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

Intra- and inter-fractional variations of tumors with fiducial markers measured using respiratory-correlated computed tomography images for respiratory gated lung stereotactic body radiation therapy

PURPOSE

This study evaluated the intra- and inter-fractional variation of tumors with fiducial markers (FMs), relative to the tumor-FM distance, to establish how close an FM should be inserted for respiratory-gated stereotactic body radiation therapy (RG-SBRT).

METHODS

Forty-five lung tumors treated with RG-SBRT were enrolled. End-expiratory computed tomography (CT) (CTplan) and four-dimensional-CT (4D-CT) scans were obtained for planning. End-expiratory CT (CTfr) scanning was performed before each fraction. The FMs were divided into two groups based on the median tumor-FM distance in the CTplan (Dp). For the intra-fractional variation, the correlations between the corresponding tumor and FM intra-fractional motions, defined as the centroid coordinates of those in each 0-90% phase, with the 50% phase of 4D-CT as the origin, were calculated in the left-right, anterior-posterior, and superior-inferior directions. Furthermore, the maximum difference in the tumor-FM distance in each phase of 4D-CT scan, based on those in the 50% phase of 4D-CT scan (Dmax), was obtained. Inter-fractional variation was defined as the maximum distance between the tumors in CTplan and CTfr, when the CT scans were fused based on each FM or vertebra.

RESULTS

The median Dp was 26.1 mm. While FM intra-fractional motions were significantly and strongly correlated with the tumor intra-fractional motions in only anterior-posterior and superior-inferior directions for the Dp > 26 mm group, they were significantly and strongly correlated in all directions for the Dp ≤ 26 mm group. In all directions, Dmax values of the Dp ≤ 26 mm group were lower than those of the Dp > 26 mm group. The inter-fractional variations based on the Dp ≤ 26 mm were smaller than those on the Dp > 26 mm and on the vertebra in all directions.

CONCLUSIONS

Regarding intra- and inter-fractional variation, FMs for Dp ≤ 26 mm can increase the accuracy for RG-SBRT.

Population-based asymmetric margins for moving targets in real-time tumor tracking

BACKGROUND

Both geometric and dosimetric components are commonly considered when determining the margin for planning target volume (PTV). As dose distribution is shaped by controlling beam aperture in peripheral dose prescription and dose-escalated simultaneously integrated boost techniques, adjusting the margin by incorporating the variable dosimetric component into the PTV margin is inappropriate; therefore, geometric components should be accurately estimated for margin calculations.

PURPOSE

We introduced an asymmetric margin-calculation theory using the guide to the expression of uncertainty in measurement (GUM) and intra-fractional motion. The margins in fiducial marker-based real-time tumor tracking (RTTT) for lung, liver, and pancreatic cancers were calculated and were then evaluated using Monte Carlo (MC) simulations.

METHODS

A total of 74 705, 73 235, and 164 968 sets of intra- and inter-fractional positional data were analyzed for 48 lung, 48 liver, and 25 pancreatic cancer patients, respectively, in RTTT clinical trials. The 2.5th and 97.5th percentiles of the positional error were considered representative values of each fraction of the disease site. The population-based statistics of the probability distributions of these representative positional errors (PD-RPEs) were calculated in six directions. A margin covering 95% of the population was calculated using the proposed formula. The content rate in which the clinical target volume (CTV) was included in the PTV was calculated through MC simulations using the PD-RPEs.

RESULTS

The margins required for RTTT were at most 6.2, 4.6, and 3.9 mm for lung, liver, and pancreatic cancer, respectively. MC simulations revealed that the median content rates using the proposed margins satisfied 95% for lung and liver cancers and 93% for pancreatic cancer, closer to the expected rates than the margins according to van Herk's formula.

CONCLUSIONS

Our proposed formula based on the GUM and motion probability distributions (MPD) accurately calculated the practical margin size for fiducial marker-based RTTT. This was verified through MC simulations.

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.

Real-time liver motion estimation via deep learning-based angle-agnostic X-ray imaging

BACKGROUND

Real-time liver imaging is challenged by the short imaging time (within hundreds of milliseconds) to meet the temporal constraint posted by rapid patient breathing, resulting in extreme under-sampling for desired 3D imaging. Deep learning (DL)-based real-time imaging/motion estimation techniques are emerging as promising solutions, which can use a single X-ray projection to estimate 3D moving liver volumes by solved deformable motion. However, such techniques were mostly developed for a specific, fixed X-ray projection angle, thereby impractical to verify and guide arc-based radiotherapy with continuous gantry rotation.

PURPOSE

To enable deformable motion estimation and 3D liver imaging from individual X-ray projections acquired at arbitrary X-ray scan angles, and to further improve the accuracy of single X-ray-driven motion estimation.

METHODS

We developed a DL-based method, X360, to estimate the deformable motion of the liver boundary using an X-ray projection acquired at an arbitrary gantry angle (angle-agnostic). X360 incorporated patient-specific prior information from planning 4D-CTs to address the under-sampling issue, and adopted a deformation-driven approach to deform a prior liver surface mesh to new meshes that reflect real-time motion. The liver mesh motion is solved via motion-related image features encoded in the arbitrary-angle X-ray projection, and through a sequential combination of rigid and deformable registration modules. To achieve the angle agnosticism, a geometry-informed X-ray feature pooling layer was developed to allow X360 to extract angle-dependent image features for motion estimation. As a liver boundary motion solver, X360 was also combined with priorly-developed, DL-based optical surface imaging and biomechanical modeling techniques for intra-liver motion estimation and tumor localization.

RESULTS

With geometry-aware feature pooling, X360 can solve the liver boundary motion from an arbitrary-angle X-ray projection. Evaluated on a set of 10 liver patient cases, the mean (± s.d.) 95-percentile Hausdorff distance between the solved liver boundary and the "ground-truth" decreased from 10.9 (±4.5) mm (before motion estimation) to 5.5 (±1.9) mm (X360). When X360 was further integrated with surface imaging and biomechanical modeling for liver tumor localization, the mean (± s.d.) center-of-mass localization error of the liver tumors decreased from 9.4 (± 5.1) mm to 2.2 (± 1.7) mm.

CONCLUSION

X360 can achieve fast and robust liver boundary motion estimation from arbitrary-angle X-ray projections for real-time imaging guidance. Serving as a surface motion solver, X360 can be integrated into a combined framework to achieve accurate, real-time, and marker-less liver tumor localization.

Development of a prediction model for target positioning by using diaphragm waveforms extracted from CBCT projection images

PURPOSE

To develop a prediction model (PM) for target positioning using diaphragm waveforms extracted from CBCT projection images.

METHODS

Nineteen patients with lung cancer underwent orthogonal rotational kV x-ray imaging lasting 70 s. IR markers placed on their abdominal surfaces and an implanted gold marker located nearest to the tumor were considered as external surrogates and the target, respectively. Four different types of regression-based PM were trained using surrogate motions and target positions for the first 60 s, as follows: Scenario A: Based on the clinical scenario, 3D target positions extracted from projection images were used as they were (PMCL ). Scenario B: The short-arc 4D-CBCT waveform exhibiting eight target positions was obtained by averaging the target positions in Scenario A. The waveform was repeated for 60 s (W4D-CBCT ) by adapting to the respiratory phase of the external surrogate. W4D-CBCT was used as the target positions (PM4D-CBCT ). Scenario C: The Amsterdam Shroud (AS) signal, which depicted the diaphragm motion in the superior-inferior direction was extracted from the orthogonal projection images. The amplitude and phase of W4D-CBCT were corrected based on the AS signal. The AS-corrected W4D-CBCT was used as the target positions (PMAS-4D-CBCT ). Scenario D: The AS signal was extracted from single projection images. Other processes were the same as in Scenario C. The prediction errors were calculated for the remaining 10 s.

RESULTS

The 3D prediction error within 3 mm was 77.3% for PM4D-CBCT , which was 12.8% lower than that for PMCL . Using the diaphragm waveforms, the percentage of errors within 3 mm improved by approximately 7% to 84.0%-85.3% for PMAS-4D-CBCT in Scenarios C and D, respectively. Statistically significant differences were observed between the prediction errors of PM4D-CBCT and PMAS-4D-CBCT .

CONCLUSION

PMAS-4D-CBCT outperformed PM4D-CBCT , proving the efficacy of the AS signal-based correction. PMAS-4D-CBCT would make it possible to predict target positions from 4D-CBCT images without gold markers.

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images

PURPOSE

The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment.

METHODS

Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50 Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model.

RESULTS

The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm³ and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85 ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5 mm was 93.6%.

CONCLUSIONS

The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

Clinical Evaluation of Fiducial Marker Pre-Planning for Virtual Bronchoscopic Navigation Implantation in Lung Tumour Patients Treated With CyberKnife

Purpose

For the treatment of invisible lung tumours with CyberKnife (CK), fiducial markers (FMs) were implanted as an internal surrogate under virtual bronchoscopic navigation (VBN). This research aims to study the benefits of introducing an additional procedure in assigning the optimal FM positions using a pre-procedure planning system and performing virtual simulation before implantation. The objectives were 1) to reduce the duration of the FM implantation procedure, 2) to reduce the radiation exposure in dose area product (DAP) (dGy*cm²) to patients, and 3) to increase the number of FMs implanted around the tumour.

Methods and Materials

This study is retrospective, single-centre, and observational in nature. A total of 32 patients were divided into two groups. In Group 1, 18 patients underwent conventional VBN FM implantation. In Group 2, 14 patients underwent additional pre-procedure planning and simulation. The steps of pre-procedure planning include 1) importing CT images into the treatment planning system (Eclipse, Varian Medical Systems, Inc.) and delineating five to six FMs in their ideal virtual positions and 2) copying the FM configuration into VBN planning software (LungPoint Bronchus Medical, Inc.) for verification and simulation. Finally, the verified FMs were deployed through VBN with the guidance of the LungPoint planning software.

Results

A total of 162 FMs were implanted among 35 lesions in 32 patients aged from 37 to 92 (median = 66; 16 men and 16 women). Results showed that 1) the average FM insertion time was shortened from 41 min (SD = 2.05) to 23 min (SD = 1.25), p = 0.00; 2) the average absorbed dose of patients in DAP was decreased from 67.4 cGy*cm² (SD = 14.48) to 25.3 cGy*cm² (SD = 3.82), p = 0.01 (1-tailed); and 3) the average number of FMs implanted around the tumour was increased from 4.7 (SD = 0.84) to 5.6 (SD = 0.76), p = 0.00 (1-tailed).

Conclusion

Pre-procedure planning reduces the FM implantation duration from 41.1 to 22.9 min, reduces the radiation exposure in DAP from 67.4 to 25.3 dGy*cm², and increases the number of FMs inserted around the tumour from 4.7 to 5.6.

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.

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

BACKGROUND

In infrared reflective (IR) marker-based hybrid real-time tumor tracking (RTTT), the internal target position is predicted with the positions of IR markers attached on the patient's body surface using a prediction model. In this work, we developed two artificial intelligence (AI)-driven prediction models to improve RTTT radiotherapy, namely, a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS) model. The models aim to improve the accuracy in predicting three-dimensional tumor motion.

METHODS

From patients whose respiration-induced motion of the tumor, indicated by the fiducial markers, exceeded 8 mm, 1079 logfiles of IR marker-based hybrid RTTT (IR Tracking) with the gimbal-head radiotherapy system were acquired and randomly divided into two datasets. All the included patients were breathing freely with more than four external IR markers. The historical dataset for the CNN model contained 1003 logfiles, while the remaining 76 logfiles complemented the evaluation dataset. The logfiles recorded the external IR marker positions at a frequency of 60 Hz and fiducial markers as surrogates for the detected target positions every 80-640 ms for 20-40 s. For each logfile in the evaluation dataset, the prediction models were trained based on the data in the first three quarters of the recording period. In the last quarter, the performance of the patient-specific prediction models was tested and evaluated. The overall performance of the AI-driven prediction models was ranked by the percentage of predicted target position within 2 mm of the detected target position. Moreover, the performance of the AI-driven models was compared to a regression prediction model currently implemented in gimbal-head radiotherapy systems.

RESULTS

The percentage of the predicted target position within 2 mm of the detected target position was 95.1%, 92.6% and 85.6% for the CNN, ANFIS, and regression model, respectively. In the evaluation dataset, the CNN, ANFIS, and regression model performed best in 43, 28 and 5 logfiles, respectively.

CONCLUSIONS

The proposed AI-driven prediction models outperformed the regression prediction model, and the overall performance of the CNN model was slightly better than that of the ANFIS model on the evaluation dataset.

Quantification of the geometric uncertainty when using implanted markers as a surrogate for lung tumor motion

BACKGROUND

Fiducial markers are used as surrogates for tumor location during radiation therapy treatment. Developments in lung fiducial marker and implantation technology have provided a means to insert markers endobronchially for tracking of lung tumors. This study quantifies the surrogacy uncertainty (SU) when using endobronchially implanted markers as a surrogate for lung tumor position.

METHODS

We evaluated SU for 17 patients treated in a prospective electromagnetic-guided MLC tracking trial. Tumor and markers were segmented on all phases of treatment planning 4DCTs and all frames of pretreatment kilovoltage fluoroscopy acquired from lateral and frontal views. The difference in tumor and marker position relative to end-exhale position was calculated as the SU for both imaging methods and the distributions of uncertainties analyzed.

RESULTS

The mean (range) tumor motion amplitude in the 4DCT scan was 5.9 mm (1.7-11.7 mm) in the superior-inferior (SI) direction, 2.2 mm (0.9-5.5 mm) in the left-right (LR) direction, and 3.9 mm (1.2-12.9 mm) in the anterior-posterior (AP) direction. Population-based analysis indicated symmetric SU centered close to 0 mm, with maximum 5th/95th percentile values over all axes of -2.0 mm/2.1 mm with 4DCT, and -2.3/1.3 mm for fluoroscopy. There was poor correlation between the SU measured with 4DCT and that measured with fluoroscopy on a per-patient basis. We observed increasing SU with increasing surrogate motion. Based on fluoroscopy analysis, the mean (95% CI) SU was 5% (2%-8%) of the motion magnitude in the SI direction, 16% (6%-26%) of the motion magnitude in the LR direction, and 33% (23%-42%) of the motion magnitude in the AP direction. There was no dependence of SU on marker distance from the tumor.

CONCLUSION

We have quantified SU due to use of implanted markers as surrogates for lung tumor motion. Population 95th percentile range are up to 2.3 mm, indicating the approximate contribution of SU to total geometric uncertainty. SU was relatively small compared with the SI motion, but substantial compared with LR and AP motion. Due to uncertainty in estimations of patient-specific SU, it is recommended that population-based margins are used to account for this component of the total geometric uncertainty.

Clinical experience with lung-specific electromagnetic transponders for real-time tumor tracking in lung stereotactic body radiotherapy

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7 patients were implanted with lung-specific electromagnetic transponders (EMT).

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We report no complications from implantation and no migration of the EMT.

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7 non-small cell lung cancer patients underwent SBRT using EMT real-time tracking.

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SBRT was delivered in free-breathing (FB) or in deep inspiration breath-hold (DIBH).

Background and purposes

Motion management is crucial for optimal stereotactic body radiotherapy (SBRT) of moving targets. We aimed to describe our clinical experience with real-time tracking of lung-specific electromagnetic transponders (EMTs) for SBRT of early stage non-small cell lung cancer in free-breathing (FB) or deep inspiration breath-hold (DIBH).

Material and methods

Seven patients were implanted with EMTs. Simulation for SBRT was performed in FB and in DIBH. We prescribed 60 Gy in 3, 5 or 8 fractions to the tumor and delivered SBRT with volumetric modulated arcs and a 6 MV flattening filter free photon beam. Patients’ setup at the linac was performed using EMT positions and cone-beam CT (CBCT) verification. Four patients were treated in DIBH because of a dosimetric benefit. We analysed patient alignment and treatment delivery parameters using DIBH or FB and EMT real-time tracking.

Results

There were no complications from the EMT implantation. Visual inspection of CBCT before and/or after SBRT revealed good alignment of structures and EMTs. The median setup time was 9.8 min (range: 4.6–34.1 min) and the median session time was 14.7 min (range: 7.3–36.5 min). EMT positions in lungs remained stable during overall treatment and allowed real-time tracking both in FB and in DIBH SBRT. The treatment beam was gated when EMT centroid position exceeded tolerance thresholds ensuring correct delivery of radiation to the tumor.

Conclusion

Using EMTs for real-time tracking of tumor motion during lung SBRT proved to be safe, accurate and easy to integrate clinically for treatments in FB or DIBH.

Real-time intrafraction motion monitoring in external beam radiotherapy

Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

Image-guidance technique comparison on respiratory reproducibility and dose indexes for stereotactic body radiotherapy in lung tumor

We investigated respiratory reproducibility from position errors of gold internal fiducial markers for breath-hold (BH) and real-time tumor tracking (RTT) techniques for stereotactic body radiotherapy in lung tumors. The relationship between position errors and dose indexes was checked for both techniques. The stereotactic body radiotherapy plan in lung tumors was planned for 29 patients. The tumor positioning was arranged using 1.5 mm diameter gold internal fiducial markers. First, CT images were acquired to analyze position errors of gold markers for BH and RTT techniques. The offset plans for both techniques were calculated by displacing the mean position errors. The dose indexes (D98, D95, D2, mean dose) in a planning target volume were evaluated from dose volume histograms for the original plan, BH, and RTT offset plans. The relationship between position errors and dose indexes was analyzed using the root mean square (RMS) for both techniques. For the BH, the RMS was 3.29 mm at the lower lobe. Similarly, it was 1.34 mm for the RTT. The difference for D98 by position error for BH was -7.0 ± 10.8% at the lower lobe and the difference of all dose indexes for the RTT was less than 1%. The D2 and mean dose for both techniques were nearly the same as those of the original plan. In conclusion, the adaptation of the BH technique should be ≤2 mm RMS. If the position error is >2 mm RMS, the RTT technique should be used instead of the BH technique.

Review of Real-Time 3-Dimensional Image Guided Radiation Therapy on Standard-Equipped Cancer Radiation Therapy Systems: Are We at the Tipping Point for the Era of Real-Time Radiation Therapy?

PURPOSE

To review real-time 3-dimensional (3D) image guided radiation therapy (IGRT) on standard-equipped cancer radiation therapy systems, focusing on clinically implemented solutions.

METHODS AND MATERIALS

Three groups in 3 continents have clinically implemented novel real-time 3D IGRT solutions on standard-equipped linear accelerators. These technologies encompass kilovoltage, combined megavoltage-kilovoltage, and combined kilovoltage-optical imaging. The cancer sites treated span pelvic and abdominal tumors for which respiratory motion is present. For each method the 3D-measured motion during treatment is reported. After treatment, dose reconstruction was used to assess the treatment quality in the presence of motion with and without real-time 3D IGRT. The geometric accuracy was quantified through phantom experiments. A literature search was conducted to identify additional real-time 3D IGRT methods that could be clinically implemented in the near future.

RESULTS

The real-time 3D IGRT methods were successfully clinically implemented and have been used to treat more than 200 patients. Systematic target position shifts were observed using all 3 methods. Dose reconstruction demonstrated that the delivered dose is closer to the planned dose with real-time 3D IGRT than without real-time 3D IGRT. In addition, compromised target dose coverage and variable normal tissue doses were found without real-time 3D IGRT. The geometric accuracy results with real-time 3D IGRT had a mean error of <0.5 mm and a standard deviation of <1.1 mm. Numerous additional articles exist that describe real-time 3D IGRT methods using standard-equipped radiation therapy systems that could also be clinically implemented.

CONCLUSIONS

Multiple clinical implementations of real-time 3D IGRT on standard-equipped cancer radiation therapy systems have been demonstrated. Many more approaches that could be implemented were identified. These solutions provide a pathway for the broader adoption of methods to make radiation therapy more accurate, impacting tumor and normal tissue dose, margins, and ultimately patient outcomes.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis

To estimate the lung tumor position from multiple anatomical features on four‐dimensional computed tomography (4D‐CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D‐CT scanning. The three‐dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D‐CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root‐mean‐square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D‐CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV.

Clinical outcomes of stage I and IIA non-small cell lung cancer patients treated with stereotactic body radiotherapy using a real-time tumor-tracking radiotherapy system

Purpose

To investigate the clinical outcomes of stage I and IIA non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT) using a real-time tumor-tracking radiotherapy (RTRT) system.

Materials and methods

Patterns-of-care in SBRT using RTRT for histologically proven, peripherally located, stage I and IIA NSCLC was retrospectively investigated in four institutions by an identical clinical report format. Patterns-of-outcomes was also investigated in the same manner.

Results

From September 2000 to April 2012, 283 patients with 286 tumors were identified. The median age was 78 years (52–90) and the maximum tumor diameters were 9 to 65 mm with a median of 24 mm. The calculated biologically effective dose (10) at the isocenter using the linear-quadratic model was from 66 Gy to 126 Gy with a median of 106 Gy. With a median follow-up period of 28 months (range 0–127), the overall survival rate for the entire group, for stage IA, and for stage IB + IIA was 75%, 79%, and 65% at 2 years, and 64%, 70%, and 50% at 3 years, respectively. In the multivariate analysis, the favorable predictive factor was female for overall survival. There were no differences between the clinical outcomes at the four institutions. Grade 2, 3, 4, and 5 radiation pneumonitis was experienced by 29 (10.2%), 9 (3.2%), 0, and 0 patients. The subgroup analyses revealed that compared to margins from gross tumor volume (GTV) to planning target volume (PTV) ≥ 10 mm, margins < 10 mm did not worsen the overall survival and local control rates, while reducing the risk of radiation pneumonitis.

Conclusions

This multi-institutional retrospective study showed that the results were consistent with the recent patterns-of-care and patterns-of-outcome analysis of SBRT. A prospective study will be required to evaluate SBRT using a RTRT system with margins from GTV to PTV < 10mm.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-016-0742-3) contains supplementary material, which is available to authorized users.

Multivariate analysis for the estimation of target localization errors in fiducial marker-based radiotherapy

PURPOSE

To assess the target localization error (TLE) in terms of the distance between the target and the localization point estimated from the surrogates (|TMD|), the average of respiratory motion for the surrogates and the target (|aRM|), and the number of fiducial markers used for estimating the target (n).

METHODS

This study enrolled 17 lung cancer patients who subsequently underwent four fractions of real-time tumor tracking irradiation. Four or five fiducial markers were implanted around the lung tumor. The three-dimensional (3D) distance between the tumor and markers was at maximum 58.7 mm. One of the markers was used as the target (Pt), and those markers with a 3D |TMDn| ≤ 58.7 mm at end-exhalation were then selected. The estimated target position (Pe) was calculated from a localization point consisting of one to three markers except Pt. Respiratory motion for Pt and Pe was defined as the root mean square of each displacement, and |aRM| was calculated from the mean value. TLE was defined as the root mean square of each difference between Pt and Pe during the monitoring of each fraction. These procedures were performed repeatedly using the remaining markers. To provide the best guidance on the answer with n and |TMD|, fiducial markers with a 3D |aRM ≥ 10 mm were selected. Finally, a total of 205, 282, and 76 TLEs that fulfilled the 3D |TMD| and 3D |aRM| criteria were obtained for n = 1, 2, and 3, respectively. Multiple regression analysis (MRA) was used to evaluate TLE as a function of |TMD| and |aRM| in each n.

RESULTS

|TMD| for n = 1 was larger than that for n = 3. Moreover, |aRM| was almost constant for all n, indicating a similar scale for the marker's motion near the lung tumor. MRA showed that |aRM| in the left-right direction was the major cause of TLE; however, the contribution made little difference to the 3D TLE because of the small amount of motion in the left-right direction. The TLE calculated from the MRA ((MRA)TLE) increased as |TMD| and |aRM| increased and adversely decreased with each increment of n. The median 3D (MRA)TLE was 2.0 mm (range, 0.6-4.3 mm) for n = 1, 1.8 mm (range, 0.4-4.0 mm) for n = 2, and 1.6 mm (range, 0.3-3.7 mm) for n = 3. Although statistical significance between n = 1 and n = 3 was observed in all directions, the absolute average difference and the standard deviation of the (MRA)TLE between n = 1 and n = 3 were 0.5 and 0.2 mm, respectively.

CONCLUSIONS

A large |TMD| and |aRM| increased the differences in TLE between each n; however, the difference in 3D (MRA)TLEs was, at most, 0.6 mm. Thus, the authors conclude that it is acceptable to continue fiducial marker-based radiotherapy as long as |TMD| is maintained at ≤58.7 mm for a 3D |aRM|  ≥  10 mm.

Differences in displacement of the proximal and distal ends of mid-upper thoracic esophageal squamous cell carcinoma

In the present study, clips were used as markers to evaluate displacement differences between proximal and distal ends of esophageal tumors and to test whether their internal target volume (ITV) margins should be determined separately. A total of 23 patients with mid-upper thoracic esophageal squamous-cell carcinoma, a tumor length of ≤8 cm and an esophageal lumen suitable for endoscopic ultrasonography were recruited for the present study. Clips were implanted endoscopically at the proximal and distal ends of the esophageal tumor (upper and lower clips). In a further exploratory study on 16 of the patients, a third clip was placed at the distal esophagus 2 cm above the gastro-esophageal junction (GEJ) (cardiac clip). The clips were contoured for all 10 phases of the four-dimensional computed tomography and the maximum displacements of the clip centroids among different breathing phases in left-right (LR), superior-inferior (SI) and anterior-posterior (AP) directions were marked as x, y and z, respectively. The ITV margins that covered 95% of the LR, SI and AP motion were 2.89, 5.00 and 2.36 mm, respectively. Axial displacement (y) was greater than radial displacement (x, z; P<0.05). It was also revealed that LR(x), SI(y) and AP(z) displacement of cardiac clips was greater than that of upper or lower clips (P<0.05). Differences in the axial and radial displacement of the upper and lower clips indicated that axial and radial ITV margins should be determined separately. However, further study is required on patients in whom the distal tumor end is located in proximity to the GEJ.

Real-time markerless lung tumor tracking in fluoroscopic video: Handling overlapping of projected structures

PURPOSE

Fluoroscopic imaging is a well-suited technique for online visualization of tumor motion in the thoracic region. Template-based approaches for tumor tracking in such images are commonly used. However, overlapping of different structures, mainly bones, can lead to limited visibility of the projected tumor shape, which in turn can negatively affect the performance of the tracking method. In this study, a method based on multiple-template matching was developed, providing fast and robust detection of tumor motion even under the influence of occurring tumor overlaps.

METHODS

A cohort of 14 patients with varying tumor sizes and locations was investigated. Image data from eight of these patients were used for evaluation. Based on the requirement of tumor visibility, the remaining datasets did not qualify for tracking. Generation of multiple templates was improved by implementation of an algorithm for automated selection of reference images containing the most characteristic tumor appearances. Various measures were taken to ensure real-time capability of the algorithm. A prematching step was introduced in order to reduce dispensable comparison operations by selecting the most appropriate template. Subsequent matching was further optimized by using prior knowledge about likely tumor motion to effectively limit necessary matching tasks.

RESULTS

Tracking accuracy of the developed multiple-template method was compared with that of single-template. Mean errors of the multiple-template approach were 0.6 ± 0.6 mm in left-right and 0.9 ± 0.9 mm in superior-inferior direction in the isocenter plane. The single-template approach achieved mean errors of 0.7 ± 0.7 mm in left-right and 1.5 ± 1.3 mm in superior-inferior direction. These results derive from evaluation against manual tumor tracking performed by four expert observers. Computational times needed for tumor detection in a single fluoroscopic frame ranged between 1 and 29 ms depending on the tumor size and motion amplitude.

CONCLUSIONS

This study shows that in case of tumor overlapping with dense structures, multiple-template tracking provides more accurate results than a single-template approach. The developed algorithm shows promising results in terms of suitability for real-time application and robustness against frequently changing overlapping.

Dynamic tumor-tracking radiotherapy with real-time monitoring for liver tumors using a gimbal mounted linac

PURPOSE

Dynamic tumor-tracking stereotactic body radiotherapy (DTT-SBRT) for liver tumors with real-time monitoring was carried out using a gimbal-mounted linear accelerator and the efficacy of the system was determined. In addition, four-dimensional (4D) dose distribution, tumor-tracking accuracy, and tumor-marker positional variations were evaluated.

MATERIALS AND METHODS

A fiducial marker was implanted near the tumor prior to treatment planning. The prescription dose at the isocenter was 48-60 Gy, delivered in four or eight fractions. The 4D dose distributions were calculated with a Monte Carlo method and compared to the static SBRT plan. The intrafractional errors between the predicted target positions and the actual target positions were calculated.

RESULTS

Eleven lesions from ten patients were treated successfully. DTT-SBRT allowed an average 16% reduction in the mean liver dose compared to static SBRT, without altering the target dose. The average 95th percentiles of the intrafractional prediction errors were 1.1, 2.3, and 1.7 mm in the left-right, cranio-caudal, and anterior-posterior directions, respectively. After a median follow-up of 11 months, the local control rate was 90%.

CONCLUSIONS

Our early experience demonstrated the dose reductions in normal tissues and high accuracy in tumor tracking, with good local control using DTT-SBRT with real-time monitoring in the treatment of liver tumors.

Retrospective evaluation of CTV to PTV margins using CyberKnife in patients with thoracic tumors

The objectives of this study were to estimate global uncertainty for patients with thoracic tumors treated in our center using the CyberKnife VSI after placement of fiducial markers and to compare our findings with the standard CTV to PTV margins used to date. Datasets for 16 patients (54 fractions) treated with the CyberKnife and the Synchrony Respiratory Tracking System were analyzed retrospectively based on CT planning, tracking information, and movement data generated and saved in the logs files by the system. For each patient, we analyzed all the main uncertainty sources and assigned a value. We also calculated an expanded global uncertainty to ensure a robust estimation of global uncertainty and to enable us to determine the position of 95% of the CTV points with a 95% confidence level during treatment. Based on our estimation of global uncertainty and compared with our general margin criterion (5 mm in all three directions: superior/inferior [SI], anterior/posterior [AP], and lateral [LAT]), 100% were adequately covered in the LAT direction, as were 94% and 94% in the SI and AP directions. We retrospectively analyzed the main sources of uncertainty in the CyberKnife process patient by patient. This individualized approach enabled us to estimate margins for patients with thoracic tumors treated in our unit and compare the results with our standard 5 mm margin.

PACS number: 87.55‐x

Prediction of clinical outcome after stereotactic body radiotherapy for non-small cell lung cancer using diffusion-weighted MRI and (18)F-FDG PET

PURPOSE/OBJECTIVES

To evaluate the use of diffusion-weighted magnetic resonance imaging (DW-MRI) and (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) for predicting disease progression (DP) among patients with non-small cell lung carcinoma (NSCLC) treated with stereotactic body radiotherapy (SBRT).

MATERIALS/METHODS

Fifteen patients with histologically confirmed stage I NSCLC who underwent pre-treatment DW-MRI and PET and were treated with SBRT were enrolled. The mean apparent diffusion coefficient (ADC) value and maximum standardised uptake value (SUVmax) were measured at the target lesion and evaluated for correlations with DP.

RESULTS

The median pre-treatment ADC value was 1.04×10(-3) (range 0.83-1.29×10(-3))mm(2)/s, and the median pre-treatment SUVmax was 9.9 (range 1.6-30). There was no correlation between the ADC value and SUVmax. The group with the lower ADC value (≤1.05×10(-3)mm(2)/s) and that with a higher SUVmax (≥7.9) tended to have poor DP, but neither trend was statistically significant (p=0.09 and 0.32, respectively). The combination of the ADC value and SUVmax was a statistically significant predictor of DP (p=0.036).

CONCLUSION

A low ADC value on pre-treatment DW-MRI and a high SUVmax may be associated with poor DP in NSCLC patients treated with SBRT. Using both values in combination was a better predictor.