Appropriate margin for planning target volume for breast radiotherapy during deep inspiration breath-hold by variance component analysis

Abstract

Background This study aimed to quantify errors by using a cine electronic portal imaging device (cine EPID) during deep inspiration breath-hold (DIBH) for left-sided breast cancer and to estimate the planning target volume (PTV) by variance component analysis. Methods This study included 25 consecutive left-sided breast cancer patients treated with whole-breast irradiation (WBI) using DIBH. Breath-holding was performed while monitoring abdominal anterior–posterior (AP) motion using the Real-time Position Management (RPM) system. Cine EPID was used to evaluate the chest wall displacements in patients. Cine EPID images of the patients (309,609 frames) were analyzed to detect the edges of the chest wall using a Canny filter. The errors that occurred during DIBH included differences between the chest wall position detected by digitally reconstructed radiographs and that of all cine EPID images. The inter-patient, inter-fraction, and intra-fractional standard deviations (SDs) in the DIBH were calculated, and the PTV margin was estimated by variance component analysis. Results The median patient age was 55 (35–79) years, and the mean irradiation time was 20.4 ± 1.7 s. The abdominal AP motion was 1.36 ± 0.94 (0.14–5.28) mm. The overall mean of the errors was 0.30 mm (95% confidence interval: − 0.05–0.65). The inter-patient, inter-fraction, and intra-fractional SDs in the DIBH were 0.82 mm, 1.19 mm, and 1.63 mm, respectively, and the PTV margin was calculated as 3.59 mm. Conclusions Errors during DIBH for breast radiotherapy were monitored using EPID images and appropriate PTV margins were estimated by variance component analysis.

Evaluation of internal margins for prostate for step and shoot intensity‐modulated radiation therapy and volumetric modulated arc therapy using different margin formulas

“…Ono et al reported internal and setup margins for breast radiotherapy during deep inspiration breath-hold using the REML formula. 20 They found that the systematic errors of the chest wall motion were smaller than the random errors, and appropriate systematic errors were indicated. In the current study, it was clarified that the appropriate IMs for the prostate were derived from the REML formula by excluding overestimation of systematic errors.…”

“…Ono et al. evaluated the intra‐fractional and inter‐fractional chest‐wall motions for breast radiotherapy during a deep inspiration breath‐hold, and the planning volume target (PTV) margin was estimated using the REML formula 20 . They found that the PTV margin would be reduced using the REML formula excluding overestimation of systematic errors.…”

“…Ono et al evaluated the intra-fractional and inter-fractional chest-wall motions for breast radiotherapy during a deep inspiration breathhold, and the planning volume target (PTV) margin was estimated using the REML formula. 20 They found that the PTV margin would be reduced using the REML formula excluding overestimation of systematic errors. Here, the PTV margin for prostate radiotherapy is highly dependent on imaging modality during treatment, the immobilization system, the irradiation technique (which have different treatment times), and the method of margin calculation; thus, the determination of an appropriate margin is required.…”

Accuracy of patient setup positioning using surface‐guided radiotherapy with deformable registration in cases of surface deformation

“…Ono et al. 19 calculated the setup margin for left‐sided breast radiotherapy during DIBH. They found the optimal planning target volume (PTV) margin as 3.59 mm from analyzing the systematic and random errors.…”

A Review of the Edge Detection Technology

“…PSNR is the quotient of the medium voltage about the peak omen to the noise [80]. MSE shows the average of the sum of squares of the divergence amid the pixel equivalent of the detected image and the pixel equivalent of the original image [81]. We mark the structural correlation together the detected data with the initial data by SSIM.…”