Tumor Regression (TR) in Lung Irradiation With Proton Beams

E698

International Journal of Radiation Oncology  Biology  Physics

previous report that the tumor cell density is inversely correlated with ADC value in brain tumor, the relationship for brain tumor is extrapolated to estimate the tumor-cell density in pancreatic cancer. To account for different radiosensitivity for different tumor grade region, the previously reported a and b parameters for cell lines Capan-2 and Panc-1 are adopted for different tumor grade regions, namely aZ0.32 Gy-1 and bZ0.016 Gy-2 (Capan-2) for G1/G2 region and aZ0.24 Gy-1 and bZ0.018 Gy-2 (Panc-1) for G3 region. The total dose, dose per fraction, and biological equivalent dose (BED) for achieving 99.99% TCP after chemoradiation therapy were estimated. To account for the effect of chemotherapy, a previously reported factor, fcZ1.353, was used. Results: The tumor-cell density for G1/G2 and G3 regions in pancreas tumor based on ADC cutoff value are estimated as 1.18x108 cell/cm3 and 1.35x108 cell/cm3. Assuming the tumor volume ranging from 1 cm3 to 10 cm3, the fractional dose, total dose in 28 daily fractions, and BED estimated to achieve 99.99% TCP are 2.1 (-0.1,+0.1) Gy, 59.4 (-2.9,+1.2) Gy, and 65.7 (-3.5,+1.5) Gy for a G1/G2 tumor region, and 2.6 (-0.1,+0.1) Gy, 72.1 (-3.3, +1.4) Gy, and 86.1 (-4.5, +1.9) Gy for a G3 region, respectively. This fractionation scheme may be delivered as simultaneously integrated boost. Conclusion: The spatial dose prescriptions for radiation dose painting with CRT for pancreatic cancer are estimated based on ADC maps using Poisson TCP model with selected parameters. Further studies are needed to test appropriateness of these prescriptions. Author Disclosure: X. Chen: None. P.W. Prior: None. A. Tai: None. A. Li: None.

calculations and improving image quality. The data presented are also useful for beam configuration in a treatment planning system for patient image dose calculations. The 2.5-MV imaging beam provides higher image quality and lower image dose compared to a conventional 6-MV beam. Author Disclosure: G.X. Ding: None. P. Munro: None.

3712 The Characteristics of the Newly Available 2.5-MV Imaging Beam From a Medical Linear Accelerator G.X. Ding1 and P. Munro2; 1Vanderbilt University Medical Center Nashville, TN, 2Varian Medical Systems, Palo Alto, CA, United States Purpose/Objective(s): The purpose of this investigation is to provide beam characteristics of a newly available 2.5-MV imaging beam from a medical linear accelerator and to evaluate its image quality by comparing it with a conventional 6-MV beam. The beam parameters are essential in estimating imaging dose to patient and in the design of image detectors to improve the image quality. Materials/Methods: The energy and angular distributions of an incident beam are the most important characteristics. However, the information is difficult to obtain by experimental methods, and the Monte Carlo simulation is the most efficient and accurate method in obtaining them. In this study, the EGSnrc Monte Carlo code, BEAMnrc, has been used to simulate the geometry details in the head that produce this 2.5-MV imaging beam. The Monte Carlo simulated beams were used to study its characteristics and to calculate the dose distributions. The measured data were used to validate the accuracy of the Monte Carlo simulations. The beam characteristics presented included energy spectrum of the photons, the photon fluence/energy fluence as a function of position away from the beam central axis, mean energy as a function of off-axis positions, depth-dose curves, and dose profiles for different field sizes at different depths. The simulated beams were also used to calculate the image dose to patients. The image quality between 2.5-MV imaging beam and 6-MV beam were performed for thorax and pelvic images for 1 MU and 0.5 MU acquisitions. Results: An excellent agreement was obtained between Monte Carlo calculated and measured depth-dose curves and profiles for both 10 x 10 cm2 and 40 x 40 cm2 field sizes. The results of analysis from simulated beam showed that the average energy of incident photons is only 0.47 MeV with only a small decrease (w3%) away from central axis at near the beam edge for a maximum field size of 40 x 40 cm2 field. Significant image quality improvements were observed by comparing images acquired between using 2.5-MV and 6-MV beams. For the same monitor unit (MU) used in acquitting the images the image dose from 2.5-MV imaging beam is w15% lower compared to 6-MV beam. A typical image dose to patient resulting from a pair of orthogonal setup fields is shown to be w1 cGy. Conclusion: The Monte Carlo simulation provides detailed characteristics of the 2.5-MV imaging beam which can be used for patient imaging dose

3713 Tumor Regression (TR) in Lung Irradiation With Proton Beams C.W. Cheng,1 B.W. Wessels,1 F. Jesseph,2 D. Mattson,1 T. Biswas,1 and D.B. Mansur1; 1University Hospitals Case Medical Center, Cleveland, OH, 2University Hospitals Case Medical Center, Cleveland, OH Purpose/Objective(s): In this study, detailed dosimetric calculations were performed to examine the effects of TR on proton lung irradiation. Materials/Methods: The computed tomographic (CT) scans and radiation therapy (RT) structures of 15 lung patients treated with VMATwere exported to the proton treatment planning system and MIM software. Double scattering proton beams were used for planning. HU distributions of the GTV for the 15 patients were analyzed in MIM. TR was simulated by giving the GTV density of 0.23 (lung density). This complete “melting away of GTV approach” was assumed to occur for only a percentage of the whole treatment course to simulate a gradual change in tumor topography with a certain % regression/day. To illustrate the methodology, we used a dose of 2.5 Gy in 16 fx (total 40 Gy). The following scenarios are considered: onset of TR occurs at fractions 15, 13, and 8. Two treatment plans were generated for each scenario. The first used the CT density for the GTV (no TR); the second is with GTV densityZ0.23 (TR). The composite of these plans is the effective distribution due to TR for the particular scenario. Results were compared with the plan calculated with no TR. Calculations were repeated by assuming GTV density as “1” to investigate if there is any significant change dosimetrically as related to initial GTV density. Results: For each patient, w90% of the HU in the GTV covered a range -300 to 300. Mean HU for the 15 patients was -4.8311.9. Table 1 summarizes the dose-volume results for the different scenarios and between the 2 initial GTV densities. The largest effect of TR was on heart dose while CTV and PTV coverage remained relatively constant in all scenarios and likewise for the lung doses. Dosimetric impact was more significant with the regression of a solid tumor. Comparing the TR effect of a porous GTV and a solid tumor, the V5 (heart) exhibited the largest increase, from 40.6% for a porous GTV to 43.7% for a solid tumor in S2. Conclusion: Lung GTV consists of low HU elements, which helps to reduce the dosimetric impact on OARs in TR. On the other hand, a solid GTV produces significantly larger dosimetric effects when tumor regresses, especially on the heart dose. Magnitude depends on when TR occurs and the fraction size. To the best of our knowledge, this is the first report on TR for lung irradiation with compensator-based proton therapy. Abstract 3713; Table 1. Dose-volume results for the different scenarios. S1: no TR. S2: 14fx no TR, 2fx w/TR. S3: 12fx no TR, 4 fx w/TR. S4: 8fx no TR, 8fx w/TR.

CTV

%V100% D0.03cc(Gy)

PTV

%V100% D0.03cc(Gy)

Heart

%V5 %V20 %V30

R Lung

%V20

L Lung

%V20

Initial CT density for GTV S1 S2 S3 S4

GTV density [1 S1 S2 S3 S4

99.5 99.7 99.9 99.9 44.00 44.0 43.80 43.90 96.5 96.5 97.1 97.5 44.00 44.0 43.94 43.96 40.59 40.5 42.82 44.57 26.19 26.1 26.65 28.38 17.73 18.3 18.66 20.03

97.62 97.49 97.47 95.96 44.5 44.0 44.15 44.50

0.3227 0.321 0.3231 0.3230 48.81 49.4 49.06 49.27

95.9 95.78 96.03 95.27 44.08 44.50 44.18 44.29 41.83 43.72 45.3 47.62 25.82 26.76 27.93 30.56 17.89 18.39 19.12 21.03 0.3227 0.3226 0.32 0.3248 49.55 49.56 49. 49.75

Author Disclosure: C. Cheng: Employee; University Hospitals. B.W. Wessels: Employee; Case Western Reserve University. Chief of Medical

Volume 96  Number 2S  Supplement 2016 Physics; University Hospitals. F. Jesseph: Employee; University Hospitals. D. Mattson: Employee; University Hospitals. T. Biswas: Employee; University Hospitals. D.B. Mansur: Employee; University Hospitals.

3714 Prediction of Pathologic Complete Response to Neoadjuvant Chemoradiation in the Treatment of Esophageal Cancer Using Machine Learning M.W. Macomber,1 A. Samareh,2 W.A. Chaovalitwongse,2 S.R. Bowen,3 S.A. Patel,4 J. Zeng,4 and M. Nyflot5; 1Department of Radiation Oncology, University of Washington, Seattle, WA, 2University of Washington Department of Industrial and Systems Engineering, Seattle, WA, 3 University of Washington Radiation Oncology and Radiology, Seattle, WA, 4University of Washington, Seattle, WA, 5University of Washington Radiation Oncology, Seattle, WA Purpose/Objective(s): PET/CT has a key role in the management of esophageal cancer; however, improved prediction models would be useful for clinical decision making. In particular, ability to predict pathologic complete response (pCR) to chemoradiation would help spare patients from an esophagectomy, a high-risk procedure with significant morbidity. Machine learning is a promising approach to develop models that can predict unseen data, but the efficacy of these models in predicting the onset of esophageal cancer has not been established. We assessed the classification accuracy of prediction models for pathologic complete response based on commonly used machine learning techniques of quantitative pretreatment PET/CT features. Materials/Methods: We identified 39 patients with esophageal cancer who completed neoadjuvant chemoradiation followed by surgery (trimodality therapy) and had pretreatment PET/CT at our institution from 2007-2015. PET/CT images were rigidly fused with radiation planning CT scans based on spine anatomy at the level of gross disease. The following quantitative PET/CT features were evaluated in the clinical target volume (CTV): mean uptake, max uptake, total lesion glycolysis (TLG), and volume. Based on these 4 features, 5 commonly used machine learning techniques (i.e., k nearest neighbors, decision tree, support vector machines, Naive Bayes, decision analysis) were employed to predict the pathologic response (binary classification). Using a leave-one-out cross-validation, the resulting prediction accuracies of these techniques were compared. Results: There were 29 male (74%) and 10 female patients included in this study (median age 61 years, 85% adenocarcinoma, 15% squamous cell carcinoma, all patients stage IIB-IIIC). Median follow-up was 17 months. Pathologic complete response was achieved in 10 patients (26%). Most disease recurrences were distant (13 patients), with 1 local recurrence. Median overall survival was 17 months. Across the 5 machine learning techniques, the average classification accuracy of predicting the pathological response was 0.60 (range, 0.47-0.74) with k nearest neighbors having greatest accuracy at 0.74. Conclusion: Prediction models with fair-to-good prediction accuracy can be developed using simple quantitative pretreatment PET/CT features for patients undergoing trimodality therapy for esophageal cancer. K nearest neighbors may be an especially appropriate method due to robustness to nonlinear data. These data suggest promise in applying machine learning algorithms on quantitative PET data to aid clinical decision making in larger patient populations. Further prospective studies are needed to validate this dataset. Author Disclosure: M.W. Macomber: None. A. Samareh: Graduate Research Assistant; University of Washington Medical Center. W.A. Chaovalitwongse: None. S.R. Bowen: Employee; Providence Health and Services. Board of Directors; Boyer Children’s Clinic. S.A. Patel: None. J. Zeng: None. M. Nyflot: None.

3715 Automatic Skull Stripping in Computed Tomographic Images Based on K-Means Statistical Classifier for Radiation Therapy Planning J. Villafruela,1,2 B.K. Menon,2,3 and N.D. Forkert1,2; 1University of Calgary, Department of Radiology, Calgary, AB, Canada, 2University of

Poster Viewing E699 Calgary, Hotchkiss Brain Institute, Calgary, AB, Canada, 3University of Calgary, Department of Clinical Neurosciences, Calgary, AB, Canada Purpose/Objective(s): Exact brain segmentation is fundamental for radiation treatment planning and many advanced image analyses. Manual delineation is time consuming and prone to errors. Many skull stripping methods have been developed for T1-weighted MRI brain images but cannot be applied directly to noncontrast CT (NCCT) datasets. The aim of this work was to develop and evaluate a fast and automatic skull stripping method for NCCT datasets. Materials/Methods: In the first step, noise artefacts were reduced using an edge-preserving curvature-driven flow algorithm. After this, voxels with a Hounsfield unit below 15 and above 60 were masked out to exclude air, bone, and metal structures. To account for intensity variations between different scans, the image intensities were normalized to zero mean and unit variance. After this, 4 (kZ4) tissue classes corresponding to extracranial tissues, cerebrospinal fluid, white matter, and gray matter were identified in the datasets using a k-means algorithm. The k-means clustering led to multiple misclassified voxels. This problem was solved by first dilating the identified extracranial structures by one voxel to correct for partial volume effects at the border of the brain. After this, the corrected white and gray matter labels were merged and a morphological opening by 2 voxels was applied to remove vessels and nerves connected to the brain. Finally, a largest connected component analysis was applied to extract the final brain segmentation. Results: The proposed method was evaluated using 6 NCCT datasets, which were manually segmented by 1 observer. The automatic segmentations were quantitatively compared to the manual segmentations using the Dice metric. Overall, the proposed method achieved a mean Dice value of 0.97 (ranging from 0.92 to 0.99). Differences between the segmentations were mostly found in regions with unclear borders like cortical sulci and fissures and small regions like the pituitary gland and the olfactory tract. Conclusion: The proposed skull stripping method leads to robust brain segmentations with high agreement to manual segmentations. The presented method could help accelerate radiation treatment planning and enabling advanced image analysis of NCCT datasets. Author Disclosure: J. Villafruela: None. B.K. Menon: None. N.D. Forkert: None.

3716 Challenges Associated With Pencil Beam Scanning Proton Therapy for Spinal Tumors Following Surgical Stabilization: A Robustness Evaluation of Carbon Fiber Reinforced Polyetheretherketone (Carbon-PEEK) Versus Titanium J.W. Snider III,1,2 L. Mikroutsikos,3 F. Albertini,1 A. Bolsi,1 M. Walser,1 D.C. Weber,4 and R.A. Schneider1; 1Paul Scherrer Institute, Villigen, Switzerland, 2University of Maryland Medical Center, Baltimore, MD, 3 Center for Proton Therapy, Paul Scherrer Institute, Villigen, Switzerland, 4 Department of Radiation Oncology, Inselspital Bern University Hospital and University of Bern, Bern, Switzerland Purpose/Objective(s): The presence of metal implant surgical stabilization (SS) within the target or beam path during delivery of high-dose particle therapy has correlated with substantially worse outcomes in clinical series. Titanium SS causes extensive imaging artifacts on CT/MRI that hinder radiation therapy planning and clinical follow-up. Alternative SS such as carbon fiberereinforced polyetheretherketone (Carbon-PEEK) have demonstrated considerably reduced image corruption. Carbon-PEEK has the added benefit for proton therapy of a stopping power much more akin to normal tissue. We hypothesized that pencil beam scanning proton therapy (PBSPT) plans with delivery in proximity to and through SS would prove more robust to daily setup uncertainties with Carbon-PEEK fixation as compared to titanium. Materials/Methods: A representative patient with entirely Carbon-PEEK fixation was planned to 74 Gy(RBE) for a chordoma of the C3 vertebral body. SS included a cervical cage and an anterior cervical plate. The nominal plan (NP) included no HU-correction for the SS. Two additional plans were generated with the SS manually overridden to appropriate HU