Search

Purpose We analyzed interfractional robustness of scanning carbon ion radiotherapy (CIRT) for prostate cancer based on the dose distribution using daily in‐room computed tomography (CT) images. Materials and Methods We analyzed 11 consecutive patients treated with scanning CIRT for localized prostate cancer in our hospital between December 2015 and January 2016. In‐room CT images were taken under treatment conditions in every treatment session. The dose distribution on each in‐room CT image was recalculated, while retaining the pencil beam arrangement of the initial treatment plan. Then, the dose–volume histogram (DVH) parameters including the percentage of the clinical target volume (CTV) with 95% and 90% of the prescribed dose area (V95% of CTV, V90% of CTV) and V80% of rectum were calculated. The acceptance criteria for the CTV and rectum were set at V95% of CTV ≥95%, V90% of CTV ≥98%, and V80% of rectum < 10 ml. Results V95% of CTV, V90% of CTV, and V80% of rectum for the reproduced plans were 98.8 ± 3.49%, 99.5 ± 2.15%, and 4.39 ± 3.96 ml, respectively. Acceptance of V95% of CTV, V90% of CTV, and V80% of rectum was obtained in 123 (94%), 125 (95%) and 117 sessions (89%), respectively. Acceptance of the mean dose of V95% of CTV, V90% of CTV, and V80% of rectum for each patient was obtained in 10 (91%), 10 (91%), and 11 patients (100%), respectively. Conclusions We demonstrated acceptable interfractional robustness based on the dose distribution in scanning CIRT for prostate cancer.

Background/aim The present study aimed to compare the radiation dose distribution of carbon-ion radiotherapy (CIRT) for stage I esophageal cancer with three-dimensional conformal radiotherapy (3DCRT) and volumetric modulated arc therapy (VMAT). Patients and methods Fifteen patients with cT1bN0M0 esophageal cancer who received 3DCRT at Kanagawa Cancer Center between January 2014 and April 2019 were enrolled. The dose-volume histogram parameters of the target volume and normal organs planned with CIRT, 3DCRT, and VMAT were evaluated. Results The homogeneity index for the target volume of CIRT was significantly lower than that of 3DCRT and VMAT. In addition, the radiation dose of CIRT to the heart, lungs, spinal cord, and skin was significantly lower than that of 3DCRT and VMAT. Conclusion Favorable dose distributions with CIRT were demonstrated compared with 3DCRT and VMAT for esophageal cancer.

We compared predictive performance between dose volume histogram (DVH) parameter addition and deformable image registration (DIR) addition for gastrointestinal (GI) toxicity in cervical cancer patients. A total of 59 patients receiving brachytherapy and external beam radiotherapy were analyzed retrospectively. The accumulative dose was calculated by three methods: conventional DVH parameter addition, full DIR addition and partial DIR addition. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{2{cm}^3}$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{1{cm}^3}$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{0.1{cm}^3}$\end{document} (minimum doses to the most exposed 2 cm3, 1cm3 and 0.1 cm3 of tissue, respectively) of the rectum and sigmoid were calculated by each method. V50, V60 and V70 Gy (volume irradiated over 50, 60 and 70 Gy, respectively) were calculated in full DIR addition. The DVH parameters were compared between toxicity (≥grade1) and non-toxicity groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves were compared to evaluate the predictive performance of each method. The differences between toxicity and non-toxicity groups in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{2{cm}^3}$\end{document} were 0.2, 5.7 and 3.1 Gy for the DVH parameter addition, full DIR addition and partial DIR addition, respectively. The AUCs of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{2{cm}^3}$\end{document} were 0.51, 0.67 and 0.57 for DVH parameter addition, full DIR addition and partial DIR addition, respectively. In full DIR addition, the difference in dose between toxicity and non-toxicity was the largest and AUC was the highest. AUCs of V50, V60 and V70 Gy were 0.51, 0.63 and 0.62, respectively, and V60 and V70 were high values close to the value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${D}_{2{cm}^3}$\end{document} of the full DIR addition. Our results suggested that the full DIR addition may have the potential to predict toxicity more accurately than the conventional DVH parameter addition, and that it could be more effective to accumulate to all pelvic irradiation by DIR.

The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

Background/aim The purpose of this study was to compare the dose distribution between scanning carbon-ion radiotherapy (sCIRT) and volumetric-modulated arc therapy with stereotactic body radiation therapy (VMAT-SBRT) for stage I non-small cell lung cancer (NSCLC). Patients and methods Fifteen patients with early-stage NSCLC who underwent sCIRT at Kanagawa Cancer Center between 2018-2020 were enrolled. Dose-volume histogram parameters of the planned target volume and normal organs for sCIRT and VMAT-SBRT were evaluated. Results The homogeneity index of the target volume of sCIRT was significantly lower than that of VMAT-SBRT. The dose of sCIRT was significantly lower than that of VMAT-SBRT at low volumes in the lung, heart, spinal cord, and esophagus. Conclusion The dose distribution of sCIRT for early-stage NSCLC was better than that of VMAT-SBRT.

The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through automated planning method; however, few studies have used the deep learning method for optimization of planning. The purpose of this study was to propose an automated method based on a convolutional neural network (CNN) for predicting the dosimetric eligibility of patients with prostate cancer undergoing IMRT. Sixty patients with prostate cancer who underwent IMRT were included in the study. Treatment strategy involved division of the patients into two groups, namely, meeting all dose constraints and not meeting all dose constraints, by experienced medical physicists. We used AlexNet (i.e., one of common CNN architectures) for CNN-based methods to predict the two groups. An AlexNet CNN pre-trained on ImageNet was fine-tuned. Two dataset formats were used as input data: planning computed tomography (CT) images and structure labels. Five-fold cross-validation was used, and performance metrics included sensitivity, specificity, and prediction accuracy. Class activation mapping was used to visualize the internal representation learned by the CNN. Prediction accuracies of the model with the planning CT image dataset and that with the structure label dataset were 56.7 ± 9.7% and 70.0 ± 11.3%, respectively. Moreover, the model with structure labels focused on areas associated with dose constraints. These results revealed the potential applicability of deep learning to the treatment planning of patients with prostate cancer undergoing IMRT.

We clarified the reconstructed 3D dose difference between two different commercial software programs (Mobius3D v2.0 and PerFRACTION v1.6.4). Five prostate cancer patients treated with IMRT (74 Gy/37 Fr) were studied. Log files and cine EPID images were acquired for each fraction. 3D patient dose was reconstructed using log files (Mobius3D) or log files with EPID imaging (PerFRACTION). The treatment planning dose was re-calculated on homogeneous and heterogeneous phantoms, and log files and cine EPID images were acquired. Measured doses were compared with the reconstructed point doses in the phantom. Next, we compared dosimetric metrics (mean dose for PTV, rectum, and bladder) calculated by Mobius3D and PerFRACTION for all fractions from five patients. Dose difference at isocenter between measurement and reconstructed dose for two software programs was within 3.0% in both homogeneous and heterogeneous phantoms. Moreover, the dose difference was larger using skip arc plan than that using full arc plan, especially for PerFRACTION (e.g., dose difference at isocenter for PerFRACTION: 0.34% for full arc plan vs. -4.50% for skip arc plan in patient 1). For patients, differences in dosimetric parameters were within 1% for almost all fractions. PerFRACTION had wider range of dose difference between first fraction and the other fractions than Mobius3D (e.g., maximum difference: 0.50% for Mobius3D vs. 1.85% for PerFRACTION), possibly because EPID may detect some types of MLC positioning errors such as miscalibration errors or mechanical backlash which cannot be detected by log files, or that EPID data might include image acquisition failure and image noise.

Deformable image registration (DIR) is important in dose accumulation. Currently, the impact of DIR-algorithm-associated uncertainties in proton therapy is unclear. Here, we quantify the effect of DIR uncertainties on prostate passive-scattering proton therapy (PSPT) dose accumulation. Ten patients with an intermediate risk for prostate cancer formerly treated by PSPT (PTV D95=78GyE) were studied. Dose distributions from all verification CT images (five images per patient) were warped and accumulated in the planning CT geometries with DIR. The dose-volume histogram parameters (Dmean, V40, and V70) for rectum and bladder were calculated. Two commercially available DIR software packages were employed: Velocity AI (Varian Medical Systems) and RayStation (RaySearch Laboratories). The dice similarity coefficient (DSC) and surface distance, which were calculated between planning CT contours and deformed contours, were used for DIR validation, with the relationship between the dose parameter and DIR uncertainty ultimately investigated. On average, when using RayStation, the DSC increased by 0.14 and surface distance decreased by 6.4mm, as compared to Velocity. For Dmean, V40, and V70 to the rectum, the average differences between the RayStation and Velocity were 3.9GyE, 5.5%, and 3.2%, respectively. For the bladder, the differences were 5.2GyE, 5.8%, and 5.4%, respectively. The maximum differences in V40 between RayStation and Velocity were 14.4% and 22.8% for the rectum and bladder, respectively, when the average DSC and surface distance differences were more than 0.14 and 6.4mm, respectively. Such results suggest that DIR uncertainties might significantly affect prostate PSPT dose accumulations.

We quantified intra-fractional dose variation and organ movement during CT-based 3D-image guided brachytherapy (3D-IGBT) in cervical cancer patients. Fifteen patients who underwent CT-based 3D-IGBT were studied. For all patients, pre-delivery CT for treatment planning after applicator insertion and post-delivery CT after dose delivery without changing the applicator position were acquired. Pre- and post-delivery CT were rigidly fused by matching the inserted applicator and planned dose on pre-delivery CT (pre-delivery dose) was mapped on post-delivery CT (post-delivery dose). D2, D1, and D0.1 cm3 of the rectum and bladder were compared between pre- and post-delivery doses with contours on each CT image. Organ movement and deformation was evaluated using deformation vector fields calculated by deformable image registration between pre- and post-delivery CT. We also evaluated dose variation and DVF between with and without a catheter to control filling. Differences in all DVH parameters were 15% dose difference was found in 13.8% of the fractions in rectum D2 cm3 and in 11.1% of those in bladder D2 cm3. The mean value of DVF for bladder was larger than that of rectum, especially for the superior-inferior (S-I) direction. Insertion catheters in bladder reduced mean dose and DVF variation compared with that of without catheters. In fraction groups with large dose increasing, DVF in the S-I direction was significantly larger than that of other fraction groups. Our results indicated that preparation is needed to reduce changes in the S-I direction affect dose variation.

The purpose of this study was to evaluate and compare the dosimetric effects of HyperArc-based stereotactic radiosurgery (SRS) and a robotic radiosurgery system-based planning using CyberKnife for multiple cranial metastases. In total, 11 cancer patients with multiple cranial metastases (3 to 5 tumors) treated with CyberKnife were examined. These patients were replanned using HyperArc (Varian Medical Systems, Palo Alto, USA). HyperArc plan were designed using 4 noncoplanar arc single-isocenter VMAT in 6 MV flattening filter free mode for simulated delivery with the True beam STx (Varian). The prescription dose was 23 Gy at single fraction. Dosimetric differences and blinded clinician scoring differences were evaluated. Conformity index (CI) and gradient index (GI) were 0.60 ± 0.11 and 3.94 ± 0.74, respectively, for the CyberKnife plan and 0.87 ± 0.08 and 5.31 ± 1.42, respectively, for the HyperArc plan (p < 0.05). Total brain V12-gross tumor volumes (GTVs) for the CyberKnife and HyperArc plans were 5.26 ± 2.83 and 4.02 ± 1.71 cm3, respectively. These results indicate that HyperArc plan showed better CI and total brain V12-GTV, while CyberKnife plan showed better GI. A blinded physician scoring evaluation did not show significant differences between CyberKnife and HyperArc plans. The HyperArc-based SRS plan is comparable with the CyberKnife plan, suggesting a greater potential to emerge as a suitable tool for SRS of multiple brain metastases.

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

This study aimed to evaluate the performance of the hybrid deformable image registration (DIR) method in comparison with intensity-based DIR for pelvic cone-beam computed tomography (CBCT) images, using intensity and anatomical information. Ten prostate cancer patients treated with intensity-modulated radiation therapy (IMRT) were studied. Nine or ten CBCT scans were performed for each patient. First, rigid registration was performed between the planning CT and all CBCT images using gold fiducial markers, and then DIR was performed. The Dice similarity coefficient (DSC) and center of mass (COM) displacement were used to evaluate the quantitative DIR accuracy. The average DSCs for intensity-based DIR for the prostate, rectum, bladder, and seminal vesicles were 0.84 ± 0.05, 0.75 ± 0.05, 0.69 ± 0.07 and 0.65 ± 0.11, respectively, whereas those values for hybrid DIR were 0.98 ± 0.00, 0.97 ± 0.01, 0.98 ± 0.00 and 0.94 ± 0.03, respectively (P