N Roman

Interfractional Positional Variability of Fiducial Markers and Primary Tumors in Locally Advanced Non-Small-Cell Lung Cancer During Audiovisual Biofeedback Radiotherapy

PURPOSE To evaluate implanted markers as a surrogate for tumor-based setup during image-guided lung cancer radiotherapy with audiovisual biofeedback. METHODS AND MATERIALS Seven patients with locally advanced non-small-cell lung cancer were implanted bronchoscopically with gold coils. Markers, tumor, and a reference bony structure (vertebra) were contoured for all 10 phases of the four-dimensional respiration-correlated fan-beam computed tomography and weekly four-dimensional cone-beam computed tomography. RESULTS The systematic/random interfractional marker-to-tumor centroid displacements were 2/3, 2/2, and 3/3 mm in the x (lateral), y (anterior-posterior), and z (superior-inferior) directions, respectively. The systematic/random interfractional marker-to-bone displacements were 2/3, 2/3, and 2/3 mm in the x, y, and z directions, respectively. The systematic/random tumor-to-bone displacements were 2/3, 2/4, and 4/4 mm in the x, y, and z directions, respectively. All displacements changed significantly over time (p < 0.0001). CONCLUSIONS Although marker-based image guidance may decrease the risk for geometric miss compared with bony anatomy-based positioning, the observed displacements between markers and tumor centroids indicate the need for repeated soft tissue imaging, particularly in situations with large tumor volume change and large initial marker-to-tumor centroid distance.

Evaluation of 4-dimensional Computed Tomography to 4-dimensional Cone-Beam Computed Tomography Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy

PURPOSE To evaluate 2 deformable image registration (DIR) algorithms for the purpose of contour mapping to support image-guided adaptive radiation therapy with 4-dimensional cone-beam CT (4DCBCT). METHODS AND MATERIALS One planning 4D fan-beam CT (4DFBCT) and 7 weekly 4DCBCT scans were acquired for 10 locally advanced non-small cell lung cancer patients. The gross tumor volume was delineated by a physician in all 4D images. End-of-inspiration phase planning 4DFBCT was registered to the corresponding phase in weekly 4DCBCT images for day-to-day registrations. For phase-to-phase registration, the end-of-inspiration phase from each 4D image was registered to the end-of-expiration phase. Two DIR algorithms-small deformation inverse consistent linear elastic (SICLE) and Insight Toolkit diffeomorphic demons (DEMONS)-were evaluated. Physician-delineated contours were compared with the warped contours by using the Dice similarity coefficient (DSC), average symmetric distance, and false-positive and false-negative indices. The DIR results are compared with rigid registration of tumor. RESULTS For day-to-day registrations, the mean DSC was 0.75 ± 0.09 with SICLE, 0.70 ± 0.12 with DEMONS, 0.66 ± 0.12 with rigid-tumor registration, and 0.60 ± 0.14 with rigid-bone registration. Results were comparable to intraobserver variability calculated from phase-to-phase registrations as well as measured interobserver variation for 1 patient. SICLE and DEMONS, when compared with rigid-bone (4.1 mm) and rigid-tumor (3.6 mm) registration, respectively reduced the average symmetric distance to 2.6 and 3.3 mm. On average, SICLE and DEMONS increased the DSC to 0.80 and 0.79, respectively, compared with rigid-tumor (0.78) registrations for 4DCBCT phase-to-phase registrations. CONCLUSIONS Deformable image registration achieved comparable accuracy to reported interobserver delineation variability and higher accuracy than rigid-tumor registration. Deformable image registration performance varied with the algorithm and the patient.

SU-E-J-151: Dosimetric Evaluation of DIR Mapped Contours for Image Guided Adaptive Radiotherapy with 4D Cone-Beam CT

PURPOSE To estimate dosimetric errors resulting from using contours deformably mapped from planning CT to 4D cone beam CT (CBCT) images for image-guided adaptive radiotherapy of locally advanced non-small cell lung cancer (NSCLC). METHODS Ten locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4D fan-beam CT (4DFBCT) and weekly 4DCBCT scans. Multiple physicians delineated the gross tumor volume (GTV) and normal structures in planning CT images and only GTV in CBCT images. Manual contours were mapped from planning CT to CBCTs using small deformation, inverse consistent linear elastic (SICLE) algorithm for two scans in each patient. Two physicians reviewed and rated the DIR-mapped (auto) and manual GTV contours as clinically acceptable (CA), clinically acceptable after minor modification (CAMM) and unacceptable (CU). Mapped normal structures were visually inspected and corrected if necessary, and used to override tissue density for dose calculation. CTV (6mm expansion of GTV) and PTV (5mm expansion of CTV) were created. VMAT plans were generated using the DIR-mapped contours to deliver 66 Gy in 33 fractions with 95% and 100% coverage (V66) to PTV and CTV, respectively. Plan evaluation for V66 was based on manual PTV and CTV contours. RESULTS Mean PTV V66 was 84% (range 75% - 95%) and mean CTV V66 was 97% (range 93% - 100%) for CAMM scored plans (12 plans); and was 90% (range 80% - 95%) and 99% (range 95% - 100%) for CA scored plans (7 plans). The difference in V66 between CAMM and CA was significant for PTV (p = 0.03) and approached significance for CTV (p = 0.07). CONCLUSION The quality of DIR-mapped contours directly impacted the plan quality for 4DCBCT-based adaptation. Larger safety margins may be needed when planning with auto contours for IGART with 4DCBCT images. Reseach was supported by NIH P01CA116602.

TU‐C‐141‐04: Evaluation of Clinical Acceptability of DIR Mapped Contours for Adaptive Radiotherapy with 4D Cone‐Beam CT

Purpose: To evaluate clinical acceptability of contours generated using deformable image registration of 4D fan beam (4DFBCT) to 4D cone beam (4DCBCT) images for image‐guided adaptive radiotherapy (IGART). Methods: Sixteen locally advanced non‐small cell lung cancer (NSCLC) patients underwent one planning 4DFBCT and weekly 4DCBCT scans. 4DFBCT to 4DCBCT registrations were performed using two intensity‐driven deformable image registration (DIR) algorithms: 1) small deformation, inverse consistent linear elastic (SICLE) algorithm and 2) Insight Toolkit diffeomorphic demons (DEMONS). Multiple physicians delineated the gross tumor volume in all images (MANUAL). Manual contours were mapped from 4DFBCT to 4DCBCT for two scans in each patient, and were compared with manual contours using Dice similarity coefficient (DSC), average symmetric distance (ASD), false positive (FP), false negative (FN) and percentage volume difference (%VD). A physician was asked to rate DIR generated and manual contours as A) clinically acceptable (CA) B) clinically acceptable after minor modifications (CAMM) and C) clinically unacceptable (CU) using planning CT contour as reference. Reviewing physician was unaware that some of the contours were manual. The results were analyzed with respect to similarity measures, volume and regression characteristics of the tumor. Results: No contours were rated as CU by the physician. The number of contours rated as CA and CAMM was respectively 14 (44%) and 18 (56%) for SICLE, 3 (9%) and 29 (91%) for DEMONS, and 26 (81%) and 6 (19%) for MANUAL. No correlation was observed between physician ratings and DSC, ASD, FP, FN, %VD, tumor shrinkage, day of CBCT scan, and volume of the GTV. Conclusion: DIR generated contours were considered clinically acceptable for IGART, but may require some manual adjustment prior to use. Volume and surface similarity measures did not correlate with physician judgment of clinical acceptability. Supported by NIH Grant P01 CA 116602; This work was supported by a grant (P01CA116602) from the National Cancer Institute. Gary E. Christensen holds a papent related to the SICLE registration algorithm; E. Weiss and J. Williamson has grants from Varian medical systems and Philips Radiation Oncology Systems.

MO‐F‐WAB‐06: Validation Framework and Benchmarking for Markerless Tumor Trajectory Estimation

PURPOSE To develop a validation framework for markerless tumor trajectory estimation algorithms and use this framework to optimize markerless tumor trajectory estimation in cone beam CT (CBCT) projections. METHODS Fiducial markers implanted in and near the tumor in six lung cancer patients were segmented in CBCT projections and the 3D marker trajectories were reconstructed. A correction based on the tumor position in the reconstructed CBCT, the tumor-to-marker displacement (T-M) was added to the marker trajectory to generate the reference tumor trajectory. Four combinations of T-M based on two marker segmentation methods and two tumor position measurement methods were evaluated. Since T-M is expected to be fairly stable from day to day, optimal T-M was evaluated based on day-to-day variability. For markerless tracking, three means of generating templates for 2D registration (a cylindrical template, expanded gross tumor volume (Ex-GTV), and Ex-GTV with padding to override non-target edges (padded template)) were compared using the validation framework. A constraint method was also implemented to regularize the 2D registration. RESULTS The optimal T-M was generated by planning CT to CBCT registration of the target volume along with marker segmentation from the projections. The mean (standard deviation) absolute registration error in five patients using the three different template methods were 5.3 (6.5) mm for the Ex-GTV template, 6.1 (6.4) mm for the cylinder template, and 3.5 (5.0) for the padded template. The 90% error level was 12.4, 13.3, and 7.9 mm for the three methods, respectively. The mean registration error was reduced further to 2.1 (2.5) mm (90% error of 4.3 mm) with the constraint method. CONCLUSION A validation framework for markerless trajectory measurement was optimized. The padded template method with constraints agreed best with the implanted marker trajectories. E Weiss receives research support from Varian Medical Systems.

MO‐F‐BRA‐02: Evaluation of 4D CT to 4D Cone‐Beam CT Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy

PURPOSE To evaluate two deformable image registration (DIR) algorithms for the purpose of contour mapping to support image guided adaptive radiotherapy (IGART) with 4D cone beam CT (4DCBCT). METHODS Eleven locally advanced non-small cell lung cancer (NSCLC) patients underwent one planning 4D fan- beam CT (4DFBCT) and seven weekly 4DCBCT scans. Gross tumor volume (GTV) and carina were delineated by a physician in all 4D images. For day to day registration, the end of inspiration 4DFBCT phase was deformably registered to the corresponding phase in each 4DCBCT image. For phase to phase registration, the end of inspiration phase from each 4D image was registered to end of expiration phase. The delineated contours were warped using the resulting transforms and compared to the manual contours through Dice similarity coefficient (DSC), false positive and false negative indices, and, for carina, target registration error (TRE). Two DIR algorithms were tested: 1) small deformation, inverse consistent linear elastic (SICLE) algorithm and 2) Insight Toolkit diffeomorphic demons (DEMONS). RESULTS For day to day registrations, the mean DSC was 0.59 ± 0.16 after rigid registration, 0.72 ± 0.13 with SICLE and to 0.66 ± 0.18 with DEMONS. SICLE and DEMONS reduced TRE to 4.1 ± 2.1 mm and 5.8 ± 3.7 mm respectively, from 6.2 ± 3.5 mm; and reduced false positive index to 0.27 and 0.26 respectively from 0.46. Registration with the cone beam as the fixed image resulted in higher DSC than with the fan beam as fixed (p < 0.001). SICLE and DEMONS increased the DSC on average by 10.0% and 8.0% and reduced TRE by 2.8 mm and 2.9 mm respectively for phase to phase DIR. CONCLUSIONS DIR achieved more congruent mapping of target structures to delineations than rigid registration alone, although DIR performance varied with algorithm and patient. This work was supported by National Cancer Institute Grant No. P01 CA 116602.

WE‐D‐204B‐09: Interfraction Variability of Tumor Motion Trajectory from Serial 4D Cone‐Beam CT Imaging during Audio‐Visual Biofeedback

Purpose: To quantify interfraction variations in position, volume, and intrafraction breathing motion trajectory of lungtumors and critical structures with 4DCT and 4D cone beam CT (4D CBCT)images, for patients undergoing audiovisual biofeedback.Method and Materials: A pretreatment 4D fan beam CT (4DCT) and 35–40 daily 4D CBCTs were acquired daily throughout the treatment for 7 non‐small cell lungcancer patients. The tumor, esophagus, and trachea were contoured for all 10 phases of each CT. Each phase‐specific image was registered manually on bony anatomy to the end‐inhalation phase image from the 4DCT. The centroid and volume of each structure were calculated for each phase, and used to quantify the variability of the tumor and critical structure locations during each fraction. The tumor volume, relative to its end‐inhalation volume on 4DCT, was calculated for end‐inhalation and end‐exhalation phases for each fraction. The mean position of each organ, relative to the 4DCT, was calculated for each 4D CBCT scan. Results: Analysis has been completed for one patient to date consisting of 27 fractions, consisting of 7 4D CBCTs. Over the course of treatment, the tumor volume at end‐inhalation decreased by 31%. The systematic (random) error in mean tumor position was found to be 0.12cm (0.14 cm), 0.29cm (0.12 cm), and 0.31cm (0.62 cm) in the mediolateral, anterior‐posterior, and superior inferior directions respectively. These were large in comparison to the average range of tumor motion, which was 0.09cm, 0.21cm, 0.25cm in the corresponding axes. The corresponding ranges of motion over the treatment course, were 0.06–0.13cm, 0.14–0.26cm, and 0.12–0.42cm. Conclusion: For this patient, the interfractional variation in mean tumor position was the dominant variation with fraction‐to‐fraction changes as large as 2 cm. Audiovisual biofeedback did not adequately control these baseline variations. Supported by Grant P01 CA 116602