W Sleeman

Dose escalation for locally advanced lung cancer using adaptive radiation therapy with simultaneous integrated volume-adapted boost.

PURPOSE To test the feasibility of a planned phase 1 study of image-guided adaptive radiation therapy in locally advanced lung cancer. METHODS AND MATERIALS Weekly 4-dimensional fan beam computed tomographs (4D FBCT) of 10 lung cancer patients undergoing concurrent chemoradiation therapy were used to simulate adaptive radiation therapy: After an initial intensity modulated radiation therapy plan (0-30 Gy/2 Gy), adaptive replanning was performed on week 2 (30-50 Gy/2 Gy) and week 4 scans (50-66 Gy/2 Gy) to adjust for volume and shape changes of primary tumors and lymph nodes. Week 2 and 4 clinical target volumes (CTV) were deformably warped from the initial planning scan to adjust for anatomical changes. On the week 4 scan, a simultaneous integrated volume-adapted boost was created to the shrunken primary tumor with dose increases in 5 0.4-Gy steps from 66 Gy to 82 Gy in 2 scenarios: plan A, lung isotoxicity; plan B, normal tissue tolerance. Cumulative dose was assessed by deformably mapping and accumulating biologically equivalent dose normalized to 2 Gy-fractions (EQD2). RESULTS The 82-Gy level was achieved in 1 in 10 patients in scenario A, resulting in a 13.4-Gy EQD2 increase and a 22.1% increase in tumor control probability (TCP) compared to the 66-Gy plan. In scenario B, 2 patients reached the 82-Gy level with a 13.9 Gy EQD2 and 23.4% TCP increase. CONCLUSIONS The tested image-guided adaptive radiation therapy strategy enabled relevant increases in EQD2 and TCP. Normal tissue was often dose limiting, indicating a need to modify the present study design before clinical implementation.

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.

Optimizing principal component models for representing interfraction variation in lung cancer radiotherapy.

PURPOSE To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis (PCA). METHODS In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume (GTV) and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: (1) Individual models for the GTV and each lung from one image per week (week to week, W2W); (2) a W2W composite model of all structures; (3) individual models using all images (weekly plus repeat intrafraction images, allscans); and (4) composite model with all images. Models were reconstructed retrospectively (using all available images acquired) and prospectively (using only data acquired up to a time point during treatment). Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. RESULTS An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7 +/- 0.2 mm, which increased to 1.1 +/- 0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors (p = 0.15, paired t-test). The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2 +/- 0.5 mm and for the composite model after five measurements at 0.8 +/- 0.4 mm. CONCLUSIONS Retrospective PCA models were capable of reconstructing original GTV and lung shapes and positions within several millimeters with three to four dominant modes, on average. Prospective models achieved similar accuracy after four to five measurements.

A method to evaluate dose errors introduced by dose mapping processes for mass conserving deformations

PURPOSE To present a method to evaluate the dose mapping error introduced by the dose mapping process. In addition, apply the method to evaluate the dose mapping error introduced by the 4D dose calculation process implemented in a research version of commercial treatment planning system for a patient case. METHODS The average dose accumulated in a finite volume should be unchanged when the dose delivered to one anatomic instance of that volume is mapped to a different anatomic instance-provided that the tissue deformation between the anatomic instances is mass conserving. The average dose to a finite volume on image S is defined as d(S)=e(s)/m(S), where e(S) is the energy deposited in the mass m(S) contained in the volume. Since mass and energy should be conserved, when d(S) is mapped to an image R(d(S→R)=d(R)), the mean dose mapping error is defined as Δd(m)=|d(R)-d(S)|=|e(R)/m(R)-e(S)/m(S)|, where the e(R) and e(S) are integral doses (energy deposited), and m(R) and m(S) are the masses within the region of interest (ROI) on image R and the corresponding ROI on image S, where R and S are the two anatomic instances from the same patient. Alternatively, application of simple differential propagation yields the differential dose mapping error, Δd(d)=|∂d∂e*Δe+∂d∂m*Δm|=|(e(S)-e(R))m(R)-(m(S)-m(R))m(R) (2)*e(R)|=α|d(R)-d(S)| with α=m(S)/m(R). A 4D treatment plan on a ten-phase 4D-CT lung patient is used to demonstrate the dose mapping error evaluations for a patient case, in which the accumulated dose, D(R)=∑(S=0) (9)d(S→R), and associated error values (ΔD(m) and ΔD(d)) are calculated for a uniformly spaced set of ROIs. RESULTS For the single sample patient dose distribution, the average accumulated differential dose mapping error is 4.3%, the average absolute differential dose mapping error is 10.8%, and the average accumulated mean dose mapping error is 5.0%. Accumulated differential dose mapping errors within the gross tumor volume (GTV) and planning target volume (PTV) are lower, 0.73% and 2.33%, respectively. CONCLUSIONS A method has been presented to evaluate the dose mapping error introduced by the dose mapping process. This method has been applied to evaluate the 4D dose calculation process implemented in a commercial treatment planning system. The method could potentially be developed as a fully-automatic QA method in image guided adaptive radiation therapy (IGART).

Development of a population-based model of surface segmentation uncertainties for uncertainty-weighted deformable image registrations.

PURPOSE To develop a population-based model of surface segmentation uncertainties for uncertainty-weighted surface-based deformable registrations. METHODS The contours of the prostate, the bladder, and the rectum were manually delineated by five observers on fan beam CT images of four prostate cancer patients. First, patient-specific representations of structure segmentation uncertainties were derived by determining the interobserver variability (i.e., standard deviation) of the structure boundary delineation. This was achieved by (1) generating an average structure surface mesh from the structure contours drawn by different observers, and (2) calculating three-dimensional standard deviation surface meshes (SDSMs) based on the perpendicular distances from the individual boundary surface meshes to the average surface mesh computed above. Then an average structure surface mesh was constructed to be the reference mesh for the population-based model. The average structure meshes of the other patients were deformably registered to the reference mesh. The calculated deformable vector fields were used to map the patient-specific SDSMs to the reference mesh to obtain the registered SDSMs. Finally, the population-based SDSM was derived by taking the average of the registered SDSMs in quadrature. RESULTS Population-based structure surface statistical models of the prostate, the bladder, and the rectum were created by mapping the patient-specific SDSMs to the population surface model. Graphical visualization indicates that the boundary uncertainties are dependent on anatomical location. CONCLUSIONS The authors have developed and demonstrated a general method for objectively constructing surface maps of uncertainties derived from topologically complex structure boundary segmentations from multiple observers. The computed boundary uncertainties have significant spatial variations. They can be used as weighting factors for surface-based probabilistic deformable registration.

A longitudinal four‐dimensional computed tomography and cone beam computed tomography dataset for image‐guided radiation therapy research in lung cancer

Purpose: To describe in detail a dataset consisting of serial four‐dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. Acquisition and validation methods: As part of an NCI‐sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4–70.2 Gy using daily 1.8 or 2 Gy fractions. Audio‐visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16‐slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in‐house software and hardware tools. Approximately 2500 projections were acquired over a period of 8–10 minutes in half‐fan mode with the half bow‐tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in‐house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. Data format and usage notes: 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM‐RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D‐Lung and are publicly available. Discussion: Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image‐guided and image‐guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.

SU-C-WAB-03: Assessing the Correlation Between Quantitative Measures of Contour Variability and Physician's Qualitative Measure for Clinical Usefulness of Auto-Segmentation in Prostate Cancer Radiotherapy

PURPOSE To assess the correlation between quantitative measures of contour variability and physicians qualitative measure for clinical usefulness of auto-segmentation in prostate cancer radiotherapy Methods: Our study was based on three serial CT images (one planning and two under-treatment image sets) for each of five prostate cancer patients. On each CT image, bladder, prostate and rectum were manually contoured by three experienced physicians. Deformable image registration (ITK Demons) was used to register each of the under-treatment CT images to the planning CT image. The resultant displacement vector fields were used to automatically segment planning CT organs by deformably mapping manual contours on the treatment CTs to the planning CT. For qualitative assessment of automatic and manual contours, trial was conducted with four radiation oncology residents. Each resident was shown sets of randomly chosen manual or automatic bladder, prostate and rectum contours overlaid on the planning CT image in Pinnacle (Philips TPS) using a total of hundred-thirty-five contours. Residents were asked to accept/reject contour based on its clinical usability. Quantitatively, surface distances and DICE coefficient were computed between inter-observer manual contours (manual/manual) and between each automatic and its corresponding manual contour (auto/manual). RESULTS No statistically significant differences were found in mean surface distances between manual/manual and auto/manual contours for bladder and rectum while manual/manual contour distances were significantly smaller for prostate. The distribution of DICE values between manual/manual and auto/manual contours were also similar. Qualitatively, acceptance rates for manual contours were significantly higher than that for automatic contours. CONCLUSION No correspondence was found between qualitative and quantitative measure for manual and automatic contours for rectum and bladder while the two measures appear to be related for prostate. This study suggests that using quantitative measures for evaluating auto-segmentation without a qualitative calibration might not always be predictive of its clinical usefulness.(Supported by NIH P01CA166602) This work was supported by NIH Grant P01 CA 166602 E. Weiss and J. Williamson have grants from Varian medical systems and Philips Radiation Oncology Systems.

A protocol to extend the longitudinal coverage of on-board cone-beam CT

The longitudinal coverage of a LINAC‐mounted CBCT scan is limited to the corresponding dimensional limits of its flat panel detector, which is often shorter than the length of the treatment field. These limits become apparent when fields are designed to encompass wide regions, as when providing nodal coverage. Therefore, we developed a novel protocol to acquire double orbit CBCT images using a commercial system, and combine the images to extend the longitudinal coverage for image‐guided adaptive radiotherapy (IGART). The protocol acquires two CBCT scans with a couch shift similar to the “step‐and‐shoot” cine CT acquisition, allowing a small longitudinal overlap of the two reconstructed volumes. An in‐house DICOM reading/writing software was developed to combine the two image sets into one. Three different approaches were explored to handle the possible misalignment between the two image subsets: simple stacking, averaging the overlapped volumes, and a 3D‐3D image registration with the three translational degrees of freedom. Using thermoluminescent dosimeters and custom‐designed holders for a CTDI phantom set, dose measurements were carried out to assess the resultant imaging dose of the technique and its geometric distribution. Deformable registration was tested on patient images generated with the double‐orbit protocol, using both the planning FBCT and the artificially deformed CBCT as source images. The protocol was validated on phantoms and has been employed clinically for IRB‐approved IGART studies for head and neck and prostate cancer patients. PACS number: 87.57.nj

Effect of variations in atelectasis on tumor displacement during radiation therapy for locally advanced lung cancer

Purpose Atelectasis (AT), or collapsed lung, is frequently associated with central lung tumors. We investigated the variation of atelectasis volumes during radiation therapy and analyzed the effect of AT volume changes on the reproducibility of the primary tumor (PT) position. Methods and materials Twelve patients with lung cancer who had AT and 10 patients without AT underwent repeated 4-dimensional fan beam computed tomography (CT) scans during radiation therapy per protocols that were approved by the institutional review board. Interfraction volume changes of AT and PT were correlated with PT displacements relative to bony anatomy using both a bounding box (BB) method and change in center of mass (COM). Linear regression modeling was used to determine whether PT and AT volume changes were independently associated with PT displacement. PT displacement was compared between patients with and without AT. Results The mean initial AT volume on the planning CT was 189 cm3 (37-513 cm3), and the mean PT volume was 93 cm3 (12-176 cm3). During radiation therapy, AT and PT volumes decreased on average 136.7 cm3 (20-369 cm3) for AT and 40 cm3 (−7 to 131 cm3) for PT. Eighty-three percent of patients with AT had at least one unidirectional PT shift that was greater than 0.5 cm outside of the initial BB during treatment. In patients with AT, the maximum PT COM shift was ≥0.5 cm in all patients and >1 cm in 58% of patients (0.5-2.4 cm). Changes in PT and AT volumes were independently associated with PT displacement (P < .01), and the correlation was smaller with COM (R2 = 0.58) compared with the BB method (R2 = 0.80). The median root mean squared PT displacement with the BB method was significantly less for patients without AT (0.45 cm) compared with those with AT (0.8cm, P = .002). Conclusions Changes in AT and PT volumes during radiation treatment were significantly associated with PT displacements that often exceeded standard setup margins. Repeated 3-dimensional imaging is recommended in patients with AT to evaluate for PT displacements during treatment.

SU-F-BRF-07: Impact of Different Patient Setup Strategies in Adaptive Radiation Therapy with Simultaneous Integrated Volume-Adapted Boost of NSCLC

PURPOSE To evaluate the potential impact of several setup error correction strategies on a proposed image-guided adaptive radiotherapy strategy for locally advanced lung cancer. METHODS Daily 4D cone-beam CT and weekly 4D fan-beam CT images were acquired from 9 lung cancer patients undergoing concurrent chemoradiation therapy. Initial planning CT was deformably registered to daily CBCT images to generate synthetic treatment courses. An adaptive radiation therapy course was simulated using the weekly CT images with replanning twice and a hypofractionated, simultaneous integrated boost to a total dose of 66 Gy to the original PTV and either a 66 Gy (no boost) or 82 Gy (boost) dose to the boost PTV (ITV + 3mm) in 33 fractions with IMRT or VMAT. Lymph nodes (LN) were not boosted (prescribed to 66 Gy in both plans). Synthetic images were rigidly, bony (BN) or tumor and carina (TC), registered to the corresponding plan CT, dose was computed on these from adaptive replans (PLAN) and deformably accumulated back to the original planning CT. Cumulative D98% of CTV of PT (ITV for 82Gy) and LN, and normal tissue dose changes were analyzed. RESULTS Two patients were removed from the study due to large registration errors. For the remaining 7 patients, D98% for CTV-PT (ITV-PT for 82 Gy) and CTV-LN was within 1 Gy of PLAN for both 66 Gy and 82 Gy plans with both setup techniques. Overall, TC based setup provided better results, especially for LN coverage (p = 0.1 for 66Gy plan and p = 0.2 for 82 Gy plan, comparison of BN and TC), though not significant. Normal tissue dose constraints violated for some patients if constraint was barely achieved in PLAN. CONCLUSION The hypofractionated adaptive strategy appears to be deliverable with soft tissue alignment for the evaluated margins and planning parameters. Research was supported by NIH P01CA116602.