Tumor Motion Research Articles

Experimental Validation of a Real-Time Adaptive 4D-Optimized Particle Radiotherapy Approach to Treat Irregularly Moving Tumors

Treatment of locally advanced lung cancer is limited by toxicity and insufficient local control. Particle therapy could enable more conformal treatment than intensity modulated photon therapy but is challenged by irregular tumor motion, associated range changes, and tumor deformations. We propose a new strategy for robust, online adaptive particle therapy, synergizing 4-dimensional optimization with real-time adaptive beam tracking. The strategy was tested and the required motion monitoring precision was determined. In multiphase 4-dimensional dose delivery (MP4D), a dedicated quasistatic treatment plan is delivered to each motion phase of periodic 4-dimensional computed tomography (4DCT). In the new extension, "MP4D with residual tracking" (MP4DRT), lateral beam tracking compensates for the displacement of the tumor center-of-mass relative to the current phase in the planning 4DCT. We implemented this method in the dose delivery system of a clinical carbon facility and tested it experimentally for a lung cancer plan based on a periodic subset of a virtual lung 4DCT (planned motion amplitude 20mm). Treatments were delivered in a quality assurance-like setting to a moving ionization chamber array. We considered variable motion amplitudes and baseline drifts. The required motion monitoring precision was evaluated by adding noise to the motion signal. Log-file-based dose reconstructions were performed in silico on the entire 4DCT phantom data set capable of simulating nonperiodic motion. MP4DRT was compared with MP4D, rescanned beam tracking, and internal target volume plans. Treatment quality was assessed in terms of target coverage (D95), dose homogeneity (D5-D95), conformity number, and dose to heart and lung. For all considered motion scenarios and metrics, MP4DRT produced the most favorable metrics among the tested motion mitigation strategies and delivered high-quality treatments. The conformity was similar to static treatments. The motion monitoring precision required for D95 >95% was 1.9mm. With clinically feasible motion monitoring, MP4DRT can deliver highly conformal dose distributions to irregularly moving targets.

The dosimetric error due to uncorrected tumor rotation during real-time adaptive prostate stereotactic body radiation therapy.

During prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correctionalone. The purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees-of-freedom tumor motion monitoringtechnology. The delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5-fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as . Prostate intrafractional rotational motion was recorded but not corrected. The dose differences from the planned dose due to + , ΔD( + ) and due to alone, ΔD( ), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation, was then quantified: = ΔD( + ) - ΔD( ). Fractional data analysis shows that the dose differences from the plan (both ΔD( + ) and ΔD( )) for CTV D98 was less than 5% in all treatment fractions. ΔD( + ) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation, induced residual dose error, , resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D( + ) and ∑D( ), was always within 5% of the planned dose for all four structures for everypatient. The dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3-5 mm CTV-PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetricaccuracy.

Quantifying 6D tumor motion and calculating PTV margins during liver stereotactic radiotherapy with fiducial tracking

Our study aims to estimate intra-fraction six-dimensional (6D) tumor motion with rotational correction and the related correlations between motions of different degrees of freedom (DoF), as well as quantify sufficient anisotropic clinical target volume (CTV) to planning target volume (PTV) margins during stereotactic body radiotherapy (SBRT) of liver cancer with fiducial tracking technique. A cohort of 12 patients who were implanted with 3 or 4 golden markers were included in this study, and 495 orthogonal kilovoltage (kV) pairs of images acquired during the first fraction were used to extract the spacial position of each golden marker. Translational and rotational motions of tumor were calculated based on the marker coordinates by using an iterative closest point (ICP) algorithm. Moreover, the Pearson product-moment correlation coefficients (r) were applied to quantify the correlations between motions with different degrees of freedom (DoFs). The population mean displacement ( ), systematic error (Σ) and random error (σ) were obtained to calculate PTV margins based on published recipes. The mean translational variability of tumors were 0.56, 1.24 and 3.38mm in the left-right (LR, X), anterior-posterior (AP, Y), and superior-inferior (SI, Z) directions, respectively. The average rotational angles θ X , θ Y and θ Z around the three coordinate axes were 0.88, 1.24 and 1.12, respectively. (|r|>0.4) was obtainted between Y -Z , Y - θ Z , Z -θ Z and θ X - θ Y . The PTV margins calculated based on 13 published recipes in X, Y, and Z directions were 1.08, 2.26 and 5.42mm, and the 95% confidence interval (CI) of them were (0.88,1.28), (1.99,2.53) and (4.78,6.05), respectively. The maximum translational motion was in SI direction, and the largest correlation coefficient of Y-Z was obtained. We recommend margins of 2, 3 and 7mm in LR, AP and SI directions, respectively.

Iterative learning control of a pneumatically actuated lung tumour mimic model for an improvement of PET/CT-imaging

In this paper, a model-based tracking control is proposed for a mechanism dedicated to accurately reproduce the breathing-induced motion of a human lung tumour. In former work, a 3-dimensional mechanism equipped with three pneumatically driven axes has been developed and constructed for this purpose. The overall control structure consists of decentralised axis controller for each pneumatic cylinder with a cascaded structure: A fast inner control loop governs the pressure induced drive force using backstepping techniques, whereas the outer control loop is related to an optimal, robust position control of the cylinder. Additionally, discrete-time iterative learning controllers are utilised to compensate lumped disturbance forces and model uncertainties. The suggested overall control structure has been implemented on the innovative test rig and successfully validated in a clinical framework. The given work represents the basis for future improvements of algorithms regarding PET/CT-imaging as well as radiotherapy.

Impact of spot reduction on the effectiveness of rescanning in pencil beam scanned proton therapy for mobile tumours

Objective. In pencil beam scanning proton therapy, individually calculated and positioned proton pencil beams, also referred to as ‘spots’, are used to achieve a highly conformal dose distributions to the target. Recent work has shown that this number of spots can be substantially reduced, resulting in shorter delivery times without compromising dosimetric plan quality. However, the sensitivity of spot-reduced plans to tumour motion is unclear. Although previous work has shown that spot-reduced plans are slightly more sensitive to small positioning inaccuracies of the individual pencil beams, the resulting shorter delivery times may allow for more rescanning. The aim of this study was to assess the impact of tumour motion and the effectiveness of 3D volumetric rescanning for spot-reduced treatment plans. Approach. Three liver and two lung cancer patients with non-negligible motion amplitudes were analysed. Conventional and probabilistic internal target volume definitions were used for planning considering single or multiple breathing cycles respectively. For each patient, one clinical and two spot-reduced treatment plans were created using identical field geometries. 4D dynamic dose calculations were then performed and resulting target coverage (V95%), dose homogeneity (D5%–D95%) and hot spots (D2%) evaluated for 1–25 rescans. Main results. Over all patients investigated, spot reduction reduced the number of spots by 91% in comparison to the clinical plan, reducing field delivery times by approximately 50%. This reduction, together with the substantially increased dose per spot resulting from the spot reduction process, allowed for more rescans in the same amount of time as for clinical plans and typically improved dosimetric parameters, in some cases to values better than the reference static (3D calculated) plans. However, spot-reduced plans had an increased possibility of interference with the breathing cycle, especially for simulations of perfectly repeatable breathing. Significance. For the patients analysed in this study, spot-reduced plans were found to be a valuable option to increase the efficiency of 3D volumetric rescanning for motion mitigation, if attention is paid to possible interference patterns.

Surface Imaging-Guided Adaptive Radiotherapy (ART): Imaging Protocol and Verification for Inter- and Intra-Fractional Motion Managements

<h3>Purpose/Objective(s)</h3> CBCT-guided adaptive radiotherapy (CT-ART) has proven to account for inter-fractional anatomic changes. However, no commercial solution for intra-fractional respiratory motion management existed in CT-ART. Therefore, we investigated intra-fractional motion-managed treatment strategies in CT-ART (or Image-guided RT: IGRT) using optical surface imaging combined with <i>in-situ</i> CBCTs. <h3>Materials/Methods</h3> Eight of ten cancer patients with mobile lower lung or upper abdominal malignancies have completed CT-ART (or IGRT) using Ethos radiotherapy in an IRB-approved clinical trial (phase I). Four of the eight patients underwent additional MR simulations, including cine MRIs, to estimate the tumor/abdominal skin motion correlation. In CT-ART, a pre-configured gating window (±2 mm in vertical position) on surface imaging was used for manually triggering intra-fractional CBCT and treatment beam irradiation in breath-hold (six patients for exhalation and two patients for inhalation). A donut-shaped imaging-surrogate on patient's abdomen was used to localize Region of Interest (ROI) of the abdomen on CBCTs. Two inter-fractional CBCTs at exhalation and inhalation in each fraction were acquired to verify the tumor/imaging-surrogate motions range. Intra-fractional CBCTs were used to quantify the residual motion of the tumor/imaging-surrogate within the pre-configured gating window. Tumor motion was measured by tracking the position of a tumor centroid. <h3>Results</h3> The pre-configured gating window on surface imaging was confirmed based on the correlation of the tumor and abdominal skin motions using cine MRIs. Thirty-six pairs of inter-fractional CBCTs and 100 intra-fractional CBCTs from 36 fractions were analyzed (13 fractions for IGRT and 23 for ART). In the motion range study from the inter-fractional CBCTs, the values of mean ± std were 17.3 ± 12.0 mm in the SI direction for the tumors and 16.6 ± 7.9 mm in the AP direction for the imaging-surrogate, respectively. The residual motion of the imaging-surrogate from intra-fractional CBCTs was 2.5 ± 2.1 mm for SI, 2.3 ± 2.0 mm for RL, and 2.1 ± 2.3 mm for AP directions. The residual motion of the tumors was 2.4 ± 3.1 mm for SI, 2.3 ± 2.3 mm for RL, and 2.2 ± 2.0 mm for AP directions. Trends in the residual motion during the course of treatment fractions and over time through delivery were stable. Inter-fractional residual motions of the tumors were 2.3 ± 0.8 mm for SI, 2.3 ± 0.6 mm for RL, and 2.2 ± 0.7 mm for AP directions. Intra-fractional residual motions of the tumors were 2.4 ± 0.9 mm for SI, 2.4 ± 0.7 mm for RL, and 3.2 ± 1.3 mm for AP. <h3>Conclusion</h3> We demonstrated the inter- and intra-fractional motion-managed treatment strategies in CBCT-guided ART using surface imaging. Since the residual motion of the tumors was comparable to that of the imaging-surrogate within clinical PTV margins (5 mm) but a bit larger than the pre-configured gating window, additional caution is needed with intra-fractional motion management in CT-ART using the optical surface imaging.

Lung Tumor Motion and its Impact on Deep Learning Prediction of Local Recurrence

<h3>Purpose/Objective(s)</h3> In order for deep learning (DL) models to successfully predict local recurrence of lung tumors from image-based features, uncertainties of the model must be quantified. One potential source of uncertainty for the DL model is lung tumor motion. We aimed to evaluate the association of motion magnitude with the prediction accuracy of the DL model. We hypothesized that knowledge of motion will help quantify the uncertainties of prediction and guide the use of individualized risk predictions for lung radiotherapy. <h3>Materials/Methods</h3> Physician delineated gross tumor volume (GTV) of 19 patients were propagated to their corresponding 10 breathing phases of the 4DCT images using a commercial treatment planning system. A programming environment code was used to extract the contours and determine the centroid and volume bounded by the contours. Motion was determined from the displacement of the GTV centroid. The Euclidean distance was calculated and used as an aggregate measure of tumor motion magnitude. The 4DCT, free-breathing CT (FBCT) and maximum-intensity projection (MIP) images of the 18 patients were used as input to a pre-trained DL model. The 8-layer DL model was previously trained to predict local recurrence using FBCT images of 933 lung patients. Correlation between motion magnitude and prediction accuracy was calculated for 4DCT images in each respiratory phase, FBCT and MIP images. <h3>Results</h3> Tumor movement with a mean exertion of 1 cm [IQR: 0.72-1.53] was observed from the cohort with 47.4 % of patients subjected to abdominal compression, and 57.9% of patients had peripheral lung tumors. The standard deviation of DL scores obtained from each 4DCT image correlated with the magnitude of tumor motion (R² =0.45, p=0.005). Higher uncertainty in DL scores was attributed to the larger tumor motion. The average DL score obtained from the 10 phases of the 4DCT images of each patient was (i) highly associated with the DL scores obtained from the FBCT images (r = 0.99; p<0.0001), and (ii) closest to the DL score obtained with the end-inspiration images. In comparing the prediction performance across image acquisition types, MIP images yields a higher DL score compared to FBCT (<i>p</i><0.0001) or 4DCT images (<i>p</i> =<0.0001). <h3>Conclusion</h3> The impact of tumor motion on the range of DL scores for prediction of local recurrence was reported for the first time. Standard deviations of the DL scores increased with motion magnitude. However, when DL score was measured and averaged across the 10 phases of the 4DCT images, DL score correlated well with those obtained from the FBCT images. This preliminary finding suggests that despite the increased variation with larger motion, the DL score, when averaged across the sinusoidal breathing trajectory, correlates with the DL score obtained from the FBCT images that were used for treatment planning. However, caution should be exercised when using MIP images for local recurrence prediction since a higher DL score than the FBCT images used for planning will be observed.

Real-Time Motion Monitoring Using Orthogonal Cine MRI during MR-Guided Radiation Therapy for Liver Cancer

<h3>Purpose/Objective(s)</h3> This work investigates the feasibility of real-time motion monitoring (MM) using orthogonal cine MRI during MR guided Adaptative Radiation Therapy (MRgART) for liver tumors that are normally invisible on cine MRI with no contrast agent. <h3>Materials/Methods</h3> An investigational package, Motion Monitoring Research Package (MMRP), was developed to measure real-time target motion between orthogonal cine MRI and the daily baseline 3D MRI. The MMRP package was evaluated on 8 liver cancer patients treated on a 1.5T MR-Linac. For all patients, a 3D mid-position (MidP) image derived from a daily 4D MRI was used to delineate a sub-liver volume (SLV) encompassing the tumor. During the same session, real-time 2D T2/T1-weighted bFFE cine MRI were collected with free breathing during MRgART. The cine images were acquired with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. MMRP workflow included two steps: (1) creating a 2D template in each orientation using cine data acquired over the first 12 s, followed by a rigid registration between the combined templates and the MidP image over the SLV region; (2) measuring the SLV motion by registering the rest of the 2D cine series with the corresponding template. To evaluate the MM accuracy from MMRP, the ground-truth contours on cine MRI were created manually. Common visible vessels and segments of liver boundaries in proximity to the tumor were used as anatomical landmarks for reproducible delineations on both the 3D and cine images. MM accuracy was measured using the standard deviation of the error (SDE) to compare the center of mass position of the ground-truth and the monitored motion. Maximum of tumor motion was assessed on 4D MRI. <h3>Results</h3> In 6 of the 8 cases, the assessed mean (range) centroid motions were 8.8 (4.6-13.8), 1.5 (0.7-2.8) and 3.2 (1.5-5) mm with mean SDE (range) values of 1.2 (0.7-1.6), 0.94 (0.4-1.7), and 1 (0.4-1.4) mm in superior inferior (SI), left right, and anterior posterior (AP) directions, respectively. The mean (range) of the maximum tumor motion from the 4D MRI was 8 (5-11) mm in SI direction, smaller than the centroid MM, indicating the importance of motion capture using the MM in real-time. For the remaining two cases, quantitative MM was challenging due to target deformation and large through-plane motion in the AP direction. <h3>Conclusion</h3> We have demonstrated the feasibility of accurate real-time MM of a liver tumor using a sub-liver volume as a tumor surrogate, based on real-time orthogonal cine MRI during MRgART. The approach overcomes difficulty due to the invisibility of liver tumor on the T2/T1-w 2D cine without using a contrast agent and the need of implanting radio-opaque clips or the approximation of the whole liver motion as the tumor motion. With further investigation with more patient data, the real-time MM technique may be implemented in MRgART to manage intrafraction liver motion.

Contrast-Enhanced 4D MRI for Internal Target Volume Generation in Treatment Planning for Liver Tumors

<h3>Purpose/Objective(s)</h3> Treatment planning for liver tumors is challenging, as they have variable respiratory motion and are often invisible on four-dimensional commuted tomography (4DCT), even with use of intravenous contrast. Thus, imperfect imaging surrogates are used to estimate the motion of tumors. 4D magnetic resonance (MR) with hepatobiliary contrast has the potential to allow for direct visualization of tumors through the respiratory cycle. Thus, we assessed multiple reconstruction algorithms for directly visualizing liver tumor motion for radiotherapy planning and compared treatment plans with those generated using 4DCT. <h3>Materials/Methods</h3> Patients were simulated using a 3T MR and CT scanner. Three prototype binning algorithms (phase, amplitude, and two-directional) were applied to the 4DMRs. Phase and amplitude binning averaged signal from both the inhale and exhale trajectories for theoretically improved signal to noise ratio, while two-directional binning separated trajectories for theoretically improved spatial resolution. Image quality for tumor delineation was assessed using a qualitative clarity score and quantitative sharpness score. Radiation plans were generated for internal target volumes (ITVs) derived using 4DMR and 4DCT, and the dosimetry compared. Paired t-tests were used to compare sharpness scores and dosimetric data. <h3>Results</h3> Twelve patients with 17 liver tumors were scanned between May and November 2021. Compared to phase binning, two-directional binning demonstrated equal or better clarity and sharpness scores (end-expiration: 0.33 vs. 0.38, p=0.018, end-inspiration: 0.28 vs. 0.31, p=0.010). Compared to amplitude binning, two-directional binning captured hysteresis (anterior displacement of the tumor during respiration) of ≥3 mm in 35% of patients. Dosimetric evaluation of 4DCT-derived plans revealed resulting underdosage of the PTV where the 4DCT did not capture the full extent of 4DMRI-defined PTV, (PTV receiving 90% of prescription: 75.56% vs. 89.38%, p=0.002). <h3>Conclusion</h3> Using contrast-enhanced 4D MRI is feasible for directly delineating liver tumors throughout the respiratory cycle. Two-directional binning capture hysteresis in 1/3 of patients. The current standard of creating radiation plans optimized for 4DCT-derived targets achieved lower coverage of MRI-derived targets, suggesting that adopting MRI for motion management may improve radiation treatment of liver lesions and reduce the risk of marginal misses.

Endobronchial Implanted Real-Time Radiofrequency (RF) Transponder Beacon Guided, Respiratory-Gated, Stereotactic Body Radiotherapy for Moving Lung Tumors: Interim Analysis of a Prospective Phase I/II Cohort Study

<h3>Purpose/Objective(s)</h3> Endobronchially implanted real-time tracking system (technology company) radiofrequency (RF) transponder beacons provide real-time, high precision, positional data of moving lung tumors. We report interim results of a Phase I/II prospective single arm prospective cohort study evaluating feasibility and dosimetric impact of real-time tracking system beacons for patients undergoing SBRT for moving lung tumors. <h3>Materials/Methods</h3> Eligible patients were adults, ECOG 0-2, with T1-T2N0 Non-small cell lung cancer (NSCLC) or pulmonary metastasis ≤4cm of the right middle/lower lobe, or lingula. Upper lobe tumors were eligible if they moved ≥5mm. Three real-time tracking system beacons were endobronchially implanted at prespecified peritumoral locations using SuperDimension™ navigational bronchoscopy (Medtronic, USA). Four-dimensional free-breathing CT simulation scans were obtained and end-exhalation phases were selected to define a gating window (GW). A 3mm expansion of the ITV_GW defined the PTV. Real-time tracking system-guided, respiratory phase-gated, SBRT (GW-SBRT) was delivered using 10MV VMAT photon arcs at 2400MU/min to total doses of 54Gy/3# or 48Gy/4#. For each GW-SBRT plan, a corresponding 10-phase plan was generated for comparison to standard image-guided (IG)SBRT. PTV, and OAR metrics were tabulated and differences between GW-SBRT and IG-SBRT plans analyzed using non-parametric Wilcoxon Signed-Rank pair test. Treatment related toxicity was graded using the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. <h3>Results</h3> From November 2017 to December 2021, 41 patients were screened and 17 patients consented to participate. Two patients did not undergo beacon implant: one had tumor progression; another withdrew consent. Of the 15 patients with beacon implants, median age was 73, with 7 females. 47% were T1 NSCLC, 40% were M1 and remaining 13% were T2 NSCLC. Median tumor dimension was 1.9cm. 73% of targets were located peripherally, and 27% centrally with a median respiratory tumor motion of 1.25 cm (range 0.53 cm to 4.04 cm). No beacon migration was observed between CT simulation scan and the completion of SBRT treatments. Of the N=12 tumor targets were treated with GW-SBRT, 47% of patients received 48Gy/4# and 53% received 54Gy/3#. GW-SBRT yielded an average relative reduction of PTV of 46.9% (p<0.005) compared with IG-SBRT. Lung V5, V10, V20 and D2 had mean relative reductions of 11.3%, 20.3%, 31.1% and 15.5% respectively (p<0.005). Dose to OARs was significantly reduced (p<0.05) except for spinal cord. At 6 months follow-up, mean radiographic tumor volume reduction was 53.5% (p<0.005); No treatment related grade 3 toxicity was observed. <h3>Conclusion</h3> Endobronchial RF beacons are safe and effective at reducing SBRT treatment volumes for patients with moving lung tumors and can be considered for tumors with large motion amplitude or those located in close proximity to organs at risk. This study has the ClinicalTrials.gov identifier NCT03322072.

Deep Learning-Driven Real-Time Liver Tumor Localization via Optical Surface Imaging and Biomechanical Modeling

<h3>Purpose/Objective(s)</h3> To track liver tumors in real time via a deep learning-based framework, by exploring patient-specific correlations between patients' body surface maps and internal anatomical motion. <h3>Materials/Methods</h3> Real-time liver tumor localization was achieved via deformable registration-based motion estimation, in a two-step framework: (a) liver boundary motion estimation via deep features learnt from optical body surface imaging; and (b) intra-liver motion propagation via deep learning-based biomechanical modeling from liver boundary motion. In step (a), a patient-specific, fully-connected convolutional neural network (SurfCNN) predicts the motion of nodes residing on the boundary of a patient-specific reference liver mesh, by extracting saliency features from curvature entropy maps derived out of real-time optical body surface images. Subsequently, step (b) uses the liver boundary motion solved in (a) to infer intra-liver tumor motion, using a U-Net-style model (UNet-Bio) inspired by biomechanical modeling via finite element analysis (FEA). The cascaded framework was evaluated using a dataset of 8 liver cancer patients from our institute. Each patient had a 10-phase, contrast-enhanced 4D-CT set with liver and liver tumor contoured. To generate sufficient motion variations to train and test the patient-specific SurfCNN model, we augmented the dataset of each patient by simulating varying real-time motion patterns and magnitudes. The augmentation was achieved via a principal component analysis-based statistical model, expanding the nine inter-phase deformation-vector-fields (DVFs) of each 4D-CT into 1,728 different motion states including both rigid and deformable motion. Optical surface imaging was simulated from each motion state via extracted body surface maps, while corresponding liver boundary motion was derived via the augmented DVFs to supervise the training of SurfCNN. UNet-Bio, on the other hand, could be trained as a population-based model using supervisions from intra-liver DVFs solved by FEA-based biomechanical modeling. The liver tumor localization accuracy was assessed through Dice similarity coefficient (DSC), Hausdorff distance (HD), and center-of-mass-error (COME), by comparing the augmentation DVF-propagated ‘ground-truth' liver tumor volumes against ones deformed by the cascaded framework. <h3>Results</h3> Tested using 576 unseen scenarios for each patient case, the cascaded SurfCNN and UNet-Bio scheme can localize liver tumors to 0.791(mean) ± 0.141 (s.d.) in DSC, 3.6 ± 1.9 mm in HD, and 2.3 ± 1.7 mm in COME. In comparison, the prior reference image without deformable registration yielded 0.534 ± 0.277 in DSC, 8.3 ± 6.1 mm in HD and 6.8 ± 5.8 mm in COME. The whole model inference time was less than 100 milliseconds, satisfying the temporal constraints of real-time imaging. <h3>Conclusion</h3> The deep learning-based framework allows accurate 3D liver tumor tracking in real-time via non-ionizing, marker-less, and high frame-rate optical surface imaging.

Application of Optical Surface Monitoring System in Stereotactic Body Radiation Therapy for Liver Metastases Patient

<h3>Purpose/Objective(s)</h3> Stereotactic body radiotherapy (SBRT) in liver tumors has been a challenging treatment due to the diaphragm-induced motion. Optical surface monitoring system (OSMS) provides the functions of positioning and motion management, which has been widely used in recent years because it is non-ionizing, non-invasive and contactless. The purpose of this study was to explore the application of OSMS in liver metastasis patients. <h3>Materials/Methods</h3> 33 liver metastasis SBRT patients, who were treated between August 2019 to September 2021, were divided into two groups. 14 patients in Group A were aligned using skin marking (1 anterior and 2 laterals). 19 patients in Group B were aligned near to the skin marking at the initial step. Next, the ‘move couch' function on OSMS was applied if the patient's body rotations were less than 1 degrees according to the OSMS deltas value. The setup errors and setup time were recorded in both groups. <h3>Results</h3> Total of 88 and 106 CBCT images in group A and B were analyzed. The absolute setup errors for Groups A and B were (0.21 ± 0.15) cm, (0.37 ± 0.38) cm, (0.35 ± 0.33) cm, (0.8 ± 0.3), (1.2±0.5), (1.8 ± 0.7), and (0.04 ± 0.02) cm, (0.28 ± 0.13) cm, (0.03 ± 0.02) cm, (0.1 ± 0.2), (0.1 ± 0.2), and (0.3 ± 0.4) in vertical, longitudinal, lateral, rotation, pitch and roll directions respectively. Positioning accuracy in Group B is far superior to Group A especially in rotational directions. The difference was statistically significant (p<0.05). The average setup time was 2.8 minutes and 3.0 minutes for Group A and B. There was no significant difference in mean setup time. Setup time using OSMS was gradually improved as more experiences gained by the therapist. <h3>Conclusion</h3> The study has proven that OSMS is an effective and efficient tool for liver metastasis patients. It can increase the setup accuracy and reduce repeated setup. Continuous real time monitoring is of high clinical value for liver tumor motion management.

Retrospective Analysis of Replan Frequency and Causes in Esophageal Cancer Patients Treated with Spot Scanned Proton Therapy

<h3>Purpose/Objective(s)</h3> For patients with esophageal cancer treated with intensity-modulated proton therapy (IMPT) at our institution, weekly verification CT scans are obtained to ensure that the plan is still acceptable. If unacceptable deviations from the intended dose are observed, replanning is performed. This study aims to investigate the frequency and causes of replans to determine the appropriate frequency of performing verification scans. <h3>Materials/Methods</h3> Data from a cohort of 205 esophageal cancer patients treated with IMPT between 2015 and 2020 were retrospectively analyzed. Patients were typically positioned head first supine with arms up in a vacuum immobilization device, and a four-dimensional CT (4DCT) simulation scan was obtained. The typical prescription was 50 Gy in 25 fractions over 5 weeks, with contours delineated as per consensus guidelines. For tumor motion ≤ 10 mm, patients were treated free-breathing with isolayer repainting, while for motion > 10 mm, gating was also added. Two posterior oblique beams were typically used, and single field optimization was performed. Robust optimization was utilized with 5-mm translational perturbations and 3% range uncertainty, with dose evaluation, also performed on the extreme respiratory phases. Weekly 4DCT verification scans in treatment position were acquired. Rigid image registration to the original planning CT image set was performed, and the care team reviewed the recalculated dose distributions to determine the need for replan. CTV (V95%) % was selected as a metric to compare the difference in target volume dose between verification scans that did [CTV_replan (V95%) %], versus did not [CTV_no-replan (V95%) %], lead to a replan. <h3>Results</h3> The primary tumor location was upper/middle esophagus (16%) or lower/gastroesophageal junction (83%). 21 patients (10%) required replanning, including 3 patients with 2 replans. Replans occurred during the 1^st (20%), 2^nd (33%), 3^rd (20%), or 4^th (25%) weeks of treatment. Primary reasons for replanning were a superior shift of the diaphragm baseline position affecting the range of the beams entering near this interface (46%), changes in tumor size or position (17%), new or resolving pleural effusion affecting the range of beams (13%), changes in stomach filling affecting the target geometry (13%), and patient setup reproducibility issues (13%). The average value of CTV_no-replan (V95%) % = 98% ± 3% vs. CTV_replan (V95%) % = 96% ± 1%. The modest size of this difference suggests that other factors, including poor fidelity of rigid CTV contours, also contribute to clinical decision-making. <h3>Conclusion</h3> In this large cohort of patients with esophageal cancer treated with robustly planned IMPT, the rate of adaptive replanning was 10% and resulted from 5 discrete changes as described above. Every other week frequency of verification scans should be considered.

Clinical applicability of deep learning-based respiratory signal prediction models for four-dimensional radiation therapy.

For accurate respiration gated radiation therapy, compensation for the beam latency of the beam control system is necessary. Therefore, we evaluate deep learning models for predicting patient respiration signals and investigate their clinical feasibility. Herein, long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and the Transformer are evaluated. Among the 540 respiration signals, 60 signals are used as test data. Each of the remaining 480 signals was spilt into training and validation data in a 7:3 ratio. A total of 1000 ms of the signal sequence (Ts) is entered to the models, and the signal at 500 ms afterward (Pt) is predicted (standard training condition). The accuracy measures are: (1) root mean square error (RMSE) and Pearson correlation coefficient (CC), (2) accuracy dependency on Ts and Pt, (3) respiratory pattern dependency, and (4) error for 30% and 70% of the respiration gating for a 5 mm tumor motion for latencies of 300, 500, and 700 ms. Under standard conditions, the Transformer model exhibits the highest accuracy with an RMSE and CC of 0.1554 and 0.9768, respectively. An increase in Ts improves accuracy, whereas an increase in Pt decreases accuracy. An evaluation of the regularity of the respiratory signals reveals that the lowest predictive accuracy is achieved with irregular amplitude patterns. For 30% and 70% of the phases, the average error of the three models is <1.4 mm for a latency of 500 ms and >2.0 mm for a latency of 700 ms. The prediction accuracy of the Transformer is superior to LSTM and Bi-LSTM. Thus, the three models have clinically applicable accuracies for a latency <500 ms for 10 mm of regular tumor motion. The clinical acceptability of the deep learning models depends on the inherent latency and the strategy for reducing the irregularity of respiration.

The potential of biology-guided radiation therapy in thoracic cancer: A preliminary treatment planning study.

PurposeWe investigated the feasibility of biology-guided radiotherapy (BgRT), a technique that utilizes real-time positron emission imaging to minimize tumor motion uncertainties, to spare nearby organs at risk.MethodsVolumetric modulated arc therapy (VMAT), intensity-modulated proton (IMPT) therapy, and BgRT plans were created for a paratracheal node recurrence (case 1; 60 Gy in 10 fractions) and a primary peripheral left upper lobe adenocarcinoma (case 2; 50 Gy in four fractions).ResultsFor case 1, BgRT produced lower bronchus V40 values compared to VMAT and IMPT. For case 2, total lung V20 was lower in the BgRT case compared to VMAT and IMPT.ConclusionsBgRT has the potential to reduce the radiation dose to proximal critical structures but requires further detailed investigation.

ALERT-RA: an aperture library-enabled real-time respiratory motion adaptive framework for 4D-VMAT.

To develop a framework for robust optimization of real-time respiratory motion adaptive VMAT treatment plans, and to evaluate the robustness of resulting plans to variations in tumor trajectory duringdelivery. The proposed framework is called aperture library-enabled real-time robust adaptation (ALERT-RA). A patient-specific library of optimized MLC apertures is defined for each combination of gantry angle and respiratory phase. The method assumes that the tumor is tracked in real-time throughout delivery, and the aperture corresponding to the current phase and gantry angle will be delivered. The aperture library is optimized by considering all possible tumor trajectories determined by a probabilistic respiratory motionmodel. Plan robustness to trajectory variations was evaluated by sampling a trajectory, and determining the corresponding dose, from the respiratory model for each fraction. The cumulative dose of the full treatment course was simulated 50 times. Percentile dose-volume histograms (PDVHs) were computed from these simulated treatments. The resulting plan quality and robustness of this method were compared to other previously published motion 4D-VMAT methods, including: an optimized tracking approach that assumes reproducible tumor motion, conformal tracking with aperture deformation, and a motion-encompassing method. Two fractionation schemes were tested to determine the possible effect on robustness: a conventional fractionation of 66 Gy in 33 fractions, and an SBRT course with 60 Gy in 5fractions. When considering target coverage, the ALERT-RA method was found to produce a plan which was more robust than those produced using the optimized or conformal tracking methods. Using the PDVH analysis, the 5th and 95th percentiles of the prescription dose volume for the conventionally fractioned plan were found to be (respectively) 79% and 82% for the optimized tracking approach, 81% and 83% for the conformal tracking approach, and 92% and 97% using the new ALERT-RA method. The motion-encompassing plan was slightly more robust than the ALERT-RA plan, with 5th and 95th percentiles at 94% and 95%, respectively. This came at a cost of higher dose to OARs, with the volume of lung receiving 5 Gy or more equal to 48% for the motion-encompassing plan versus 44% for the ALERT-RAplan. For the SBRT plan, the conformal tracking plan was similarly not robust, with 5th and 95th percentiles of the prescription dose volume equal to 88% and 89%. The optimized tracking SBRT plan gave values of 93% and 95%, and the motion-encompassing plan 94% and 95%, while the ALERT-RA gave values of 93% and 96%. The volume of lung receiving 20 Gy or more was slightly higher for the optimized tracking and motion-encompassing plans compared to the ALERT-RA plan, at 15%, 15%, and 14%,respectively. Compared to other motion-adaptive VMAT approaches, the ALERT-RA algorithm is capable of delivering high-quality plans which are robust to variations in tumor motiontrajectories.

Same-day consultation, simulation and lung Stereotactic Ablative Radiotherapy delivery on a Magnetic Resonance-linac.

Magnetic resonance-guided radiotherapy (MRgRT) with real-time intra-fraction tumor motion monitoring allows for high precision Stereotactic Ablative Radiotherapy (SABR). This study aimed to investigate the clinical feasibility, patient satisfaction and delivery accuracy of single-fraction MR-guided SABR in a single day (one-stop-shop, OSS). Ten patients with small lung tumors eligible for single fraction treatments were included. The OSS procedure consisted of consultation, treatment simulation, treatment planning and delivery. Following SABR delivery, patients completed a reported experience measure (PREM) questionnaire. Prescribed doses ranged 28-34Gy. Median GTV was 2.2cm3 (range 1.3-22.9cm3). A gating boundary of 3mm, and PTV margin of 5mm around the GTV, were used with auto-beam delivery control. Accuracy of SABR delivery was studied by analyzing delivered MR-cines reconstructed from machine log files. All 10 patients completed the OSS procedure in a single day, and all reported satisfaction with the process. Median time for the treatment planning step and the whole procedure were 2.8h and 6.6h, respectively. With optimization of the procedure, treatment could be completed in half a day. During beam-on, the 3mm tracking boundary encompassed between 78.0 and 100% of the GTV across all patients, with corresponding PTV values being 94.4-100% (5th-95th percentiles). On average, system-latency for triggering a beam-off event comprised 5.3% of the delivery time. Latency reduced GTV coverage by an average of -0.3 %. Duty-cycles during treatment delivery ranged from 26.1 to 64.7%. An OSS procedure with MR-guided SABR for lung cancer led to good patient satisfaction. Gated treatment delivery was highly accurate with little impact of system-latency.

Dosimetric robustness of lung tumor photon radiotherapy evaluated from multiple event CT imaging

Purpose:Intrafractional respiratory motion is a concern for lung tumor radiotherapy but full evaluation of its impact is hampered by the lack of images representing the true motion. This study presents a novel evaluation using free-breathing images acquired over realistic treatment times to study the dosimetric impact of respiratory motion in photon radiotherapy. Methods:Cine-CT images of 14 patients with lung cancer acquired during eight minutes of free-breathing at three occasions were used to simulate dose tracking of four different planning methods. These methods aimed to deliver 54 Gy in three fractions to D50% of the target and were denoted as robust 4D (RB4), homogeneous fluence to the ITV (FLU) and an isodose prescription to the ITV with a high central dose (ISD), concurrently renormalized (IRN). Differences in dose coverage probability and homogeneity between the methods were quantified. Correlations between underdosage and attributes regarding the tumor and its motion were investigated. Results:Despite tumor motion amplitudes being larger than in the 4DCT all but FLU achieved the intended CTV D50% for the cohort average. For all methods but IRN at least 93% of the patients would have received 95% of the intended dose. No differences in D50% were found between RB4 and ISD nor IRN. However, RB4 led to better homogeneity. Conclusions:Tumor motion in free-breathing not covered by the 4DCT had a small impact on dose. The RB4 is recommended for planning of free-breathing treatments. No factor was found that consistently correlated dose degradation with patient or motion attributes.

A phantom-based analysis for tracking intra-fraction pancreatic tumor motion by ultrasound imaging during radiation therapy.

PurposeIn this study, we aim to further evaluate the accuracy of ultrasound tracking for intra-fraction pancreatic tumor motion during radiotherapy by a phantom-based study.MethodsTwelve patients with pancreatic cancer who were treated with stereotactic body radiation therapy were enrolled in this study. The displacement points of the respiratory cycle were acquired from 4DCT and transferred to a motion platform to mimic realistic breathing movements in our phantom study. An ultrasound abdominal phantom was placed and fixed in the motion platform. The ground truth of phantom movement was recorded by tracking an optical tracker attached to this phantom. One tumor inside the phantom was the tracking target. In the evaluation of the results, the monitoring results from the ultrasound system were compared with the phantom motion results from the infrared camera. Differences between infrared monitoring motion and ultrasound tracking motion were analyzed by calculating the root-mean-square error.ResultsThe 82.2% ultrasound tracking motion was within a 0.5 mm difference value between ultrasound tracking displacement and infrared monitoring motion. 0.7% ultrasound tracking failed to track accurately (a difference value > 2.5 mm). These differences between ultrasound tracking motion and infrared monitored motion do not correlate with respiratory displacements, respiratory velocity, or respiratory acceleration by linear regression analysis.ConclusionsThe highly accurate monitoring results of this phantom study prove that the ultrasound tracking system may be a potential method for real-time monitoring targets, allowing more accurate delivery of radiation doses.

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT). We input pre-therapy free breathing (FB) computed tomography (CT) images from 849 patients treated with lung SBRT into a multitask deep neural network to generate an image fingerprint signature (or DL score) that predicts time-to-event local failure outcomes. The network includes a convolutional neural network encoder for extracting imaging features and building a task-specific fingerprint, a decoder for estimating handcrafted radiomic features, and a task-specific network for generating image signature for radiotherapy outcome prediction. The impact of tumor motion on the DL scores was then examined for a holdout set of 468 images from 39 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images. Tumor motion was estimated using a 3D vector of the maximum distance traveled, and its association with DL score variance was assessed by linear regression. The variance and amplitude in 4D CT image-derived DL scores were associated with tumor motion (R2 =0.48 and 0.46, respectively). Specifically, DL score variance was deterministic and represented by sinusoidal undulations in phase with the respiratory cycle. DL scores, but not tumor volumes, peaked near end-exhalation. The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r=0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p<0.0001). An image-based DL risk score derived from a series of 4D CT images varies in a deterministic, sinusoidal trajectory in a phase with the respiratory cycle. These results indicate that DL models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.