J Liang

Expected treatment dose construction and adaptive inverse planning optimization: implementation for offline head and neck cancer adaptive radiotherapy.

PURPOSE To construct expected treatment dose for adaptive inverse planning optimization, and evaluate it on head and neck (h&n) cancer adaptive treatment modification. METHODS Adaptive inverse planning engine was developed and integrated in our in-house adaptive treatment control system. The adaptive inverse planning engine includes an expected treatment dose constructed using the daily cone beam (CB) CT images in its objective and constrains. Feasibility of the adaptive inverse planning optimization was evaluated retrospectively using daily CBCT images obtained from the image guided IMRT treatment of 19 h&n cancer patients. Adaptive treatment modification strategies with respect to the time and the number of adaptive inverse planning optimization during the treatment course were evaluated using the cumulative treatment dose in organs of interest constructed using all daily CBCT images. RESULTS Expected treatment dose was constructed to include both the delivered dose, to date, and the estimated dose for the remaining treatment during the adaptive treatment course. It was used in treatment evaluation, as well as in constructing the objective and constraints for adaptive inverse planning optimization. The optimization engine is feasible to perform planning optimization based on preassigned treatment modification schedule. Compared to the conventional IMRT, the adaptive treatment for h&n cancer illustrated clear dose-volume improvement for all critical normal organs. The dose-volume reductions of right and left parotid glands, spine cord, brain stem and mandible were (17 ± 6)%, (14 ± 6)%, (11 ± 6)%, (12 ± 8)%, and (5 ± 3)% respectively with the single adaptive modification performed after the second treatment week; (24 ± 6)%, (22 ± 8)%, (21 ± 5)%, (19 ± 8)%, and (10 ± 6)% with three weekly modifications; and (28 ± 5)%, (25 ± 9)%, (26 ± 5)%, (24 ± 8)%, and (15 ± 9)% with five weekly modifications. CONCLUSIONS Adaptive treatment modification can be implemented including the expected treatment dose in the adaptive inverse planning optimization. The retrospective evaluation results demonstrate that utilizing the weekly adaptive inverse planning optimization, the dose distribution of h&n cancer treatment can be largely improved.

The effect of density variation on photon dose calculation and its impact on intensity modulated radiotherapy and stereotactic body radiotherapy

PURPOSE Inaccurate density information may introduce dose calculation errors when inhomogeneity correction is applied. The aim of the present study was to examine the effect of density variation on photon dose calculation accuracy using the convolution/superposition (CS) algorithm with the focus on newer treatment technologies including intensity modulated radiotherapy, volumetric modulated arc radiotherapy, and stereotactic body radiotherapy (SBRT). METHODS Calculations were first performed using simple inhomogeneity phantoms in order to determine clinically relevant tolerance levels for different tissue types. The clinical validity of these tolerance levels was then demonstrated by evaluating their dosimetric impact on clinical treatment plans. The dose difference was examined by comparing the dose-volume histogram statistics and the spatial distribution of dose errors calculated on a voxel-by-voxel basis. In order to gain some insight into this issue for the Monte Carlo (MC) algorithm, the authors also performed additional validation using a MC dose calculation system. RESULTS For soft tissue and bone, the tolerance levels determined from this study appear to be consistent with the values previously calculated using simpler inhomogeneity correction methods. However, the tolerance level for low density lung tissue has been found to be much smaller than what previous studies had reported. The results from this study also suggest that if density variation is restricted within ±0.02, ±0.03, and ±0.10 g/cm3 for lung, soft tissue, and bone, respectively, the resulting dose error in target volumes can be limited to <2% for most clinical cases and <3% for more challenging lung SBRT cases. When the same amount of density variation is introduced, MC algorithm yields ∼0.3%-0.9% and ∼0.0%-1.2% smaller dose errors for the target and organs-at-risk as compared to CS. CONCLUSIONS It is important to include lung substitute material into the periodic quality assurance of CT simulators and treatment planning systems. This study suggests that the tolerance value of CT number for lung material is ∼ ± 20 HU in order to keep the associated dose uncertainty at 2%-3% or less. Further studies with larger number of lung cases are warranted to validate this new tolerance value before it can be applied to clinical practice.

A feasibility study of intrafractional tumor motion estimation based on 4D‐CBCT using diaphragm as surrogate

Abstract Purpose To investigate the intrafractional stability of the motion relationship between the diaphragm and tumor, as well as the feasibility of using diaphragm motion to estimate lung tumor motion. Methods Eighty‐five paired (pre and posttreatment) daily 4D‐CBCT images were obtained from 20 lung cancer patients who underwent SBRT. Bony registration was performed between the pre‐ and post‐CBCT images to exclude patient body movement. The end‐exhalation phase image of the pre‐CBCT image was selected as the reference image. Tumor positions were obtained for each phase image using contour‐based translational alignments. Diaphragm positions were obtained by translational alignment of its apex position. A linear intrafraction model was constructed using regression analysis performed between the diaphragm and tumor positions manifested on the pretreatment 4D‐CBCT images. By applying this model to posttreatment 4D‐CBCT images, the tumor positions were estimated from posttreatment 4D‐CBCT diaphragm positions and compared with measured values. A receiver operating characteristic (ROC) test was performed to determine a suitable indicator for predicting the estimate accuracy of the linear model. Results Using the linear model, per‐phase position, mean position, and excursion estimation errors were 1.12 ± 0.99 mm, 0.97 ± 0.88 mm, and 0.79 ± 0.67 mm, respectively. Intrafractional per‐phase tumor position estimation error, mean position error, and excursion error were within 3 mm 95%, 96%, and 99% of the time, respectively. The residual sum of squares (RSS) determined from pretreatment images achieved the largest prediction power for the tumor position estimation error (discrepancy < 3 mm) with an Area Under ROC Curve (AUC) of 0.92 (P < 0.05). Conclusion Utilizing the relationship between diaphragm and tumor positions on the pretreatment 4D‐CBCT image, intrafractional tumor positions were estimated from intrafractional diaphragm positions. The estimation accuracy can be predicted using the RSS obtained from the pretreatment 4D‐CBCT image.

Early detection of potential errors during patient treatment planning

Abstract Purpose Data errors caught late in treatment planning require time to correct, resulting in delays up to 1 week. In this work, we identify causes of data errors in treatment planning and develop a software tool that detects them early in the planning workflow. Methods Two categories of errors were studied: data transfer errors and TPS errors. Using root cause analysis, the causes of these errors were determined. This information was incorporated into a software tool which uses ODBC‐SQL service to access TPSs Postgres and Mosaiq MSSQL databases for our clinic. The tool then uses a read‐only FTP service to scan the TPS unix file system for errors. Detected errors are reviewed by a physicist. Once confirmed, clinicians are notified to correct the error and educated to prevent errors in the future. Time‐cost analysis was performed to estimate the time savings of implementing this software clinically. Results The main errors identified were incorrect patient entry, missing image slice, and incorrect DICOM tag for data transfer errors and incorrect CT‐density table application, incorrect image as reference CT, and secondary image imported to incorrect patient for TPS errors. The software has been running automatically since 2015. In 2016, 84 errors were detected with the most frequent errors being incorrect patient entry (35), incorrect CT‐density table (17), and missing image slice (16). After clinical interventions to our planning workflow, the number of errors in 2017 decreased to 44. Time savings in 2016 with the software is estimated to be 795 h. This is attributed to catching errors early and eliminating the need to replan cases. Conclusions New QA software detects errors during planning, improving the accuracy and efficiency of the planning process. This important QA tool focused our efforts on the data communication processes in our planning workflow that need the most improvement.

SU-F-J-192: A Quick and Effective Method to Validate Patient's Daily Setup and Geometry Changes Prior to Proton Treatment Delivery Based On Water Equivalent Thickness Projection Imaging (WETPI) for Head Neck Cancer (HNC) Patient

PURPOSE With the implementation of Cone-beam Computed-Tomography (CBCT) in proton treatment, we introduces a quick and effective tool to verify the patients daily setup and geometry changes based on the Water-Equivalent-Thickness Projection-Image(WETPI) from individual beam angle. METHODS A bilateral head neck cancer(HNC) patient previously treated via VMAT was used in this study. The patient received 35 daily CBCT during the whole treatment and there is no significant weight change. The CT numbers of daily CBCTs were corrected by mapping the CT numbers from simulation CT via Deformable Image Registration(DIR). IMPT plan was generated using 4-field IMPT robust optimization (3.5% range and 3mm setup uncertainties) with beam angle 60, 135, 300, 225 degree. WETPI within CTV through all beam directions were calculated. 3%/3mm gamma index(GI) were used to provide a quantitative comparison between initial sim-CT and mapped daily CBCT. To simulate an extreme case where human error is involved, a couch bar was manually inserted in front of beam angle 225 degree of one CBCT. WETPI was compared in this scenario. RESULTS The average of GI passing rate of this patient from different beam angles throughout the treatment course is 91.5 ± 8.6. In the cases with low passing rate, it was found that the difference between shoulder and neck angle as well as the head rest often causes major deviation. This indicates that the most challenge in treating HNC is the setup around neck area. In the extreme case where a couch bar is accidently inserted in the beam line, GI passing rate drops to 52 from 95. CONCLUSION WETPI and quantitative gamma analysis give clinicians, therapists and physicists a quick feedback of the patients setup accuracy or geometry changes. The tool could effectively avoid some human errors. Furthermore, this tool could be used potentially as an initial signal to trigger plan adaptation.

SU-F-T-191: 4D Dose Reconstruction of Intensity Modulated Proton Therapy (IMPT) Based On Breathing Probability Density Function (PDF) From 4D Cone Beam Projection Images: A Study for Lung Treatment

PURPOSE With energy repainting in lung IMPT, the dose delivered is approximate to the convolution of dose in each phase with corresponding breathing PDF. This study is to compute breathing PDF weighted 4D dose in lung IMPT treatment and compare to its initial robust plan. METHODS Six lung patients were evaluated in this study. Amsterdam shroud image were generated from pre-treatment 4D cone-beam projections. Diaphragm motion curve was extract from the shroud image and the breathing PDF was generated. Each patient was planned to 60 Gy (12GyX5). In initial plans, ITV density on average CT was overridden with its maximum value for planning, using two IMPT beams with robust optimization (5mm uncertainty in patient position and 3.5% range uncertainty). The plan was applied to all 4D CT phases. The dose in each phase was deformed to a reference phase. 4D dose is reconstructed by summing all these doses based on corresponding weighting from the PDF. Plan parameters, including maximum dose (Dmax), ITV V100, homogeneity index (HI=D2/D98), R50 (50%IDL/ITV), and the lung-GTVs V12.5 and V5 were compared between the reconstructed 4D dose to initial plans. RESULTS The Dmax is significantly less dose in the reconstructed 4D dose, 68.12±3.5Gy, vs. 70.1±4.3Gy in the initial plans (p=0.015). No significant difference is found for the ITV V100, HI, and R50, 92.2%±15.4% vs. 96.3%±2.5% (p=0.565), 1.033±0.016 vs. 1.038±0.017 (p=0.548), 19.2±12.1 vs. 18.1±11.6 (p=0.265), for the 4D dose and initial plans, respectively. The lung-GTV V12.5 and V5 are significantly high in the 4D dose, 13.9%±4.8% vs. 13.0%±4.6% (p=0.021) and 17.6%±5.4% vs. 16.9%±5.2% (p=0.011), respectively. CONCLUSION 4D dose reconstruction based on phase PDF can be used to evaluate the dose received by the patient. A robust optimization based on the phase PDF may even further improve patient care.

SU-F-T-382: Volumetric Modulated Arc Therapy (VMAT) Beam Angle Optimization in Pulsed Partial Brain Irradiation (PPBI) for Newly Diagnosed Glioblastoma

PURPOSE To optimize VMAT beam parameters in PPBI to minimize treatment time. We investigate the coverage and organs at risk (OR) avoidance capability of shorter arcs with shorter treatment times. METHODS We evaluated the treatment plans for eleven previously treated PPBI patients. Each patient received 46Gy (2Gy×23) to the initial target and an additional 14Gy (2Gy×7) as a sequential boost. Each daily 2-Gy fraction was delivered as ten 0.2-Gy pulses separated by 3-minute intervals using VMAT. Each pulse was delivered using the same arc and covered at least 95% of the PTV with at least 95% of the prescription dose. To optimize the VMAT beam angle, an initial 360° full-arc VMAT plan was implemented. Beam control points and their corresponding dose rates were exported. A curve of the product of control point and dose rate was plotted against treatment beam angle. The optimum angle range was determined from this relationship. We chose the minimum continuous angle range that covered 85% of the area under the curve. Planning parameters, including treatment time for each pulse (T-pulse), PTV coverage, maximum dose (Dmax), homogeneity index (HI=D5/D95), R50 (50%IDL/PTV), and Dmax to ORs, were compared. RESULTS Mean PTV volume was 364.1±181.5cc. Mean T-pulse of partial-arc beams was 34.3±10.6s, vs. 63.0±1.7s (p<0.001) for that of full-arc beams. No significant differences were found for PTV V95, Dmax and R50, 99.4%±1.2% vs. 99.7%±0.5% (p=0.066), 108.0%±1.2% vs. 107.5%±1.1% (p=0.107), 2.95±0.38 vs. 2.87±0.35 (p=0.165), for the plans with partial-arc and full-arc beams, respectively. However, plans using full-arc do provide better PTV V100 and HI, 96.0%±3.0% vs. 97.2%±2.0% (p=0.025) and 1.06±0.03 vs. 1.04±0.01 (p=0.009). No significant difference was found on Dmax to ORs. CONCLUSION PPBI with optimized partial-arc plans are clinically comparable to full-arc plans, while treatment time be significantly reduced, average saving of 287s for a 10-pulse treatment.

SU-F-J-60: Impact of DIR Method On Treatment Dose Wrapping

PURPOSE To investigate clinical relevant discrepancy between doses wrapped by pure image and biomechanical model based deformable registration (DIR). METHODS 12 patients, each with a CT pair, were included (5 H&N, 5 Prostate and 2 Lung). A research DIR tool (ADMRIE) was utilized for image based DIR (IMG-DIR). To assure organ matching, contour constrain was applied for prostate patients. Tetrahedron meshes were generated for organs (parotid, bladder, rectum and lung). Deformable vector fields (DVF) from IMG-DIR were interpolated to the surface node of meshes as boundary condition. Biomechanical models using finite element modeling (FEM) were generated by assigning organ specific material properties. The models were then input into a FEM tool (ABAQUS) to calculate internal deformation (FEM-DIR). The output volume node displacements were then interpolated to image grids to get refined DVF. The IMRT treatment doses were wrapped by both DVFs to pre-treatment CTs. DVF vector distance (DVF-VD) was calculated on each organ. Dose parameters were calculated for wrapped doses and normalized to pretreatment plan. Gamma passing rate (GPR) was calculated with IMG-DIR dose as reference. Correlation was evaluated between parotid shrinkage and DVF-VD /dose-discrepancy. RESULTS H&N:parotid volume with DVF-VD (>1.5mm) was 6.5±4.7%. The normalized mean dose difference (NMDD) of IMG-DIR and FEM-DIR was -0.8±1.5%, with range (-4.7% to 1.5%). 2mm/2% GPR was 99.0±1.4%. Moderate correlation was found between parotid shrinkage and DVF-VD (R=0.61)/NMDD (R=0.68). Prostate:bladder had a NMDD of -9.9±9.7%, with FEM-DIR wrapped dose systematically higher. Only minor deviation was observed for rectum NMDD (0.5±1.1%). 3mm/3% GPR of bladder and rectum were 81.9±12.0% and 93.1±4.3%, respectively. One of lung patients had 3.9%NMDD and 3mm/3%GPR of 95.2% inside lung. CONCLUSION Impact of DIR methods on treatment dose wrapping is patient and organ specific. Generally, bigger organ with larger volume variation leads to greater dose wrapping uncertainty.Acknowledgement:Elekta research grant support. This work was supported by research funding from Elekta.

SU-F-T-205: Effectiveness of Robust Treatment Planning to Account for Inter- Fractional Variation in Intensity Modulated Proton Therapy for Head Neck Cancer

PURPOSE To evaluate the potential benefits of robust optimization in intensity modulated proton therapy(IMPT) treatment planning to account for inter-fractional variation for Head Neck Cancer(HNC). METHODS One patient with bilateral HNC previous treated at our institution was used in this study. Ten daily CBCTs were selected. The CT numbers of the CBCTs were corrected by mapping the CT numbers from simulation CT via Deformable Image Registration. The planning target volumes(PTVs) were defined by a 3mm expansion from clinical target volumes(CTVs). The prescription was 70Gy, 54Gy to CTV1, CTV2, and PTV1, PTV2 for robust optimized(RO) and conventionally optimized(CO) plans respectively. Both techniques were generated by RayStation with the same beam angles: two anterior oblique and two posterior oblique angles. The similar dose constraints were used to achieve 99% of CTV1 received 100% prescription dose while kept the hotspots less than 110% of the prescription. In order to evaluate the dosimetric result through the course of treatment, the contours were deformed from simulation CT to daily CBCTs, modified, and approved by a radiation oncologist. The initial plan on the simulation CT was re-replayed on the daily CBCTs followed the bony alignment. The target coverage was evaluated using the daily doses and the cumulative dose. RESULTS Eight of 10 daily deliveries with using RO plan achieved at least 95% prescription dose to CTV1 and CTV2, while still kept maximum hotspot less than 112% of prescription compared with only one of 10 for the CO plan to achieve the same standards. For the cumulative doses, the target coverage for both RO and CO plans was quite similar, which was due to the compensation of cold and hot spots. CONCLUSION Robust optimization can be effectively applied to compensate for target dose deficit caused by inter-fractional target geometric variation in IMPT treatment planning.

TH-AB-201-12: Using Machine Log-Files for Treatment Planning and Delivery QA

PURPOSE To determine the segment reduction and dose resolution necessary for machine log-files to effectively replace current phantom-based patient-specific quality assurance, while minimizing computational cost. METHODS Elektas Log File Convertor R3.2 records linac delivery parameters (dose rate, gantry angle, leaf position) every 40ms. Five VMAT plans [4 H&N, 1 Pulsed Brain] comprised of 2 arcs each were delivered on the ArcCHECK phantom. Log-files were reconstructed in Pinnacle on the phantom geometry using 1/2/3/4° control point spacing and 2/3/4mm dose grid resolution. Reconstruction effectiveness was quantified by comparing 2%/2mm gamma passing rates of the original and log-file plans. Modulation complexity scores (MCS) were calculated for each beam to correlate reconstruction accuracy and beam modulation. Percent error in absolute dose for each plan-pair combination (log-file vs. ArcCHECK, original vs. ArcCHECK, log-file vs. original) was calculated for each arc and every diode greater than 10% of the maximum measured dose (per beam). Comparing standard deviations of the three plan-pair distributions, relative noise of the ArcCHECK and log-file systems was elucidated. RESULTS The original plans exhibit a mean passing rate of 95.1±1.3%. The eight more modulated H&N arcs [MCS=0.088±0.014] and two less modulated brain arcs [MCS=0.291±0.004] yielded log-file pass rates most similar to the original plan when using 1°/2mm [0.05%±1.3% lower] and 2°/3mm [0.35±0.64% higher] log-file reconstructions respectively. Log-file and original plans displayed percent diode dose errors 4.29±6.27% and 3.61±6.57% higher than measurement. Excluding the phantom eliminates diode miscalibration and setup errors; log-file dose errors were 0.72±3.06% higher than the original plans - significantly less noisy. CONCLUSION For log-file reconstructed VMAT arcs, 1° control point spacing and 2mm dose resolution is recommended, however, less modulated arcs may allow less stringent reconstructions. Following the aforementioned reconstruction recommendations, the log-file technique is capable of detecting delivery errors with equivalent accuracy and less noise than ArcCHECK QA. I am funded by an Elekta Research Grant.