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PURPOSE: Consistency and standardization of radiotherapy plan quality in multi-institutional clinical trials are always challenging, and non-protocol compliant radiotherapy plans have been shown to impact patient outcomes. This study aimed to use knowledge-based planning (KBP) as a method of producing high quality, consistent, protocol-compliant treatment plans, in a complex setting of spine SBRT on NRG Oncology RTOG 0631. METHODS: An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues. RESULTS: The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of PTV receiving prescription dose increased from 93.3 ± 3.2% to 98.3 ± 1.4% (P=0.01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cc for manual plans to 1.8 ± 0.6 cc with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. KBP plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target. KBP applied to NRG Oncology RTOG 0631 CONCLUSION: Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results due to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are non-standard.

Supplementary A: Feature selection methods. Supplementary B: Machine learning methods. Supplementary C: The values selected for the hyper-parameters on each validation fold. Supplementary D: P-values of the log-rank test for all the feature selection and ML methods on each validation fold. (PDF 625 kb)

We address the challenges associated with applying a single isocenter treatment technique to extracranial oligometastases.We propose a technique that uses a Single Isocenter with Distinct Optimizations (SIDO) in which all Volumetric Modulated Arc Therapy (VMAT) fields share an isocenter but each field treats only one target. When necessary, setup uncertainties from rotations and deformations are mitigated by applying a couch translation between VMAT arcs, and interplay is minimized using dynamic conformal arcs (DCA) as the starting point for inverse optimization. Using CBCTs from eleven previous SBRT cases we determined the likelihood of needing a translational shift between SIDO arcs to correct rotational uncertainties. We also compared SIDO and SIDO with DCA to single (VMAT) and dual (VMAT and DCA) isocenter plans for phantom (N=11) and patient (N=4) cases.Spatial uncertainties from inter-fractional rotations were greatest for large target separation and small margins, with ~30% of fractions requiring a correction (3mm margin and 10cm separation). SIDO with DCA greatly decreased arc modulation, with approximately 20% more modulation than DCA, and similar conformity to conventional VMAT. SIDO and SIDO with DCA had comparable conformity to single and dual isocenter plans when separation between PTVs is3cm, while traditional single isocenter VMAT had superior conformity for3cm. SIDO with DCA had superior GI over other planning techniques for almost all cases.SIDO for extracranial oligometastases allows flexibility to mitigate spatial uncertainties from rotation and deformation, and has comparable dosimetry to traditional VMAT with low modulation when inverse optimization begins with DCA.

To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement.Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images.Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89 ± 0.03, and single cycle sorting, DSC = 0.83 ± 0.05, p-value0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images.In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management.

Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods.Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance.For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference.A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT.

The purpose of this study was to develop a techique to generate on-board volumetric cine-magnetic resonance imaging (VC-MRI) using patient prior images, motion modeling, and on-board 2-dimensional cine MRI.One phase of a 4-dimensional MRI acquired during patient simulation is used as patient prior images. Three major respiratory deformation patterns of the patient are extracted from 4-dimensional MRI based on principal-component analysis. The on-board VC-MRI at any instant is considered as a deformation of the prior MRI. The deformation field is represented as a linear combination of the 3 major deformation patterns. The coefficients of the deformation patterns are solved by the data fidelity constraint using the acquired on-board single 2-dimensional cine MRI. The method was evaluated using both digital extended-cardiac torso (XCAT) simulation of lung cancer patients and MRI data from 4 real liver cancer patients. The accuracy of the estimated VC-MRI was quantitatively evaluated using volume-percent-difference (VPD), center-of-mass-shift (COMS), and target tracking errors. Effects of acquisition orientation, region-of-interest (ROI) selection, patient breathing pattern change, and noise on the estimation accuracy were also evaluated.Image subtraction of ground-truth with estimated on-board VC-MRI shows fewer differences than image subtraction of ground-truth with prior image. Agreement between normalized profiles in the estimated and ground-truth VC-MRI was achieved with less than 6% error for both XCAT and patient data. Among all XCAT scenarios, the VPD between ground-truth and estimated lesion volumes was, on average, 8.43 ± 1.52% and the COMS was, on average, 0.93 ± 0.58 mm across all time steps for estimation based on the ROI region in the sagittal cine images. Matching to ROI in the sagittal view achieved better accuracy when there was substantial breathing pattern change. The technique was robust against noise levels up to SNR = 20. For patient data, average tracking errors were less than 2 mm in all directions for all patients.Preliminary studies demonstrated the feasibility of generating real-time VC-MRI for on-board localization of moving targets in radiation therapy.