Nobutaka Mukumoto

Evaluation of dynamic tumour tracking radiotherapy with real-time monitoring for lung tumours using a gimbal mounted linac

PURPOSE To evaluate feasibility and acute toxicities after dynamic tumour tracking (DTT) irradiation with real-time monitoring for lung tumours using a gimbal mounted linac. MATERIALS AND METHODS Spherical gold markers were placed around the tumour using a bronchoscope prior to treatment planning. Prescription dose at the isocentre was 56 Gy in 4 fractions for T2a lung cancer and metastatic tumour, and 48 Gy in 4 fractions for the others. Dose-volume metrics were compared between DTT and conventional static irradiation using in-house developed software. RESULTS Of twenty-two patients enrolled, DTT radiotherapy was successfully performed for 16 patients, except 4 patients who coughed out the gold markers, one who showed spontaneous tumour regression, and one where the abdominal wall motion did not correlate with the tumour motion. Dose covering 95% volume of GTV was not different between the two techniques, while normal lung volume receiving 20 Gy or more was reduced by 20%. A mean treatment time per fraction was 36 min using DTT. With a median follow-up period of 13.2 months, no severe toxicity grade 3 or worse was observed. CONCLUSIONS DTT radiotherapy using a gimbal mounted linac was clinically feasible for lung treatment without any severe acute toxicity.

Predictive uncertainty in infrared marker-based dynamic tumor tracking with Vero4DRT

PURPOSE To quantify the predictive uncertainty in infrared (IR)-marker-based dynamic tumor tracking irradiation (IR Tracking) with Vero4DRT (MHI-TM2000) for lung cancer using logfiles. METHODS A total of 110 logfiles for 10 patients with lung cancer who underwent IR Tracking were analyzed. Before beam delivery, external IR markers and implanted gold markers were monitored for 40 s with the IR camera every 16.7 ms and with an orthogonal kV x-ray imaging subsystem every 80 or 160 ms. A predictive model [four-dimensional (4D) model] was then created to correlate the positions of the IR markers (PIR) with the three-dimensional (3D) positions of the tumor indicated by the implanted gold markers (Pdetect). The sequence of these processes was defined as 4D modeling. During beam delivery, the 4D model predicted the future 3D target positions (Ppredict) from the PIR in real-time, and the gimbaled x-ray head then tracked the target continuously. In clinical practice, the authors updated the 4D model at least once during each treatment session to improve its predictive accuracy. This study evaluated the predictive errors in 4D modeling (E4DM) and those resulting from the baseline drift of PIR and Pdetect during a treatment session (EBD). E4DM was defined as the difference between Ppredict and Pdetect in 4D modeling, and EBD was defined as the mean difference between Ppredict calculated from PIR in updated 4D modeling using (a) a 4D model created from training data before the model update and (b) an updated 4D model created from new training data. RESULTS The mean E4DM was 0.0 mm with the exception of one logfile. Standard deviations of E4DM ranged from 0.1 to 1.0, 0.1 to 1.6, and 0.2 to 1.3 mm in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions, respectively. The median elapsed time before updating the 4D model was 13 (range, 2-33) min, and the median frequency of 4D modeling was twice (range, 2-3 times) per treatment session. EBD ranged from -1.0 to 1.0, -2.1 to 3.3, and -2.0 to 3.5 mm in the LR, AP, and SI directions, respectively. EBD was highly correlated with BDdetect in the LR (R = -0.83) and AP directions (R = -0.88), but not in the SI direction (R = -0.40). Meanwhile, EBD was highly correlated with BDIR in the SI direction (R = -0.67), but not in the LR (R = 0.15) or AP (R = -0.11) direction. If the 4D model was not updated in the presence of intrafractional baseline drift, the predicted target position deviated from the detected target position systematically. CONCLUSIONS Application of IR Tracking substantially reduced the geometric error caused by respiratory motion; however, an intrafractional error due to baseline drift of >3 mm was occasionally observed. To compensate for EBD, the authors recommend checking the target and IR marker positions constantly and updating the 4D model several times during a treatment session.

Accuracy verification of infrared marker‐based dynamic tumor‐tracking irradiation using the gimbaled x‐ray head of the Vero4DRT (MHI‐TM2000)

PURPOSE To verify the accuracy of an infrared (IR) marker-based dynamic tumor-tracking irradiation system (IR tracking) using the gimbaled x-ray head of the Vero4DRT (MHI-TM2000). METHODS The gimbaled 6-MV C-band x-ray head of the Vero4DRT can swing along the pan-and-tilt direction to track a moving target. During beam delivery, the Vero4DRT predicts the future three-dimensional (3D) target position in real time using a correlation model [four-dimensional (4D) model] between the target and IR marker motion, and then continuously transfers the corresponding tracking orientation to the gimbaled x-ray head. The 4D-modeling error (E4DM) and the positional tracking error (EP) were defined as the difference between the predicted and measured positions of the target in 4D modeling and as the difference between the tracked and measured positions of the target during irradiation, respectively. For the clinical application of IR tracking, we assessed the relationship between E4DM and EP for three 1D sinusoidal (peak-to-peak amplitude [A]: 20-40 mm, breathing period [T]: 2-4 s), five 1D phase-shifted sinusoidal (A: 20 mm, T: 4 s, phase shift [τ]: 0.2-2 s), and six 3D patient respiratory patterns. RESULTS The difference between the 95th percentile of the absolute EP (EP (95)) and the mean (μ) + two standard deviations (SD) of absolute E4DM (E4DM (μ+2SD)) was within ± 1 mm for all motion patterns. As the absolute correlation between the target and IR marker motions decreased from 1.0 to 0.1 for the 1D phase-shifted sinusoidal patterns, the E4DM (μ+2SD) and EP (95) increased linearly, from 0.4 to 3.0 mm (R = -0.98) and from 0.5 to 2.2 mm (R = -0.95), respectively. There was a strong positive correlation between E4DM (μ+2SD) and EP (95) in each direction [(lateral, craniocaudal, anteroposterior) = (0.99, 0.98, 1.00)], even for the 3D respiratory patterns; thus, EP (95) was readily estimated from E4DM (μ+2SD). CONCLUSIONS Positional tracking errors correlated strongly with 4D-modeling errors in IR tracking. Thus, the accuracy of the 4D model must be verified before treatment, and margins are required to compensate for the 4D-modeling error.

Intra- and interfractional variations in geometric arrangement between lung tumours and implanted markers

PURPOSE To quantify the intra- and interfractional variations between lung tumours and implanted markers. MATERIALS AND METHODS Gold markers were implanted transbronchially around a lung tumour in fifteen patients. They underwent four-dimensional computed tomography scans twice, and the centroids of the tumour and markers were determined. Intrafractional variations were defined as the residual tumour motions relative to the markers due to respiration from the end-exhale phase. Interfractional variations were defined as the residual setup errors after correction for the position of the implanted markers in end-exhale phase images. RESULTS The intrafractional variations differed between patients. The root mean squares of standard deviations for each phase were 0.6, 0.9, and 1.5mm in the right-left, anterior-posterior, and superior-inferior directions, respectively. The maximum difference in intrafractional variation among 10 phases was correlated with the amplitude of tumour motion in all directions and the tumour-marker distance in the anterior-posterior and superior-inferior directions. The interfractional variations were within 2.5mm. CONCLUSIONS The intrafractional variations differed according to the amount of tumour motion and the tumour-marker distance. Additionally, interfractional variations of up to 2.5mm were observed. Thus, a corresponding margin should be considered during implanted marker-based beam delivery to account for these variations.

Differences in dose-volumetric data between the analytical anisotropic algorithm and the x-ray voxel Monte Carlo algorithm in stereotactic body radiation therapy for lung cancer

The objective of this study was to evaluate the differences in dose-volumetric data obtained using the analytical anisotropic algorithm (AAA) vs the x-ray voxel Monte Carlo (XVMC) algorithm for stereotactic body radiation therapy (SBRT) for lung cancer. Dose-volumetric data from 20 patients treated with SBRT for solitary lung cancer generated using the iPlan XVMC for the Novalis system consisting of a 6-MV linear accelerator and micro-multileaf collimators were recalculated with the AAA in Eclipse using the same monitor units and identical beam setup. The mean isocenter dose was 100.2% and 98.7% of the prescribed dose according to XVMC and AAA, respectively. Mean values of the maximal dose (D(max)), the minimal dose (D(min)), and dose received by 95% volume (D₉₅) for the planning target volume (PTV) with XVMC were 104.3%, 75.1%, and 86.2%, respectively. When recalculated with the AAA, those values were 100.8%, 77.1%, and 85.4%, respectively. Mean dose parameter values considered for the normal lung, namely the mean lung dose, V₅, and V₂₀, were 3.7Gy, 19.4%, and 5.0% for XVMC and 3.6Gy, 18.3%, and 4.7% for the AAA, respectively. All of these dose-volumetric differences between the 2 algorithms were within 5% of the prescribed dose. The effect of PTV size and tumor location, respectively, on the differences in dose parameters for the PTV between the AAA and XVMC was evaluated. A significant effect of the PTV on the difference in D₉₅ between the AAA and XVMC was observed (p = 0.03). Differences in the marginal doses, namely D(min) and D₉₅, were statistically significant between peripherally and centrally located tumors (p = 0.04 and p = 0.02, respectively). Tumor location and volume might have an effect on the differences in dose-volumetric parameters. The differences between AAA and XVMC were considered to be within an acceptable range (<5 percentage points).

Optimization of the x-ray monitoring angle for creating a correlation model between internal and external respiratory signals.

PURPOSE To perform dynamic tumor tracking irradiation with the Vero4DRT (MHI-TM2000), a correlation model [four dimensional (4D) model] between the displacement of infrared markers on the abdominal wall and the three-dimensional position of a tumor indicated by a minimum of three implanted gold markers is required. However, the gold markers cannot be detected successfully on fluoroscopic images under the following situations: (1) overlapping of the gold markers; and (2) a low intensity ratio of the gold marker to its surroundings. In the present study, the authors proposed a method to readily determine the optimal x-ray monitoring angle for creating a 4D model utilizing computed tomography (CT) images. METHODS The Vero4DRT mounting two orthogonal kV x-ray imaging subsystems can separately rotate the gantry along an O-shaped guide-lane and the O-ring along its vertical axis. The optimal x-ray monitoring angle was determined on CT images by minimizing the root-sum-square of water equivalent path lengths (WEPLs) on the orthogonal lines passing all of the gold markers while rotating the O-ring and the gantry. The x-ray monitoring angles at which the distances between the gold markers were within 5 mm at the isocenter level were excluded to prevent false detection of the gold markers in consideration of respiratory motions. First, the relationship between the WEPLs (unit: mm) and the intensity ratios of the gold markers was examined to assess the validity of our proposed method. Second, our proposed method was applied to the 4D-CT images at the end-expiration phase for 11 lung cancer patients who had four to five gold markers. To prove the necessity of the x-ray monitoring angle optimization, the intensity ratios of the least visible markers (minimum intensity ratios) that were estimated from the WEPLs were compared under the following conditions: the optimal x-ray monitoring angle and the angles used for setup verification. Additionally, the intra- and interfractional variations in the intensity ratio were examined from the optimal x-ray monitoring angle. RESULTS A negative strong correlation was observed between the WEPL (x) and the intensity ratio (y) (y = 6.57 exp[-0.0125x] + 1, R = -0.88 [95% confidence interval: -0.85 to -0.90], p < 0.01). Our proposed method effectively avoided having the x-ray beam pass through high-density structures, although there were large interpatient variations in the optimal x-ray monitoring angle because of the geometric arrangement between the gold markers and the anatomical structures. The minimum intensity ratios that were estimated from the WEPLs at the optimal x-ray monitoring angle ranged from 1.43 to 2.48, which was an average of 1.27 times (range, 1.02-1.66) higher than the angles used for setup verification. The maximum intra- and interfractional decreases in the intensity ratio were 0.23 and 0.17, respectively. CONCLUSIONS The authors demonstrated that the optimal x-ray monitoring angle for creating a 4D model can improve the visibility of gold markers.

Long-term stability assessment of a 4D tumor tracking system integrated into a gimbaled linear accelerator

We assessed long‐term stability of tracking accuracy using the Vero4DRT system. This metric was observed between September 2012 and March 2015. A programmable respiratory motion phantom, designed to move phantoms synchronously with respiratory surrogates, was used. The infrared (IR) markers moved in the anterior–posterior (AP) direction as respiratory surrogates, while a cube phantom with a steel ball at the center, representing the tumor, and with radiopaque markers around it moved in the superior–inferior (SI) direction with one‐dimensional (1D) sinusoidal patterns. A correlation model between the tumor and IR marker motion (4D model) was created from the training data obtained for 20 s just before beam delivery. The irradiation field was set to 3×3 cm2 and 300 monitor units (MUs) of desired MV X‐ray beam were delivered. The gantry and ring angles were set to 0° and 45°, respectively. During beam delivery, the system recorded approximately 60 electronic portal imaging device (EPID) images. We analyzed: 1) the predictive accuracy of the 4D model (EP), defined as the difference between the detected and predicted target positions during 4D model creation, and 2) the tracking accuracy (ET), defined as the difference between the center of the steel ball and the MV X‐ray field on the EPID image. The median values of mean plus two standard deviations (SDs) for EP were 0.06, 0.35, and 0.06 mm in the left–right (LR), SI, and AP directions, respectively. The mean values of maximum deviation for ET were 0.38, 0.49, and 0.53 mm and the coefficients of variance (CV) were 0.16, 0.10, and 0.05 in lateral, longitudinal, and 2D directions, respectively. Consequently, the IR Tracking accuracy was consistent over a period of two years. Our proposed method assessed the overall tracking accuracy readily using real‐time EPID images, and proved to be a useful QA tool for dynamic tumor tracking with the Vero4DRT system. PACS number: 87.59.‐e, 88.10.gc, 87.55.Qr

A multi-centre analysis of treatment procedures and error components in dynamic tumour tracking radiotherapy

PURPOSE This study aimed to compare procedures for dynamic tumour tracking (DTT) using a gimbal-mounted linac between centres in Japan (KU-IBRI) and Belgium (UZB), to quantify tracking error (TE), and to estimate tumour-fiducial uncertainties and PTV margins. METHODS Twenty-two patients were evaluated. TE was divided into components originating from the patient, fraction, segment, and residuals. RESULTS KU-IBRI applied DTT to lung cancer, while UZB treated both the lung and liver. Patients from UZB were younger and had a higher body mass index. DTT procedures differed in the use of body fixation, correction for set-up error, type of fiducial markers, and goodness of fit of correlation model. TE was larger at UZB in the intra-fraction components, whereas the tumour-fiducial uncertainties were estimated to be larger at KU-IBRI. These results ultimately led to similar PTV margins at both centres (2.1, 4.2, and 2.6 mm for KU-IBRI; 2.4, 3.6, and 2.0 mm for UZB in LR, AP, and SI, respectively, for 99% coverage of patients). CONCLUSION Several differences in procedures and patient characteristics were observed that affected TE and tumour-fiducial uncertainties. This analysis confirmed similar accuracy in DTT delivery and adequate PTV margins in the different centres based on their local specific workflows.

Baseline correction of a correlation model for improving the prediction accuracy of infrared marker-based dynamic tumor tracking

We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker‐based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four‐dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F1st(x,v), was adjusted by the baseline drift of IR markers (BDIR) along the x‐axis, as function F′(x,v). Next, BDdetect, that defined as the difference between the target positions indicated by the implanted fiducial markers (Pdetect) and the predicted target positions with F′(x,v) (Ppredict) was determined using orthogonal kV X‐ray images at the peaks of the Pdetect of the end‐inhale and end‐exhale phases for 10 s just before irradiation. F′(x,v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as Fcor(x,v)=F1st{(x−BDIR),v}−BDdetect. We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between Pdetect and Ppredict (|Ep|) was compared between F1st(x,v) and Fcor(x,v). The median 95th percentile of |Ep| (units: mm) was 1.0, 1.7, and 3.5 for F1st(x,v), and 0.6, 1.1, and 2.1 for Fcor(x,v) in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of |Ep| peaked at 3.2 mm using Fcor(x,v) compared with 8.4 mm using F1st(x,v). Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation. PACS number: 87.19.rs, 87.19.Wx, 87.56.‐v, 87.59.‐e, 88.10.gc

The impact of abdominal compression on outcome in patients treated with stereotactic body radiotherapy for primary lung cancer.

The aim of this study was to evaluate the impact of abdominal compression (AC) on outcome in patients treated with stereotactic body radiotherapy (SBRT) for primary lung cancer. We retrospectively reviewed data for 47 patients with histologically proven non-small cell lung cancer and lung tumour motion ≥8 mm treated with SBRT. Setup error was corrected based on bony structure. The differences in overall survival (OS), local control (LC) and disease-free survival (DFS) were evaluated to compare patients treated with AC (n = 22) and without AC (n = 25). The median follow-up was 42.6 months (range, 1.4–94.6 months). The differences in the 3-year OS, LC and DFS rate between the two groups were not statistically significant (P = 0.909, 0.209 and 0.639, respectively). However, the largest difference was observed in the LC rate, which was 82.5% (95% CI, 54.9–94.0%) for patients treated without AC and 65.4% (95% CI, 40.2–82.0%) for those treated with AC. After stratifying the patients into prognostic groups based on sex and T-stage, the LC difference increased in the group with an unfavourable prognosis. The present study suggests that AC might be associated with a worse LC rate after SBRT using a bony-structure-based set-up.