Tumor-tracking Radiotherapy Research Articles

Application of motion prediction based on a long short-term memory network for imaging dose reduction in real-time tumor-tracking radiation therapy

PurposeTo demonstrate the possibility of using a lower imaging rate while maintaining acceptable accuracy by applying motion prediction to minimize the imaging dose in real-time image-guided radiation therapy. MethodsTime-series of three-dimensional internal marker positions obtained from 98 patients in liver stereotactic body radiation therapy were used to train and test the long-short-term memory (LSTM) network. For real-time imaging, the root mean squared error (RMSE) of the prediction on three-dimensional marker position made by LSTM, the residual motion of the target under respiratory-gated irradiation, and irradiation efficiency were evaluated. In the evaluation of the residual motion, the system-specific latency was assumed to be 100 ms. ResultsExcept for outliers in the superior–inferior (SI) direction, the median/maximum values of the RMSE for imaging rates of 7.5, 5.0, and 2.5 frames per second (fps) were 0.8/1.3, 0.9/1.6, and 1.2/2.4 mm, respectively. The median/maximum residual motion in the SI direction at an imaging rate of 15.0 fps without prediction of the marker position, which is a typical clinical setting, was 2.3/3.6 mm. For rates of 7.5, 5.0, and 2.5 fps with prediction, the corresponding values were 2.0/2.6, 2.2/3.3, and 2.4/3.9 mm, respectively. There was no significant difference between the irradiation efficiency with and that without prediction of the marker position. The geometrical accuracy at lower frame rates with prediction applied was superior or comparable to that at 15 fps without prediction. In comparison with the current clinical setting for real-time image-guided radiation therapy, which uses an imaging rate of 15.0 fps without prediction, it may be possible to reduce the imaging dose by half or more. ConclusionsMotion prediction can effectively lower the frame rate and minimize the imaging dose in real-time image-guided radiation therapy.

Deep learning-based target decomposition for markerless lung tumor tracking in radiotherapy.

In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential. To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition. We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8±0.7mm in the superior-inferior (SI) direction and 0.9±0.8mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12h to train. During testing, it took approximately 35ms to generate one sDTI, and 13ms to perform the tumor tracking using template matching. Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.

Deep inspiration breath hold real-time tumor-tracking radiation therapy (DBRT) as a novel stereotactic body radiation therapy approach for lung tumors

Radiotherapy with deep inspiration breath hold (DIBH) reduces doses to the lungs and organs at risk. The stability of breath holding and reproducibility of tumor location are higher during expiration than during inspiration; therefore, we developed an irradiation method combining DIBH and real-time tumor-tracking radiotherapy (RTRT) (DBRT). Nine patients were enrolled in this study. Fiducial markers were placed near tumors using bronchoscopy. Treatment planning computed tomography (CT) was performed thrice during DIBH, assisted by spirometer-based device. Each CT scan was fused using fiducial markers. Gross tumor volume (GTV) was contoured for each dataset and summed to create GTVsum; adding a 5-mm margin around GTVsum generated the planning target volume. The prescribed dose was mainly 42 Gy in four fractions. The treatment plan was created using DIBH CT (DBRT-plan), with a similar treatment plan created for expiratory CT for cases for which DBRT could not be performed (conv-plan). Vx defined as the volume of the lung received x Gy, and the mean lung dose, V20, V10, and V5 were evaluated. DBRT was completed in all patients. Mean dose, V20, and V10 were significantly lower in the DBRT-plan than in the conv-plan (all p = 0.003). Mean rates of decrease for mean dose, V20, and V10 were 14.0%, 27.6%, and 19.1%, respectively. No significant difference was observed in V5. We developed DBRT, a stereotactic body radiation therapy performed with the DIBH technique; it combines a spirometer-based breath-hold support system with an RTRT system. All patients who underwent DBRT completed the procedure without any technical or mechanical complications. This is a promising methodology that may significantly reduce lung doses.

Markerless dynamic tumor tracking (MDTT) radiotherapy using diaphragm as a surrogate for liver targets.

To assess the feasibility of using the diaphragm as a surrogate for liver targets during MDTT. Diaphragm as surrogate for markers: a dome-shaped phantom with implanted markers was fabricated and underwent dual-orthogonal fluoroscopy sequences on the Vero4DRT linac. Ten patients participated in an IRB-approved, feasibility study to assess the MDTT workflow. All images were analyzed using an in-house program to back-project the diaphragm/markers position to the isocenter plane. ExacTrac imager log files were analyzed. Diaphragm as tracking structure for MDTT: The phantom "diaphragm" was contoured as a markerless tracking structure (MTS) and exported to Vero4DRT/ExacTrac. A single field plan was delivered to the phantom film plane under static and MDTT conditions. In the patient study, the diaphragm tracking structure was contoured on CT breath-hold-exhale datasets. The MDTT workflow was applied until just prior to MV beam-on. Diaphragm as surrogate for markers: phantom data confirmed the in-house 3D back-projection program was functioning as intended. In patients, the diaphragm/marker relative positions had a mean±RMS difference of 0.70±0.89, 1.08±1.26, and 0.96±1.06mm in ML, SI, and AP directions. Diaphragm as tracking structure for MDTT: Building a respiratory-correlation model using the diaphragm as surrogate for the implanted markers was successful in phantom/patients. During the tracking verification imaging step, the phantom mean±SD difference between the image-detected and predicted "diaphragm" position was 0.52±0.18mm. The 2D film gamma (2%/2mm) comparison (static to MDTT deliveries) was 98.2%. In patients, the mean difference between the image-detected and predicted diaphragm position was 2.02±0.92mm. The planning target margin contribution from MDTT diaphragm tracking is 2.2, 5.0, and 4.7mm in the ML, SI, and AP directions. In phantom/patients, the diaphragm motion correlated well with markers' motion and could be used as a surrogate. MDTT workflows using the diaphragm as the MTS is feasible using the Vero4DRT linac and could replace the need for implanted markers for liver radiotherapy.

Deep-Inspiration Breath-Hold Stereotactic Body Radiation Therapy by Combining Spirometer-Guided Breath-Hold and a Real-Time Tumor Tracking System: A Novel Approach

There are several methods used against respiratory motion (RM). Expiratory breath-hold (BH) is considered more stable and reproducible than inspiratory BH; therefore, BH with spirometry is often used for expiration. The real-time tumor tracking radiotherapy (RTRT) system is a highly effective method for reducing the margin of RM. This system ambushes and irradiates tumors during the expiratory phase when tumors move slowly. Although these methods usually involve expiration, it is advantageous to expand the lungs with inspiration to reduce the risk of adverse events. Here, we developed a new approach of performing stereotactic body radiation therapy (SBRT) under deep-inspiration BH (DIBH) by combining these two methods. Lung tumors with respiratory motion ≥ 1 cm were included. Three or four fiducial markers were placed near the tumor via bronchoscopy. DIBH CT (CT-IN) was performed under the guidance of spirometer. The PTV was obtained by adding a 5-mm margin to the GTV delineated on CT-IN. The prescribed dose was 42 Gy in four fractions for the D95 of the PTV. An error of 2.0 mm around the planned position of the fiducial marker on CT-IN was permitted along each orthogonal axis as a gating box. In preparation for cases in which the reproducibility of DIBH is low and treatment cannot be performed, light expiration BH CT (CT-EX) was also performed, and a radiotherapy plan was prepared for the conventional RTRT system so that it could be switched at any time. Lung volumes and doses (mean dose, V20 Gy, V10 Gy, and V5 Gy) on CT-EX and CT-IN were compared. Five patients underwent SBRT with DIBH, and all completed the planned irradiation course. The median treatment time per fraction was 27.86 min (range, 25.5-40.6). Four tumors were located in the left lower lobe and one in the right lower lobe. The median volume of PTV was 12.4 (range, 5.2-26.2) mL. The lung volumes and doses on CT-EX and CT-IN are shown in the Table. The lung volume on CT-IN was 1.6 times larger than that on CT-EX. The PTV-to-lung ratio on CT-IN was significantly lower than that on CT-EX. V20 Gy and V10 Gy on CT-IN were significantly lower than those on CT-EX. SBRT with DIBH was achieved by combining the spirometer and RTRT system. This can help to eliminate concerns about reproducibility and high-speed tumor movement during inspiration, which are weaknesses of spirometer-guided breath-hold and the RTRT system, respectively, while ensuring the accuracy of the RTRT system. DIBH SBRT is a promising method that can reduce lung dose.

Comparison of dynamic tumor tracking error measurement methods for robotic radiosurgery.

Dynamic tumor motion tracking is used in robotic radiosurgery for targets subject to respiratory motion, such as lung and liver cancers. Different methods of measuring tracking error have been reported, but the differences among these methods have not been studied, and the optimal method is unknown. The purpose of this study was to assess and compare tracking errors encountered with individual patients using different evaluation methods for method optimization. We compared the beam's eye view (BEV), machine learning (ML), log (addition error: AE), and log (root sum square: RSS) methods. Log (AE) and log (RSS) were calculated from log files. These tracking errors were compared, and the optimal evaluation method was ascertained. A t-test was performed to evaluate statistically significant differences. Here, the significance level was set at 5%. The mean values of BEV, log (AE), log (RSS), and ML were 2.87, 3.91, 2.91, and 3.74mm, respectively. The log (AE) and ML were higher than BEV (p<0.001), and log (RSS) was equivalent to the BEV, suggesting that the log (RSS) calculated with the log file method can substitute for the BEV calculated with the BEV method. As RSS error calculation is simpler than BEV calculation, using it may improve clinical practice throughput. This study clarified differences among three tracking error evaluation methods for dynamic tumor tracking radiotherapy using a robotic radiosurgery system. The log (RSS) calculated by the log file method was found to be the best alternative to BEV method, as it can calculate tracking errors more easily than the BEV method.

Fixation rate of fiducial gold markers in each segmental bronchus for stereotactic body radiotherapy

BackgroundIn real-time tumor-tracking radiotherapy for lung tumors, placement of fiducials, such as a gold marker (GM), is necessary. However, transbronchially placed GMs often drop out spontaneously. Therefore, we aimed to clarify the fixation rate of GMs in each segmental bronchus. MethodsIn this study, we examined the fixation rates of 791 GMs placed in 235 patients (259 procedures), from November 2011 to November 2020, at a single facility. The relationship between the elapsed time and the fixation rate was assessed using the Kaplan–Meier method. Moreover, we measured the distance between the GM and the chest wall (DMC), as confirmed using planning computed tomography, and analyzed the relationship of this distance with the fixation rate. ResultsOverall, 28.8% of GMs dropped out within 10 days of placement. The fixation rate of GMs in the bronchi was significantly lower in the upper lobe than in the other lobes, in both lungs (right, p < 0.01; left, p = 0.05). Moreover, in the left upper lobe, the fixation rate of GMs was significantly lower in B1+2 than in B3 (p = 0.0181). In addition, the group with a short DMC had a significantly higher GM fixation rate. (p < 0.01). ConclusionsThe fixation rate of GMs was lower in the upper lobes than in the lower lobes. Additionally, the fixation rate was lower in B1+2 than in B3. Shorter DMCs were associated with higher GM fixation rates.

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

CG-DRR：digital reconstructed radiograph generation algorithm based on Cycle-GAN

Development of a high‐resolution two‐dimensional detector‐based dose verification system for tumor‐tracking irradiation in the CyberKnife system

We aim to evaluate the basic characteristics of SRS MapCHECK (SRSMC) for CyberKnife (CK) and establish a dose verification system using SRSMC for the tumor‐tracking irradiation for CK. The field size and angular dependence of SRSMC were evaluated for basic characterization. The output factors (OPFs) and absolute doses measured by SRSMC were compared with those measured using microDiamond and microchamber detectors and those calculated by the treatment planning system (TPS). The angular dependence was evaluated by comparing the SRSMC with a microchamber. The tumor‐tracking dose verification system consists of SRSMC and a moving platform. The doses measured using SRSMC were compared with the doses measured using a microchamber and radiochromic film. The OPFs and absolute doses of SRSMC were within ±3.0% error for almost all field sizes, and the angular dependence was within ±2.0% for all incidence angles. The absolute dose errors between SRSMC and TPS tended to increase when the field size was smaller than 10 mm. The absolute doses of the tumor‐tracking irradiation measured using SRSMC and those measured using a microchamber agreed within 1.0%, and the gamma pass rates of SRSMC in comparison with those of the radiochromic film were greater than 95%. The basic characteristics of SRSMC for CK presented acceptable results for clinical use. The results of the tumor‐tracking dose verification system realized using SRSMC were equivalent to those of conventional methods, and this system is expected to contribute toward improving the efficiency of quality control in many facilities.

Improving the accuracy of motion quantification using area detector computed tomography for real-time tumor-tracking irradiation in stereotactic ablative radiotherapy.

CyberKnife radiotherapy enables tumor-tracking irradiation using positional information regarding the tumor and a fiducial marker in a patient's body. This positional information acts as a surrogate of tumor motion. Therefore, deviations in these movements should be quantitatively estimated and included as an internal margin for radiation treatment planning. This study aimed to investigate variations between the positions of fiducial markers and tumor regions using 320-row area detector computed tomography and to analyze the effectiveness of our proposed method in contouring tumor regions based on the fiducial marker position. To determine the moving tumor volume, a typical single-phase image was selected, and pixel values in other phase images were accumulated. Moreover, a maximum-intensity projection image was created to clarify motion deviations in the tumor volume. To evaluate the delineation accuracy, the dice similarity coefficient and mean distance to agreement were calculated in phase-selected and breath-holding computed tomography. Moving chest phantom images were acquired using helical scanning 4-dimensional computed tomography (H-4DCT) and volumetric scanning 4-dimensional computed tomography (V-4DCT), and the delineation accuracies were compared for each scanning type. The average dice similarity coefficient and mean distance to agreement were degraded in limited-phase images, which cannot represent the hysteretic motion of a tumor. Moreover, deviations in tumor volume with unstable motion reached 71.6% in H-4DCT but only 1.6% in V-4DCT. Our proposed method with V-4DCT using area detector computed tomography can achieve accurate moving tumor delineation and can clarify positional associations between the fiducial marker and tumor under respiratory motion.

Investigation of fiducial marker recognition possibility by water equivalent length in real-time tracking radiotherapy.

Real-time tumor tracking radiotherapy (RTRT) systems typically use fiducial markers implanted near the tumor to track the target using X-ray fluoroscopy. Template pattern matching, used in tracking, is often used to automatically localize the fiducial markers. In radiotherapy of the liver, the thickness of the body that can recognize the fiducial markers must be clinically assessed. The purpose of this study was to quantify the recognition of fiducial markers according to body thickness in stereotactic body radiotherapy of the liver using clinical images obtained using SyncTraX FX4. The recognition scores of fiducial markers were examined in relation to water equivalent length (WEL), tube current, and each flat panel detector. The relationship between the contrast ratio of the fiducial marker and the background and the WEL was also investigated. The average recognition score was found to be less than 20 when the WEL was greater than 25cm. The probability of successful tracking of image recognition was mostly smaller than 0.8 when the WEL was over 30cm. The relationship between WEL and tube current did not significantly differ between 100 and 140mA, but there was a significant difference (p < 0.05) for all other combinations. To ensure tracking of fiducial markers during SBRT, if the WEL representing body thickness is longer than 25cm, the X-ray fluoroscopy arrangement should be determined based on the WEL.

Development of a Margin Determination Framework for Tumor-Tracking Radiation Therapy With Intraoperatively Implanted Fiducial Markers.

PurposeTo develop an internal target volume (ITV) margin determination framework (or decision-supporting framework) for treating multiple lung metastases using CyberKnife Synchrony with intraoperatively implanted fiducial markers (IIFMs). The feasibility of using non-ideally implanted fiducial markers (a limited number and/or far from a target) for tracking-based lung stereotactic ablative radiotherapy (SABR) was investigated.MethodsIn the developed margin determination framework, an optimal set of IIFMs was determined to minimize a tracking uncertainty-specific ITV (ITVtracking) margin (margin required to cover target-to-marker motion discrepancy), i.e., minimize the motion discrepancies between gross tumor volume (GTV) and the selected set of fiducial markers (FMs). The developed margin determination framework was evaluated in 17 patients with lung metastases. To automatically calculate the respiratory motions of the FMs, a template matching-based FM tracking algorithm was developed, and GTV motion was manually measured. Furthermore, during-treatment motions of the selected FMs were analyzed using log files and compared with those calculated using 4D CTs.ResultsFor 41 of 42 lesions in 17 patients (97.6%), an optimal set of the IIFMs was successfully determined, requiring an ITVtracking margin less than 5 mm. The template matching-based FM tracking algorithm calculated the FM motions with a sub-millimeter accuracy compared with the manual measurements. The patient respiratory motions during treatment were, on average, significantly smaller than those measured at simulation for the patient cohort considered.ConclusionUse of the developed margin determination framework employing CyberKnife Synchrony with a limited number of IIFMs is feasible for lung SABR.

Prediction of target position from multiple fiducial markers by partial least squares regression in real-time tumor-tracking radiation therapy.

The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers’ positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior–inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.

Fundamental study on quality assurance (QA) procedures for a real‐time tumor tracking radiotherapy (RTRT) system from the viewpoint of imaging devices

PurposeThe real‐time tumor tracking radiotherapy (RTRT) system requires periodic quality assurance (QA) and quality control. The goal of this study is to propose QA procedures from the viewpoint of imaging devices in the RTRT system.MethodsTracking by the RTRT system (equips two sets of colored image intensifiers (colored I.I.s) fluoroscopy units) for the moving gold‐marker (diameter 2.0 mm) in a rotating phantom were performed under various X‐ray conditions. To analyze the relationship between fluoroscopic image quality and precision of gold marker coordinate calculation, the standard deviation of the 3D coordinate (σ3D [mm]) of the gold marker, the mean of the pattern recognition score (PRS) and the standard deviation of the distance between rays (DBR) (σDBR [mm]) were evaluated.ResultsWhen tracking with speed of 10‐60 mm/s, σDBR increased, though the mean PRS did not change significantly (p>0.05). On the contrary, the mean PRS increased depending on the integral noise equivalent quanta (∫NEQ) that is an indicator of image quality calculated from the modulation transfer function (MTF) as an indicator of spatial resolution and the noise power spectrum (NPS) as an indicator of noise characteristic.ConclusionThe indicators of NEQ, MTF, and NPS were useful for managing the tracking accuracy of the RTRT system. We propose observing the change of these indicators as additional QA procedures for each imaging device from the commissioning baseline.

Treatment outcomes of stereotactic body radiation therapy using a real-time tumor-tracking radiotherapy system for hepatocellular carcinomas.

To report the outcomes of stereotactic body radiotherapy using a real-time tumor-tracking radiotherapy system for hepatocellular carcinoma patients. From January 2005 to July 2018, 63 patients with 74 lesions with a maximum diameter ≤52mm were treated by stereotactic body radiotherapy using a real-time tumor-tracking radiotherapy system. No patient with a Child-Pugh Score ≥9 was included, and 85.6% had a score of 5 or 6. Using the biological effective dose (BED) with an α/β ratio of 10 (BED10 ), the median dose in BED10 at the reference point was 76.8Gy (range 60-122.5Gy). Overall survival (OS) and local control rates were assessed using the Kaplan-Meier method. With a median follow-up period of 24.6months (range 0.9-118.4months), the 1-year and 2-year OS rates were 86.8% (95% confidence interval [95% CI] 75.8-93.3) and 71.1% (57.8-81.6), respectively. The 2-year OS was 89.6% in patients with the baseline modified albumin-bilirubin (mALBI) grade =1, and 61.7% in patients with grade ≥2a. In the multivariate analysis, the mALBI grade (=1 vs. ≥2a) was a significant factor for OS (p=0.028, 95% CI 1.11-6.18). The 1-year and 2-year local control rates were 100% (100-100%) and 92.0% (77.5-97.5%). The local control rates were significantly higher in the BED10 ≥100Gy group than in the BED10 <100Gy group (2-year 100% vs. 86.5%, p=0.049) at the reference point. This retrospective study of stereotactic body radiotherapy using real-time tumor-tracking radiotherapy for hepatocellular carcinoma showed favorable outcomes with lower incidence of toxicities, especially in patients treated with BED10 ≥100Gy to the reference point.

Simulated Four-Dimensional CT for Markerless Tumor Tracking Using Deep Learning Network With Multi-Task Learning

Our markerless tumor tracking algorithm required 4DCT data to train model file. To perform the markerless tracking for respiratory gated treatment even 4DCT is not available, we developed deep neural network (DNN) to predict 4DCT from 3DCT. A total of 2420 thoracic 4DCT data sets (436 patients) was used to train DNN, which was designed to export 9 different deformed vector fields (DVFs) from a single CT data based on the 3D convolutional autoencoder with shortcut connections. However, since our DNN input a single data (CT image) and output 9 different data (DVF), it is not good to use shortcut connection in between encoder block and decoder block like U-net. To solve this problem, shortcut connection was applied to every two convolutional layers (residual block). Since DNN was trained to export 9 DVF files close to those of the ground-truth DVFs, we used the multitask learning technique. DVF from exhale to respective phases was calculated by applying deformable image registration. Then 3DCT data at exhale was transformed by using the predicted DVFs to obtain simulated 4DCT data. We compared markerless tracking accuracy in between original and simulated 4DCT and evaluated for 20 lung patients. Our tracking algorithm uses machine learning approaches. For the training stage, a pair of digitally reconstructed radiography images was calculated by using 4DCT. Training data was divided into subimages around the tumor region (including tumor and not). Subimage feature information was expressed by the intensity gradient and tracking algorithm parameters were optimized by the randomized tree method to calculate tumor likelihood map. Use of tumor positions as a function of respiratory phase designed regression model to calculate a tumor probability estimation map. For the predicting stage, incoming fluoroscopic image was cropped to a subimage, and the tracking algorithm calculated a tumor likelihood map and a tumor probability estimation map. This process was repeated by sliding subimage position on the incoming fluoroscopic images. Then the final tumor position on fluoroscopic image was then estimated by multiplying likelihood map and probability map. Simulated 4DCT for respective phases were successfully generated from 4DCT at T50 and seemed natural close to the original 4DCT. Tracking positional errors averaged over all patients were -.0.07±0.25 mm, -0.04±0.34 mm, and -0.36±0.46 mm for left-right (LR), anterior-posterior (AP), and superior-inferior (SI) direction, respectively. The 95% percentile averaged over all patient was 0.26 mm, 0.36 mm, and 0.22 mm in RL, AP and SI directions, respectively. Our developed DNN for generating simulated 4DCT data is useful for the markerless tumor tracking when original 4DCT is not available. Use of our DNN would accelerate markerless tumor tracking in radiotherapy and increased treatment accuracy in thoracoabdominal treatment.

A novel dynamic robotic moving phantom system for patient-specific quality assurance in real-time tumor-tracking radiotherapy.

In this study, we assess a developed novel dynamic moving phantom system that can reproduce patient three‐dimensional (3D) tumor motion and patient anatomy, and perform patient‐specific quality assurance (QA) of respiratory‐gated radiotherapy using SyncTraX. Three patients with lung cancer were enrolled in a study. 3D printing technology was adopted to obtain individualized lung phantoms using CT images. A water‐equivalent phantom (WEP) with the 3D‐printed plate lung phantom was set at the tip of the robotic arm. The log file that recorded the 3D positions of the lung tumor was used as the input to the dynamic robotic moving phantom. The WEP was driven to track 3D respiratory motion. Respiratory‐gated radiotherapy was performed for driving the WEP. The tracking accuracy was calculated as the differences between the actual and measured positions. For the absolute dose and dose distribution, the differences between the planned and measured doses were calculated. The differences between the planned and measured absolute doses were <1.0% at the isocenter and <4.0% for the lung region. The gamma pass ratios of γ3 mm/3% and γ2 mm/2% under the conditions of gating and no‐gating were 99.9 ± 0.1% and 90.1 ± 8.5%, and 97.5 ± 0.9% and 68.6 ± 17.8%, respectively, for all the patients. Furthermore, for all the patients, the mean ± SD of the root mean square values of the positional error were 0.11 ± 0.04 mm, 0.33 ± 0.04 mm, and 0.20 ± 0.04 mm in the LR, AP, and SI directions, respectively. Finally, we showed that patient‐specific QA of respiratory‐gated radiotherapy using SyncTraX can be performed under realistic conditions using the moving phantom.

The updated outcomes of bladder-preserving trimodal therapy using a real-time tumor-tracking radiotherapy system for patients with muscle-invasive bladder cancer.

ObjectiveBladder-preserving trimodal therapy is recognized as a promising alternative treatment for muscle-invasive bladder cancer. We report the updated outcomes of muscle-invasive bladder cancer patients that were treated using our treatment protocol, which involves radiotherapy delivered with a real-time tumor-tracking radiotherapy system.MethodsThirty-eight patients who were diagnosed with T2-T4N0M0 bladder cancer between 1998 and 2016 and had clinically inoperable disease or refused to undergo surgery were enrolled. The treatment protocol included maximal transurethral resection followed by whole-bladder radiotherapy (40 Gy). Concurrent nedaplatin-based chemotherapy was administered to patients with adequate renal function. At the time of the first evaluation (via transurethral resection of the tumor bed), fiducial markers were endoscopically inserted into the bladder wall around the tumor. A boost of 25 Gy was administered using the real-time tumor-tracking radiotherapy system. The second evaluation (via transurethral resection of the tumor bed) was performed 6 months after the start of treatment. The Kaplan–Meier method and Cox hazards analysis were used to analyze overall survival and cancer-specific survival.ResultsThe median duration of the follow-up period was 28 months (range: 3–161 months). The 5- and 10-year overall survival rates were 54.9 and 41.2%, respectively. Twenty-five (65.8%) and twenty (74.1%) patients had achieved complete responses to chemoradiation at the first and second evaluations, respectively. In univariate and multivariate analyses, performance status was found to be significantly associated with overall survival [P = 0.03, hazard ratio: 3.48, 95% confidence interval: 1.15–10.6] and cancer-specific survival [P = 0.02, hazard ratio: 4.57, 95% confidence interval: 1.32–16.9], and sex was shown to be significantly associated with cancer-specific survival [P = 0.03, hazard ratio: 3.07, 95% confidence interval: 1.09–8.30].ConclusionsOur bladder-preserving trimodal therapy protocol, which involves the use of a real-time tumor-tracking radiotherapy system, produced an acceptable overall survival rate. This therapy is a reasonable alternative for patients that are medically unfit for or do not want to undergo cystectomy.

Prediction of lung tumor motion using nonlinear autoregressive model with exogenous input

The present note addresses the development of a lung tumor position predictor to be used in dynamic tumor tracking radiotherapy, abbreviated as DTT-RT. As there exists 50–500 ms positioning lag in the control of the multi-leaf collimator (MLC) of commercial medical linear accelerators, prediction of future lung tumor position with sufficiently long prediction horizon is inevitable for the successful implementation of DTT-RT. The present article proposes a lung tumor position predictor, which is classified as a nonlinear autoregressive model with exogenous input (NARX). The proposed predictor was trained using seven lung tumor motion trajectories of patients who underwent respiratory gated radiotherapy at Yamaguchi University Hospital. We considered three different prediction horizons, 600 ms, 800 ms and 1 s, which were sufficiently long to compensate for the possible positioning control lag of the MLC. A patient-specific model corresponding to an intended prediction horizon was obtained by training it using the selected tumor motion trajectory with the specified horizon. Accordingly, we obtained three NARX predictors for a single patient. We calculated two performance metrics: the RMS prediction errors and the rate of coverage of the entire tumor trajectory defined by the number of samples of the measured tumor position which was inside the 4 mm cube centered at the corresponding predicted tumor position. The latter quantifies the feasibility of the predictors to generate future gating cubes in the implementation of DTT-RT. The (mean standard deviation) values of the rates of 600 ms, 800 ms and 1 s prediction horizon calculated using the proposed NARX predictors were %, % and %, respectively.

Quantitative evaluation of image recognition performance of fiducial markers in real-time tumor-tracking radiation therapy

PurposeTo quantitatively evaluate and compare the image recognition performance of multiple fiducial markers available in real-time tumor-tracking radiation therapy (RTRT). MethodsClinically available markers including sphere shape, coil shape, cylinder shape, line shape, and ball shape (folded line shape) were evaluated in liver and lung models of RTRT. Maximum thickness of the polymethyl metacrylate (PMMA) phantom that could automatically recognize the marker was determined by template-pattern matching. Image registration accuracy of the fiducial marker was determined using liver RTRT model. Lung RTRT was mimicked with an anthropomorphic chest phantom and a one-dimensional motion stage in order to simulate marker motion in heterogeneous fluoroscopic images. The success or failure of marker tracking and image registration accuracy for the lung model were evaluated in the same manner as that for the liver model. ResultsAll fiducial markers except for line shape and coil shape of thinner diameter were recognized by the PMMA phantom, which is assumed to have the typical thickness of an abdomen, with two-dimensional image registration accuracy of <2 pixels. Three-dimensional calculation error with the use of real-time stereoscopic fluoroscopy in RTRT was thought to be within 1 mm. In the evaluation using the lung model, the fiducial markers were recognized stably with sufficient accuracy for clinical application. The same was true for the evaluation using the liver model. ConclusionsThe image recognition performance of fiducial markers was quantified and compared. The results presented here may be useful for the selection of fiducial markers.