Development of a phantom for assessing the precision of setup in skin mark-less surface-guided radiotherapy.

BACKGROUND: Surface-guided radiotherapy (SGRT) is adopted by several institutions; however, reports on the phantoms used to assess the precision of the SGRT setup are limited. PURPOSE: The purpose of this study was to develop a phantom to verify the accuracy of the irradiation position during skin mark-less SGRT. METHODS: An acrylonitrile butadiene styrene (ABS) plastic cube phantom with a diameter of 150 mm on each side containing a dummy target of 15 mm and two types of body surface-shaped phantoms (breast/face shape) that could be attached to the cube phantom were fabricated. Films can be inserted on four sides of the cubic phantom (left, right, anterior and posterior), and the center of radiation can be calculated by irradiating the dummy target with orthogonal MV beams. Three types of SGRT using a VOXELAN-HEV600M (Electronics Research&Development Corporation, Okayama, Japan) were evaluated using this phantom: (i) SGRTCT-a SGRT set-up based solely on a computed tomography (CT)-reference image. (ii) SGRTCT + CBCT-a method where cone beam computed tomography (CBCT) matching was performed after SGRTCT. (iii) SGRTScan-a resetup technique using a scan reference image obtained after completing the (ii) step. RESULTS: Both the breast and face phantoms were recognized in the SGRT system without problems. SGRTScan ensure precision within 1 mm/1° for breast and face verification, respectively. All SGRT methods showed comparable rotational accuracies with no significant disparities. CONCLUSIONS: The developed phantom was useful for verifying the accuracy of skin mark-less SGRT position matching. The SGRTScan demonstrated the feasibility of achieving skin-mark less SGRT with high accuracy, with deviations of less than 1 mm. Additional research is necessary to evaluate the suitability of the developed phantoms for use in various facilities and systems. This phantom could be used for postal surveys in the future.

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT. METHODS: Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. RESULTS: The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. CONCLUSIONS: We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.

Evaluation of a New Method for CyberKnife Treatment for Central Lung and Mediastinal Tumors by Tracheobronchial Tracking.

BACKGROUND: CyberKnife treatment for central lung tumors and mediastinal tumors can be difficult to perform with marker less. PURPOSE: We aimed to evaluate a novel tracheobronchial-based method (ie, tracheobronchial tracking) for the purpose of minimally invasive CyberKnife treatment for central lung and mediastinal tumors. METHODS: Five verification plans were created using an in-house phantom. Each plan included five irradiation sessions. The reference plan irradiated and tracked the simulated tumor (using the target tracking volume, TTV). Trachea plans tracked the simulated tracheo-bronchus and irradiated the simulated tumor and included two types of subplans: correlated plans in which the displacement of the simulated tracheobronchial and the simulated tumor were correlated, and non-correlated plans in which these factors were not correlated. Moreover, 15 mm and 25 mm TTVs were evaluated for each plan. The sin waveform and the patient's respiratory waveform were prepared as the respiratory model. Evaluations were performed by calculating the dose difference between the radiophotoluminescent glass dosimeter (RPLD)-generated mean dose values (generated by the treatment planning system, TPS) and the actual absorbed RPLD dose. Statistical analyses were performed to evaluate findings for each plan. Correlation and prediction errors were calculated for each axis of each plan using log files to evaluate tracking accuracy. RESULTS: Dose differences were statistically significant only in comparisons with the non-correlated plan. When evaluated using the sin waveform, the mean values for correlation and prediction errors in each axis and for all plans were less than 0.6 mm and 0.1 mm, respectively. In the same manner, they were less than 1.1 mm and 0.2 mm when evaluated using the patient's respiratory waveform. CONCLUSION: Our newly-developed tracheobronchial tracking method would be useful in facilitating minimally invasive CyberKnife treatment in certain cases of central lung and mediastinal tumors.

Cycle-generative adversarial network-based bone suppression imaging for highly accurate markerless motion tracking of lung tumors for cyberknife irradiation therapy.

PURPOSE: Lung tumor tracking during stereotactic radiotherapy with the CyberKnife can misrecognize tumor location under conditions where similar patterns exist in the search area. This study aimed to develop a technique for bone signal suppression during kV-x-ray imaging. METHODS: Paired CT images were created with or without bony structures using a 4D extended cardiac-torso phantom (XCAT phantom) in 56 cases. Subsequently, 3020 2D x-ray images were generated. Images with bone were input into cycle-consistent adversarial network (CycleGAN) and the bone suppressed images on the XCAT phantom (BSIphantom ) were created. They were then compared to images without bone using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Next, 1000 non-simulated treatment images from real cases were input into the training model, and bone-suppressed images of the patient (BSIpatient ) were created. Zero means normalized cross correlation (ZNCC) by template matching between each of the actual treatment images and BSIpatient were calculated. RESULTS: BSIphantom values were compared to their paired images without bone of the XCAT phantom test data; SSIM and PSNR were 0.90 ± 0.06 and 24.54 ± 4.48, respectively. It was visually confirmed that only bone was selectively suppressed without significantly affecting tumor visualization. The ZNCC values of the actual treatment images and BSIpatient were 0.763 ± 0.136 and 0.773 ± 0.143, respectively. The BSIpatient showed improved recognition accuracy over the actual treatment images. CONCLUSIONS: The proposed bone suppression imaging technique based on CycleGAN improves image recognition, making it possible to achieve highly accurate motion tracking irradiation.

Current status of remote radiotherapy treatment planning in Japan: findings from a national survey†.

The purpose of this study was to investigate the status of remote-radiotherapy treatment planning (RRTP) in Japan through a nationwide questionnaire survey. The survey was conducted between 29 June and 4 August 2022, at 834 facilities in Japan that were equipped with linear accelerators. The survey utilized a Google form that comprised 96 questions on facility information, information about the respondent, utilization of RRTP between facilities, usage for telework and the inclination to implement RRTPs in the respondent's facility. The survey analyzed the utilization of the RRTP system in four distinct implementation types: (i) utilization as a supportive facility, (ii) utilization as a treatment facility, (iii) utilization as a teleworker outside of the facility and (iv) utilization as a teleworker within the facility. The survey response rate was 58.4% (487 facilities responded). Among the facilities that responded, 10% (51 facilities) were implementing RRTP. 13 served as supportive facilities, 23 as treatment facilities, 17 as teleworkers outside of the facility and 5 as teleworkers within the facility. In terms of system usage between supportive and treatment facilities, 70-80% of the participants utilized the system for emergencies or as overtime work for external workers. A substantial number of facilities (38.8%) reported that they were unfamiliar with RRTP implementation. The survey showed that RRTP utilization in Japan is still limited, with a significant number of facilities unfamiliar with the technology. The study highlights the need for greater understanding and education about RRTP and financial funds of economical compensation.

Dosimetric comparison of five different radiotherapy treatment planning approaches for locally advanced non-small cell lung cancer with sequential plan changes.

BACKGROUND: The purpose of this study was to compare the dosimetric characteristics of five different treatment planning techniques for locally advanced non-small cell lung cancer (LA-NSCLC) with sequential plan changes. METHODS: A total of 13 stage III NSCLC patients were enrolled in this study. These patients had both computed tomography (CT) images for initial and boost treatment plans. The latter CT images were taken if tumor shrinkage was observed after 2 weeks of treatment. The prescription dose was 60 Gy/30 Fr (initial: 40 Gy/20 Fr, and boost: 20 Gy/10 Fr). Five techniques (forward-planed 3-dimensional conformal radiotherapy [F-3DCRT] on both CT images, inverse-planned 3DCRT [I-3DCRT] on both CT images, volumetric modulated arc therapy [VMAT] on both CT images, F-3DCRT on initial CT plus VMAT on boost CT [bVMAT], and hybrid of fixed intensity-modulated radiotherapy [IMRT] beams and VMAT beams on both CT images [hybrid]) were recalculated for all patients. The accumulated doses between initial and boost plans were compared among all treatment techniques. RESULTS: The conformity indexes (CI) of the planning target volume (PTV) of the five planning techniques were 0.34 ± 0.10, 0.57 ± 0.10, 0.86 ± 0.08, 0.61 ± 0.12, and 0.83 ± 0.11 for F-3DCRT, I-3DCRT, VMAT, bVMAT, and hybrid, respectively. In the same manner, lung volumes receiving >20 Gy (V20Gy ) were 21.05 ± 10.56%, 20.86 ± 6.45, 19.50 ± 7.38%, 19.98 ± 10.04%, and 17.74 ± 7.86%. There was significant improvement about CI and V20Gy for hybrid compared with F-3DCRT (p < 0.05). CONCLUSION: The IMRT/VMAT hybrid technique for LA-NSCLC patients improved target CI and reduced lung doses. Furthermore, if IMRT was not available initially, starting with 3DCRT might be beneficial as demonstrated in the bVMAT procedure of this study.

Evaluation of computed tomography metal artifact and CyberKnife fiducial recognition for novel size fiducial markers.

PURPOSE: This study aimed to compare fiducial markers used in CyberKnife treatment in terms of metal artifact intensity observed in CT images and fiducial recognition in the CyberKnife system affected by patient body thickness and type of marker. METHODS: Five markers, ACCULOC 0.9 mm × 3 mm, Ball type Gold Anchor (GA) 0.28 mm × 10 mm, 0.28 mm × 20 mm, and novel size GA 0.4 mm × 10 mm, 0.4 mm × 20 mm were evaluated. To evaluate metal artifacts of CT images, two types of CT images of water-equivalent gels with each marker were acquired using Aquilion LB CT scanner, one applied SEMAR (SEMAR-on) and the other did not apply this technique (SEMAR-off). The evaluation metric of artifact intensity (MSD ) which represents a variation of CT values were compared for each marker. Next, 5, 15, and 20 cm thickness of Tough Water (TW) was placed on the gel under the condition of overlapping the vertebral phantom in the Target Locating System, and the live image of each marker was acquired to compare fiducial recognition. RESULTS: The mean MSD of SEMAR-off was 78.80, 74.50, 97.25, 83.29, and 149.64 HU for ACCULOC, GA0.28 mm × 10 mm, 20 mm, and 0.40 mm × 10 mm, 20 mm, respectively. In the same manner, that of SEMAR-on was 23.52, 20.26, 26.76, 24.89, and 33.96 HU, respectively. Fiducial recognition decreased in the order of 5, 15, and 20 cm thickness, and GA 0.4 × 20 mm showed the best recognition at thickness of 20 cm TW. CONCLUSIONS: We demonstrated the potential to reduce metal artifacts in the CT image to the same level for all the markers we evaluated by applying SEMAR. Additionally, the fiducial recognition of each marker may vary depending on the thickness of the patient's body. Particularly, we showed that GA 0.40 × 20 mm may have more optimal recognition for CyberKnife treatment in cases of high bodily thickness in comparison to the other markers.

Long-term experience in quality assurance of on-rail computed tomography systems for image-guided radiotherapy using in-house multifunctional phantoms.

To report the long-term quality assurance (QA) experience of an on-rail computed tomography (CT) system for image-guided radiotherapy using an in-house phantom. An on-rail CT system combining the Elekta Synergy and Canon Aquilion LB was used. The treatment couch was shared by the linear accelerators and CT, and the couch was rotated by 180° when using the on-rail-CT system to ensure that the CT direction was toward the head. All QA analyses were performed by radiation technologists on CBCT or on-rail CT images of the in-house phantom. The CBCT center accuracy from the linac laser, couch rotational accuracy (CBCT center vs. on-rail CT center), horizontal accuracy by CT gantry shift, and remote couch shift accuracy were evaluated. This study reported the QA status of the system during the period 2014-2021. The absolute mean accuracy of couch rotation was 0.4 ± 0.28 mm, 0.44 ± 0.36 mm, and 0.37 ± 0.27 mm in the SI, RL, and AP directions, respectively. Horizontal and remote movement accuracies of the treatment couch were also within 0.5 mm of the absolute mean value. A decrease in the accuracy of couch rotation was also observed due to aging deterioration of related parts caused by the frequent use of couch rotation. The three-dimensional accuracy of on-rail CT systems derived mainly from treatment couches can be maintained within 0.5 mm with appropriate accuracy assurance for at least > 8 years.

Development of a dynamic motion platform with two independent drive systems for radiotherapy.

BACKGROUND: There are some motion platforms for radiotherapy quality assurance. However, no platform with two drive systems that can move along three axes is available. PURPOSE: The purpose of this study is to develop a dynamic motion platform with two drive systems capable of three-axis motion and to evaluate its motion performance. METHODS: The developed moving platform had two drive systems that use the same equipment. Each axis of the platform used can support a maximum load of 10 kg. The motors for moving the platform in each direction are capable of a drive stroke up to 40 mm. The drive speed is 30 mm/s at maximum load fluctuation. To evaluate the static positional accuracy of this system with an arbitrary input movement, the XYZ position of each axis was measured using a coordinate measuring machine operating from 0 to 40 mm at 10 mm intervals. In addition, the accuracy of dynamic motion was verified with Sine waveform inputs of different patterns to the three axes for approximately 60 s, and they were compared with the resulting detected signals by SyncTrax. RESULTS: The two drive systems were successfully operated on three axes by using independent control systems. For static position, the accuracies were within 0.2 mm, 0.05 mm, and 0.14 mm for lateral, longitudinal, and vertical directions, respectively. For dynamic motion, the mean absolute errors in the X, Y, and Z axes between the platform inputs and SyncTrax detected signals were 0.14 ± 0.10 mm, 0.16 ± 0.12 mm, and 0.16 ± 0.11 mm, respectively. CONCLUSIONS: A new dynamic platform for radiation therapy with two drive systems capable of three-axis motion was developed, and the positional accuracy of the drive axes was confirmed to be less than 0.2 mm.

Evaluation of performance of pelvic CT-MR deformable image registration using two software programs.

We assessed the accuracy of deformable image registration (DIR) accuracy between CT and MR images using an open-source software (Elastix, from Utrecht Medical Center) and a commercial software (Velocity AI Ver. 3.2.0 from Varian Medical Systems, Palo Alto, CA, USA) software. Five male patients' pelvic regions were studied using publicly available CT, T1-weighted (T1w) MR, and T2-weighted (T2w) MR images. In the cost function of the Elastix, we used six DIR parameter settings with different regularization weights (Elastix0, Elastix0.01, Elastix0.1, Elastix1, Elastix10, and Elastix100). We used MR Corrected Deformable algorithm for Velocity AI. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) for the prostate, bladder, rectum and left and right femoral heads were used to evaluate DIR accuracy. Except for the bladder, most algorithms produced good DSC and MDA results for all organs. In our study, the mean DSCs for the bladder ranged from 0.75 to 0.88 (CT-T1w) and from 0.72 to 0.76 (CT-T2w). Similarly, the mean MDA ranges were 2.4 to 4.9 mm (CT-T1w), 4.6 to 5.3 mm (CT-T2w). For the Elastix, CT-T1w was outperformed CT-T2w for both DSCs and MDAs at Elastix0, Elastix0.01, and Elastix0.1. In the case of Velocity AI, no significant differences in DSC and MDA of all organs were observed. This implied that the DIR accuracy of CT and MR images might differ depending on the sequence used.

Evaluation of the dosimetric impact of heart function-based volumetric modulated arc therapy planning in patients with esophageal cancer.

Radiotherapy for esophageal cancer entails high-dose irradiation of the myocardium owing to its close anatomical proximity to the esophagus. This study aimed to evaluate the dosimetric impact of functional avoidance planning for the myocardium with volumetric-modulated arc therapy (VMAT) in patients with esophageal cancer and determine the feasibility of functional planning. Ten patients with early stage esophageal cancer were included in this study. The prescribed dose was 60 Gy administered in 30 fractions. An experienced physician contoured the left ventricle (LV) of the myocardium. For each patient, an anatomical plan (non-LV-sparing plan) and a functional plan (LV-sparing plan) were created using the VMAT. In the functional plan, the mean percentage of LV volume receiving a dose of ≥ 30 and ≥ 40 Gy was 6.0% ± 6.7% and 2.4% ± 2.7%, respectively, whereas in the anatomical plan, they were 11.7% ± 13.1% and 4.9% ± 6.5%, respectively. There were no significant differences with respect to the dose to the hottest 1 cm3 of the planning target volume (PTV) and the minimum dose of the gross tumor volume and the dosimetric parameters of other normal tissues between the anatomical and functional plans. We compared the anatomical and functional plans of patients with esophageal cancer undergoing VMAT. Our results demonstrated that the functional plan reduced the dose to the LV with no significant differences in the organs at risk and PTV, indicating that avoidance planning can be safely performed when administering VMAT in patients with esophageal cancer.

Dose distribution correction for the influence of magnetic field using a deep convolutional neural network for online MR-guided adaptive radiotherapy.

PURPOSE: This study aimed to develop a deep convolutional neural network (CNN)-based dose distribution conversion approach for the correction of the influence of a magnetic field for online MR-guided adaptive radiotherapy. METHODS: Our model is based on DenseNet and consists of two 2D input channels and one 2D output channel. These three types of data comprise dose distributions without a magnetic field (uncorrected), electron density (ED) maps, and dose distributions with a magnetic field. These data were generated as follows: both types of dose distributions were created using 15-field IMRT in the same conditions except for the presence or absence of a magnetic field with the GPU Monte Carlo dose in Monaco version 5.4; ED maps were acquired with planning CT images using a clinical CT-to-ED table at our institution. Data for 50 prostate cancer patients were used; 30 patients were allocated for training, 10 for validation, and 10 for testing using 4-fold cross-validation based on rectum gas volume. The accuracy of the model was evaluated by comparing 2D gamma-indexes against the dose distributions in each irradiation field with a magnetic field (true). RESULTS: The gamma indexes in the body for CNN-corrected uncorrected dose against the true dose were 94.95% ± 4.69% and 63.19% ± 3.63%, respectively. The gamma indexes with 2%/2-mm criteria were improved by 10% in most test cases (99.36%). CONCLUSIONS: Our results suggest that the CNN-based approach can be used to correct the dose-distribution influences with a magnetic field in prostate cancer treatment.

Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom.

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VICBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VICBCT- and 4D-CT-based ventilation images (VICT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VICBCT. Correlations between the VICBCT and VICT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VICBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VICBCT was greater or equal to that of the HU-based VICBCT. In addition, the ventilation accuracy of VICBCT increased at low gantry speeds. Thus, the image quality of VICBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VICBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VICBCT imaging into the functional avoidance planning process.

Evaluation of a 3D-printed heterogeneous anthropomorphic head and neck phantom for patient-specific quality assurance in intensity-modulated radiation therapy.

We evaluated an anthropomorphic head and neck phantom with tissue heterogeneity, produced using a personal 3D printer, with quality assurance (QA), specific to patients undergoing intensity-modulated radiation therapy (IMRT). Using semi-automatic segmentation, 3D models of bone, soft tissue, and an air-filled cavity were created based on computed tomography (CT) images from patients with head and neck cancer treated with IMRT. For the 3D printer settings, polylactide was used for soft tissue with 100% infill. Bone was reproduced by pouring plaster into the cavity created by the 3D printer. The average CT values for soft tissue and bone were 13.0 ± 144.3 HU and 439.5 ± 137.0 HU, respectively, for the phantom and 12.1 ± 124.5 HU and 771.5 ± 405.3 HU, respectively, for the patient. The gamma passing rate (3%/3 mm) was 96.1% for a nine-field IMRT plan. Thus, this phantom may be used instead of a standard shape phantom for patient-specific QA in IMRT.

A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients.

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.