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PURPOSE: Patient-specific quality assurance (QA) measurement is conducted to confirm the accuracy of dose delivery. However, measurement is time-consuming and places a heavy workload on the medical physicists and radiological technologists. In this study, we proposed a prediction model for gamma evaluation, based on deep learning. We applied the model to a QA measurement dataset of prostate cancer cases to evaluate its practicality. METHODS: Sixty pretreatment verification plans from prostate cancer patients treated using intensity modulated radiation therapy were collected. Fifteen-layer convolutional neural networks (CNN) were developed to learn the sagittal planar dose distributions from a RT-3000 QA phantom (R-TECH.INC., Tokyo, Japan). The percentage gamma passing rate (GPR) was measured using GAFCHROMIC EBT3 film (Ashland Specialty Ingredients, Covington, USA). The input training data also included the volume of the PTV (planning target volume), rectum, and overlapping region, measured in cm3 , and the monitor unit values for each field. The network produced predicted GPR values at four criteria: 2%(global)/2 mm, 3%(global)/2 mm, 2%(global)/3 mm, and 3%(global)/3 mm. Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, was used for learning and for optimizing the CNN-based model. Fivefold cross-validation was applied to validate the performance of the proposed method. Forty cases were used for training and validation set in fivefold cross-validation, and the remaining 20 cases were used for the test set. The predicted and measured GPR values were compared. RESULTS: A linear relationship was found between the measured and predicted values, for each of the four criteria. Spearman rank correlation coefficients in validation set between measured and predicted GPR values at four criteria were 0.73 at 2%/2 mm, 0.72 at 3%/2 mm, 0.74 at 2%/3 mm, and 0.65 at 3%/3 mm, respectively (P < 0.01). The Spearman rank correlation coefficients in the test set were 0.62 (P < 0.01) at 2%/2 mm, 0.56 (P < 0.01) at 3%/2 mm, 0.51 (P = 0.02) at 2%/3 mm, and 0.32 (P = 0.16) at 3%/3 mm. These results demonstrated a strong or moderate correlation between the predicted and measured values. CONCLUSIONS: We developed a CNN-based prediction model for patient-specific QA of dose distribution in prostate treatment. Our results suggest that deep learning may provide a useful prediction model for gamma evaluation of patient-specific QA in prostate treatment planning.

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BACKGROUND: Volume-based parameters, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), on F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) are useful for predicting treatment response in nonsmall cell lung cancer (NSCLC). We aimed to examine intra- and inter-operator reproducibility to measure the MTV and TLG, and to estimate their dependency on the uptake time. METHODS: Fifty NSCLC patients underwent preoperative FDG-PET. After an injection of FDG, the whole body was scanned twice: at the early phase (61.4 ± 2.8 min) and delayed phase (117.7 ± 1.6 min). Two operators independently defined the tumor boundary using three different delineation methods: (1) the absolute SUV threshold method (MTVp and TLGp; p = 2.0, 2.5, 3.0, 3.5), (2) the fixed% SUVmax threshold method (MTVq% and TLGq%; q = 35, 40, 45), and (3) the adaptive region-growing method (MTVARG and TLGARG). Parameters were compared between operators and between phases. RESULTS: Both the intra- and inter-operator reproducibility were high for all parameters using any method (intra-class correlation > 0.99 each). MTV3.0 and MTV3.5 resulted in a significant increase from the early to delayed phase (P < 0.05 for both), whereas MTV2.0 and MTV2.5 neither increased nor decreased (P = n.s.). All of the MTVq% values significantly decreased over time (P < 0.01), whereas MTVARG and TLG with any delineation method increased significantly (P < 0.05). CONCLUSIONS: High reproducibility of MTV and TLG was obtained by all of the methods used. MTV2.0 and MTV2.5 were the least sensitive to uptake time, and may be good alternatives when we compare images acquired with different uptake times, although applying constant uptake time is important for volume measurement.

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We investigated the uncertainty in patient set-up margin analysis with a small dataset consisting of a limited number of clinical cases over a short time period, and propose a method for determining the optimum set-up margin. Patient set-up errors from 555 registration images of 15 patients with prostate cancer were tested for normality using a quantile-quantile (Q-Q) plot and a Kolmogorov-Smirnov test with the hypothesis that the data were not normally distributed. The ranges of set-up errors include the set-up errors within the 95% interval of the entire patient data histogram, and their equivalent normal distributions were compared. The patient set-up error was not normally distributed. When the patient set-up error distribution was assumed to have a normal distribution, an underestimate of the actual set-up error occurred in some patients but an overestimate occurred in others. When using a limited dataset for patient set-up errors, which consists of only a small number of the cases over a short period of time in a clinical practice, the 2.5% and 97.5% intervals of the actual patient data histogram from the percentile method should be used for estimating the set-up margin. Since set-up error data is usually not normally distributed, these intervals should provide a more accurate estimate of set-up margin. In this way, the uncertainty in patient set-up margin analysis in radiation therapy can be reduced.

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We aimed to optimize internal margin (IM) determination for respiratory-gated radiotherapy using end-expiratory phase assessments using a motion phantom. Four-dimensional computed tomography (4D CT) data were acquired using a GE LightSpeed RT CT scanner, a respiratory-gating system, and a motion phantom designed to move sinusoidally. To analyze the accuracy of 4D CT temporal resolution, a 25.4 mm diameter sphere was inserted into the motion phantom, and we measured the differences in sphere diameters between static and end-exhalation phase images. In addition, the IM obtained from the maximum intensity projection within the gating window (MIP(GW)) image was compared to theoretical value. Cranial-caudal motion displacement ranged from 5.0 to 30.0 mm, and the respiratory period ranged from 2.0 to 6.0 sec. Differences in sphere diameters between static and end-exhalation phase images ranged from 0.37 to 4.6 mm, with 5.0-mm and 30 mm target displacements, respectively. Differences between the IM obtained from the MIP(GW) and the theoretical values ranged from 1.12 to 6.23 mm with 5.0mm and 30 mm target displacements, respectively. These differences increased in proportion to the target velocity due to a motion artifact generated during tube rotation. In this study, the IMs obtained using the MIPGW image were overestimated in all cases. We therefore propose that the internal target volume (ITV) for respiratory-gated radiotherapy should be determined by adding the calculated value to the end-exhalation phase image. We also demonstrate a methodology for subtracting motion artifacts from the ITV using a motion phantom.

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The post-exposure density growth (PEDG) is one of the characteristics of radiochromic film (RCF). In film dosimetry using RCF and a flatbed scanner, pixel values read out from the RCF are converted to dose (hereafter, film dose) by using a calibration curve. The aim of this study is to analyze the relationship between the pixel value read out from the RCF and the PEDG, and that between the film dose converted from the RCF and the PEDG. The film (GAFCHROMIC EBT) was irradiated with 10-MV X-rays in an ascending 11-dose-step arrangement. The pixel values of the irradiated EBT film were measured at arbitrary hours using an Epson flatbed scanner. In this study, the reference time was 24 h after irradiation, and all dose conversions from the pixel values read out from the EBT film were made using a calibration curve for 24 h after irradiation. For delivered doses of 33 and 348 cGy, the measured pixel values at 0.1 and 16 h after irradiation represented ranges of -9.6% to -0.7% and -3.9% to -0.3%, respectively, of the reference value. The relative changes between the pixel values read out from the EBT film at each elapsed time and that at the reference time decreased with increasing delivered dose. However, the difference range for all the film doses had a width of approximately -10% of the reference value at elapsed times from 0.1 to 16 h, and it showed no dependence on the delivered dose.

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