deviation (SD) of systematic positioning errors Ʃis relatively small compared to an SD of random positioning errors σ. The margin formula for 0 ≤ Ʃ≤ σwas calculated by linearly interpolating two boundaries at Ʃ= 0 and Ʃ= σ, assuming that the van Herk margin approximation of k1Ʃ+ k2σis valid at Ʃ= σ. It was shown that a margin formula for 0 ≤ Ʃ≤ σmay be approximated by k1σ+ k2Ʃ, leading to a more general form of k1 max(Ʃ,σ) + k2 min(Ʃ,σ) which is a piecewise linear approximation for any values of Ʃand σ.展开更多
We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliar...We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees;in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg·ha-1, respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among individual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Almost none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attributed to plot selection and 10% to the allometric biomass model. The total error of our measurements was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.展开更多
背景与目的:乳腺癌保乳术后单周超大分割全乳放疗能在保证疗效和安全性的同时缩短疗程,是目前可选的全乳放疗方案。超大分割放疗要求患者每日接受图像引导,但其对位置误差的影响尚不明确。在每日锥形束计算机断层扫描(cone-beam compute...背景与目的:乳腺癌保乳术后单周超大分割全乳放疗能在保证疗效和安全性的同时缩短疗程,是目前可选的全乳放疗方案。超大分割放疗要求患者每日接受图像引导,但其对位置误差的影响尚不明确。在每日锥形束计算机断层扫描(cone-beam computed tomography,CBCT)引导下,本研究旨在探索单周超大分割全乳放疗的位置误差及其影响因素,并计算临床靶体积(clinical target volume,CTV)外扩至计划靶体积(planning target volume,PTV)的三维边界。方法:纳入2021年2月-10月于上海瑞金医院入组乳腺癌术后单周超大分割全乳放疗前瞻性研究(NCT04926766)连续入组的患者的临床资料[(2020)临伦审第(352)号]。所有患者每日治疗前摆位后行CBCT1,根据CBCT1纠正误差后再行CBCT2,当次治疗结束后行CBCT3。CBCT1、CBCT2与定位CT的三维位置误差分别为初始、残余分次间误差。CBCT2与CBCT3间的三维位置误差为分次内误差。根据每次治疗的分次间及分次内误差,基于van Herk公式计算CTV外扩至PTV三维边界。结果:本研究共入组患者34例,收集CBCT图像510例次。每日治疗前CBCT在线位置纠正显著减少三维位置误差(初始分次间误差vs残余分次间误差:前后2.8 mm vs 0.4 mm;头脚1.6 mm vs 0.5 mm;左右1.8 mm vs 0.3 mm;P均<0.001)。对于残余分次间误差,CTV体积较大患者(>402.5 cm^(3)vs≤402.5 cm^(3))在前后方向(0.5 mm vs 0.3 mm,P=0.023)和头脚方向(0.6 mm vs 0.5 mm,P=0.037)更大。对于分次内误差,CTV较大患者(>402.5 cm^(3)vs≤402.5 cm^(3))在前后方向更大(0.5 mm vs 0.2 mm,P=0.001);身体质量指数(body mass index,BMI)较高患者(>23.2 kg/m^(2)vs≤23.2 kg/m^(2))在前后方向更大(0.7 mm vs 0.2 mm,P<0.001);体重更大患者(> 60.0 kg vs≤60.0 kg)在前后方向更大(0.5 mm vs 0.2 mm,P=0.033)。每日CBCT引导下CTV外扩至PTV边界推荐为:前后2.3 mm,头脚2.8 mm,左右2.0mm。但CTV>402.5 cm^(3)和BMI>23.2 kg/m^(2)的患者需要更大的头脚方向外扩边界,分别为3.1和3.4 mm。结论:每日CBCT图像引导下,对大部分患者将全乳放疗CTV外扩至PTV的三维边界限制在3 mm内是可行的,而BMI较高和CTV较大患者需在头脚方向适度增大外扩边界。展开更多
文摘Purpose: To investigate the feasibility of applying ANOVA newly proposed by Yukinori to verify the setup errors, PTV (Planning Target Volume) margins, DVH for lung cancer with SBRT. Methods: 20 patients receiving SBRT to 50 Gy in 5 fractions with a Varian iX linear acceleration were selected. Each patient was scanned with kV-CBCT before the daily treatment to verify the setup position. Two other error calculation methods raised by Van Herk and Remeijer were also compared to discover the statistical difference in systematic errors (Σ), random errors (σ), PTV margins and DVH. Results: Utilizing two PTV margin calculation formulas (Stroom, Van Herk), PTV calculated by Yukinori method in three directions were (5.89 and 3.95), (5.54 and 3.55), (3.24 and 0.78) mm;Van Herk method were (6.10 and 4.25), (5.73 and 3.83), (3.51 and 1.13) mm;Remeijer method were (6.39 and 4.57), (5.98 and 4.10), (3.69 and 1.33) mm. The volumes of PTV using Yukinori method were significantly smaller (P < 0.05) than Van Herk method and Remeijer method. However, dosimetric indices of PTV (D98, D50, D2) and for OARs (Mean Dose, V20, V5) had no significant difference (P > 0.05) among three methods. Conclusions: In lung SBRT treatment, due to fraction reduction and high level of dose per fraction, ANOVA was able to offset the effect of random factors in systematic errors, reducing the PTV margins and volumes. However, no distinct dose distribution improvement was founded in target volume and organs at risk.
文摘A radiotherapy treatment margin formula has been analytically derived when a standard deviation (SD) of systematic positioning errors Ʃis relatively small compared to an SD of random positioning errors σ. The margin formula for 0 ≤ Ʃ≤ σwas calculated by linearly interpolating two boundaries at Ʃ= 0 and Ʃ= σ, assuming that the van Herk margin approximation of k1Ʃ+ k2σis valid at Ʃ= σ. It was shown that a margin formula for 0 ≤ Ʃ≤ σmay be approximated by k1σ+ k2Ʃ, leading to a more general form of k1 max(Ʃ,σ) + k2 min(Ʃ,σ) which is a piecewise linear approximation for any values of Ʃand σ.
文摘We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees;in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg·ha-1, respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among individual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Almost none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attributed to plot selection and 10% to the allometric biomass model. The total error of our measurements was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.
文摘背景与目的:乳腺癌保乳术后单周超大分割全乳放疗能在保证疗效和安全性的同时缩短疗程,是目前可选的全乳放疗方案。超大分割放疗要求患者每日接受图像引导,但其对位置误差的影响尚不明确。在每日锥形束计算机断层扫描(cone-beam computed tomography,CBCT)引导下,本研究旨在探索单周超大分割全乳放疗的位置误差及其影响因素,并计算临床靶体积(clinical target volume,CTV)外扩至计划靶体积(planning target volume,PTV)的三维边界。方法:纳入2021年2月-10月于上海瑞金医院入组乳腺癌术后单周超大分割全乳放疗前瞻性研究(NCT04926766)连续入组的患者的临床资料[(2020)临伦审第(352)号]。所有患者每日治疗前摆位后行CBCT1,根据CBCT1纠正误差后再行CBCT2,当次治疗结束后行CBCT3。CBCT1、CBCT2与定位CT的三维位置误差分别为初始、残余分次间误差。CBCT2与CBCT3间的三维位置误差为分次内误差。根据每次治疗的分次间及分次内误差,基于van Herk公式计算CTV外扩至PTV三维边界。结果:本研究共入组患者34例,收集CBCT图像510例次。每日治疗前CBCT在线位置纠正显著减少三维位置误差(初始分次间误差vs残余分次间误差:前后2.8 mm vs 0.4 mm;头脚1.6 mm vs 0.5 mm;左右1.8 mm vs 0.3 mm;P均<0.001)。对于残余分次间误差,CTV体积较大患者(>402.5 cm^(3)vs≤402.5 cm^(3))在前后方向(0.5 mm vs 0.3 mm,P=0.023)和头脚方向(0.6 mm vs 0.5 mm,P=0.037)更大。对于分次内误差,CTV较大患者(>402.5 cm^(3)vs≤402.5 cm^(3))在前后方向更大(0.5 mm vs 0.2 mm,P=0.001);身体质量指数(body mass index,BMI)较高患者(>23.2 kg/m^(2)vs≤23.2 kg/m^(2))在前后方向更大(0.7 mm vs 0.2 mm,P<0.001);体重更大患者(> 60.0 kg vs≤60.0 kg)在前后方向更大(0.5 mm vs 0.2 mm,P=0.033)。每日CBCT引导下CTV外扩至PTV边界推荐为:前后2.3 mm,头脚2.8 mm,左右2.0mm。但CTV>402.5 cm^(3)和BMI>23.2 kg/m^(2)的患者需要更大的头脚方向外扩边界,分别为3.1和3.4 mm。结论:每日CBCT图像引导下,对大部分患者将全乳放疗CTV外扩至PTV的三维边界限制在3 mm内是可行的,而BMI较高和CTV较大患者需在头脚方向适度增大外扩边界。
