Search NASA⌕ Search

SEARCH · Search NASA

Results for “HISTOGRAM”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

MO-A-BRD-09: A Data-Mining Algorithm for Large Scale Analysis of Dose-Outcome Relationships in a Database of Irradiated Head-And-Neck (HN) Cancer Patients

Purpose: To develop a framework for automatic extraction of clinically meaningful dosimetric-outcome relationships from an in-house, analytic oncology database. Methods: Dose-volume histograms (DVH) and clinical outcome-related structured data elements have been routinely stored to our database for 513 HN cancer patients treated from 2007 to 2014. SQL queries were developed to extract outcomes that had been assessed for at least 100 patients, as well as DVH curves for organs-at-risk (OAR) that were contoured for at least 100 patients. DVH curves for paired OAR (e.g., left and right parotids) were automatically combined and included as additional structures for analysis. For each OAR-outcome combination, DVH dose points, D(V{sub t}), at a series of normalized volume thresholds, V{sub t}=[0.01,0.99], were stratified into two groups based on outcomes after treatment completion. The probability, P[D(V{sub t})], of an outcome was modeled at each V{sub t} by logistic regression. Notable combinations, defined as having P[D(V{sub t})] increase by at least 5% per Gy (p<0.05), were further evaluated for clinical relevance using a custom graphical interface. Results: A total of 57 individual and combined structures and 115 outcomes were queried, resulting in over 6,500 combinations for analysis. Of these, 528 combinations met the 5%/Gy requirement, with further manual inspection revealing a number of reasonable models based on either reported literature or proximity between neighboring OAR. The data mining algorithm confirmed the following well-known toxicity/outcome relationships: dysphagia/larynx, voice changes/larynx, esophagitis/esophagus, xerostomia/combined parotids, and mucositis/oral mucosa. Other notable relationships included dysphagia/pharyngeal constrictors, nausea/brainstem, nausea/spinal cord, weight-loss/mandible, and weight-loss/combined parotids. Conclusion: Our database platform has enabled large-scale analysis of dose-outcome relationships. The current data-mining framework revealed both known and novel dosimetric and clinical relationships, underscoring the potential utility of this analytic approach. Multivariate models may be necessary to further evaluate the complex relationship between neighboring OARs and observed outcomes. This research was supported through collaborations with Elekta, Philips, and Toshiba.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-C-17A-10: Comparison of Dose Deformable Accumulation by Using Parallel and Serial Approaches

Purpose: The uncertainty of dose accumulation over multiple CT datasets with deformable fusion may have significant impact on clinical decisions. In this study, we investigate the difference of two dose summation approaches involving deformable fusion. Methods: Five patients, four external beam and one brachytherapy(BT), were chosen for the study. The BT patient was treated with CT-based HDR. The CT image sets acquired in the imageguidance process (8-11 CTs/patient) were used to determine the dose delivered to the four external beam patients. (prostate, pelvis, lung and head and neck). For the HDR patient (cervix), five CT image sets and the corresponding BT plans were used. In total 44 CT datasets and RT dose/plans were imported into the image fusion software MiM (6.0.4) for analysis.For each of the five clinical cases, the dose from each fraction was accumulated into the primary CT dataset by using both Parallel and Serial approaches. The dose-volume histogram (DVH) for CTV and selected organs-at-risks (OAR) were generated. The D95(CTV), OAR(mean) and OAR(max) for the four external beam cases the D90(CTV), and the max dose to bladder and rectum for the BT case were compared. Results: For the four external beam patients, the difference in D95(CTV) were <1.2% PD between the parallel and the serial approaches. The differences of the OAR(mean) and the OAR(max ) range from 0 to 3.7% and <1% PD respectively. For the HDR patient, the dose difference for D90 is 11% PD while that of the max dose to bladder and rectum were 11.5% and 23.3% respectively. Conclusion: For external beam treatments, the parallel and serial approaches have <5% difference probably because tumor volume and OAR have less changes from fraction to fraction. For the brachytherapy case, >10% dose difference between the two approaches was observed as significant volume changes of tumor and OAR were observed among treatment fractions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-C-17A-13: Uncertainty Evaluation of CT Image Deformable Registration for H and N Cancer Adaptive Radiotherapy

Purpose: To evaluate uncertainties of organ specific Deformable Image Registration (DIR) for H and N cancer Adaptive Radiation Therapy (ART). Methods: A commercial DIR evaluation tool, which includes a digital phantom library of 8 patients, and the corresponding “Ground truth Deformable Vector Field” (GT-DVF), was used in the study. Each patient in the phantom library includes the GT-DVF created from a pair of CT images acquired prior to and at the end of the treatment course. Five DIR tools, including 2 commercial tools (CMT1, CMT2), 2 in-house (IH-FFD1, IH-FFD2), and a classic DEMON algorithms, were applied on the patient images. The resulting DVF was compared to the GT-DVF voxel by voxel. Organ specific DVF uncertainty was calculated for 10 ROIs: Whole Body, Brain, Brain Stem, Cord, Lips, Mandible, Parotid, Esophagus and Submandibular Gland. Registration error-volume histogram was constructed for comparison. Results: The uncertainty is relatively small for brain stem, cord and lips, while large in parotid and submandibular gland. CMT1 achieved best overall accuracy (on whole body, mean vector error of 8 patients: 0.98±0.29 mm). For brain, mandible, parotid right, parotid left and submandibular glad, the classic Demon algorithm got the lowest uncertainty (0.49±0.09, 0.51±0.16, 0.46±0.11, 0.50±0.11 and 0.69±0.47 mm respectively). For brain stem, cord and lips, the DVF from CMT1 has the best accuracy (0.28±0.07, 0.22±0.08 and 0.27±0.12 mm respectively). All algorithms have largest right parotid uncertainty on patient #7, which has image artifact caused by tooth implantation. Conclusion: Uncertainty of deformable CT image registration highly depends on the registration algorithm, and organ specific. Large uncertainty most likely appears at the location of soft-tissue organs far from the bony structures. Among all 5 DIR methods, the classic DEMON and CMT1 seem to be the best to limit the uncertainty within 2mm for all OARs. Partially supported by research grant from Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-G-17A-03: MR-Based Cortical Bone Segmentation for PET Attenuation Correction with a Non-UTE 3D Fast GRE Sequence

Purpose: To determine the feasibility of identifying cortical bone on MR images with a short-TE 3D fast-GRE sequence for attenuation correction of PET data in PET/MR. Methods: A water-fat-bone phantom was constructed with two pieces of beef shank. MR scans were performed on a 3T MR scanner (GE Discovery™ MR750). A 3D GRE sequence was first employed to measure the level of residual signal in cortical bone (TE{sub 1}/TE{sub 2}/TE{sub 3}=2.2/4.4/6.6ms, TR=20ms, flip angle=25°). For cortical bone segmentation, a 3D fast-GRE sequence (TE/TR=0.7/1.9ms, acquisition voxel size=2.5×2.5×3mm{sup 3}) was implemented along with a 3D Dixon sequence (TE{sub 1}/TE{sub 2}/TR=1.2/2.3/4.0ms, acquisition voxel size=1.25×1.25×3mm{sup 3}) for water/fat imaging. Flip angle (10°), acquisition bandwidth (250kHz), FOV (480×480×144mm{sup 3}) and reconstructed voxel size (0.94×0.94×1.5mm{sup 3}) were kept the same for both sequences. Soft tissue and fat tissue were first segmented on the reconstructed water/fat image. A tissue mask was created by combining the segmented water/fat masks, which was then applied on the fast-GRE image (MRFGRE). A second mask was created to remove the Gibbs artifacts present in regions in close vicinity to the phantom. MRFGRE data was smoothed with a 3D anisotropic diffusion filter for noise reduction, after which cortical bone and air was separated using a threshold determined from the histogram. Results: There is signal in the cortical bone region in the 3D GRE images, indicating the possibility of separating cortical bone and air based on signal intensity from short-TE MR image. The acquisition time for the 3D fast-GRE sequence was 17s, which can be reduced to less than 10s with parallel imaging. The attenuation image created from water-fat-bone segmentation is visually similar compared to reference CT. Conclusion: Cortical bone and air can be separated based on intensity in MR image with a short-TE 3D fast-GRE sequence. Further research is required to optimize the strategy to reduce Gibbs artifacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-C-17A-10: Patient Features Based Dosimetric Pareto Front Prediction In Esophagus Cancer Radiotherapy

Purpose: The purpose of this study is to study the feasibility of the dosimetric pareto front (PF) prediction based on patient anatomic and dosimetric parameters for esophagus cancer patients. Methods: Sixty esophagus patients in our institution were enrolled in this study. A total 2920 IMRT plans were created to generated PF for each patient. On average, each patient had 48 plans. The anatomic and dosimetric features were extracted from those plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose and PTV homogeneous index (PTVHI) were recorded for each plan. The principal component analysis (PCA) was used to extract overlap volume histogram (OVH) features between PTV and other critical organs. The full dataset was separated into two parts include the training dataset and the validation dataset. The prediction outcomes were the MHD and MLD for the current study. The spearman rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The PF was fit by the the stepwise multiple regression method. The cross-validation method was used to evaluation the model. Results: The mean prediction error of the MHD was 465 cGy with 100 repetitions. The most correlated factors were the first principal components of the OVH between heart and PTV, and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 195 cGy. The most correlated factors were the first principal components of the OVH between lung and PTV, and the overlap between lung and PTV in Z-axis. Conclusion: It is feasible to use patients anatomic and dosimetric features to generate a predicted PF. Additional samples and further studies were required to get a better prediction model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-F-BRF-07: Accuracy of Routine Treatment Planning 4D and DIBH CT Delineation of the Left Anterior Descending Artery in Radiotherapy

Purpose: To assess the feasibility of routine treatment planning 4DCT and deep inspiration breath-hold (DIBH) to accurately contour the left anterior descending artery (LAD), a primary indicator of cardiac toxicity, for radiotherapy treatment planning of breast cancer. Methods: Ten subjects were imaged with a cardiac-gated MRI protocol to determine the displacement of a ROI that included the LAD. The subjects performed a series of breath-hold maneuvers to obtain short-axis and radial views, which were resampled to create a 3D-volume. Tissue motion was determined using a multi-resolution 3D optical flow deformable image registration algorithm. The ROI motion was then used as a spatial boundary to characterize the blurring motion of the LAD in ten patients during clinical 4DCT and DIBH protocols. A radiologist contoured the LAD. Coronary motion-induced blurring artifacts were quantified by applying an unsharp filter to accentuate the LAD despite motion-blurring. The 4DCT maximum inhalation and exhalation respiratory phases were co-registered to determine the LAD displacement during tidal respiration, as visualized in 4DCT. Results: The average 90{sup th} percentile heart motion for the ROI was 0.7±0.1mm(LR), 1.3±0.6mm(SI), 0.6±0.2mm(AP) in the cardiac-gated MRI cohort. The average relative increase in the number of voxels comprising the LAD contour was 69.4±4.5% for the DIBH. During tidal respiration, the average relative increase in the LAD contour was 69.3±5.9% and 67.9±4.6% for inhalation and exhalation respiratory phases respectively. The average 90{sup th} percentile LAD motion was 4.8±1.1mm(LR), 0.9±0.4mm(SI), 1.9±0.6mm(AP) for the 4DCT cohort, in the absence of cardiac-gating. Conclusion: Uncompensated coronary motion was the dominant form of motion blurring present in the CT images due to the high frequency of the cardiac cycle relative to the respiratory cycle. The 4D and DIBH CT contour delineation of the LAD was consistently overestimated without cardiac-gating, which could have led to inaccurate dose volume histogram indicators in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-F-18C-09: Mammogram Surveillance Using Texture Analysis for Breast Cancer Patients After Radiation Therapy

Purpose: To study the feasibility of applying cancer risk model established from treated patients to predict the risk of recurrence on follow-up mammography after radiation therapy for both ipsilateral and contralateral breast. Methods: An extensive set of textural feature functions was applied to a set of 196 Mammograms from 50 patients. 56 Mammograms from 28 patients were used as training set, 44 mammograms from 22 patients were used as test set and the rest were used for prediction. Feature functions include Histogram, Gradient, Co-Occurrence Matrix, Run-Length Matrix and Wavelet Energy. An optimum subset of the feature functions was selected by Fisher Coefficient (FO) or Mutual Information (MI) (up to top 10 features) or a method combined FO, MI and Principal Component (FMP) (up to top 30 features). One-Nearest Neighbor (1-NN), Linear Discriminant Analysis (LDA) and Nonlinear Discriminant Analysis (NDA) were utilized to build a risk model of breast cancer from the training set of mammograms at the time of diagnosis. The risk model was then used to predict the risk of recurrence from mammogram taken one year and three years after RT. Results: FPM with NDA has the best classification power in classifying the training set of the mammogram with lesions versus those without lesions. The model of FPM with NDA achieved a true positive (TP) rate of 82% compared to 45.5% of using FO with 1-NN. The best false positive (FP) rates were 0% and 3.6% in contra-lateral breast of 1-year and 3-years after RT, and 10.9% in ipsi-lateral breast of 3-years after RT. Conclusion: Texture analysis offers high dimension to differentiate breast tissue in mammogram. Using NDA to classify mammogram with lesion from mammogram without lesion, it can achieve rather high TP and low FP in the surveillance of mammogram for patient with conservative surgery combined RT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-A-BRE-01: Debate: To Measure or Not to Measure

Recent studies have highlighted some of the limitations of patient-specific pre-treatment IMRT QA measurements with respect to assessing plan deliverability. Pre-treatment QA measurements are frequently performed with detectors in phantoms that do not involve any patient heterogeneities or with an EPID without a phantom. Other techniques have been developed where measurement results are used to recalculate the patient-specific dose volume histograms. Measurements continue to play a fundamental role in understanding the initial and continued performance of treatment planning and delivery systems. Less attention has been focused on the role of computational techniques in a QA program such as calculation with independent dose calculation algorithms or recalculation of the delivery with machine log files or EPID measurements. This session will explore the role of pre-treatment measurements compared to other methods such as computational and transit dosimetry techniques. Efficiency and practicality of the two approaches will also be presented and debated. The speakers will present a history of IMRT quality assurance and debate each other regarding which types of techniques are needed today and for future quality assurance. Examples will be shared of situations where overall quality needed to be assessed with calculation techniques in addition to measurements. Elements where measurements continue to be crucial such as for a thorough end-to-end test involving measurement will be discussed. Operational details that can reduce the gamma tool effectiveness and accuracy for patient-specific pre-treatment IMRT/VMAT QA will be described. Finally, a vision for the future of IMRT and VMAT plan QA will be discussed from a safety perspective. Learning Objectives: Understand the advantages and limitations of measurement and calculation approaches for pre-treatment measurements for IMRT and VMAT planning Learn about the elements of a balanced quality assurance program involving modulated techniques Learn how to use tools and techniques such as an end-to-end test to enhance your IMRT and VMAT QA program.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-BRE-02: BEST IN PHYSICS (THERAPY) - Stereotactic Radiotherapy for Renal Sympathetic Ablation for the Treatment of Refractory Hypertension

Purpose: To determine the safety and efficacy of stereotactic radiotherapy as a novel treatment for patients with refractory hypertension in a swine model. Uncontrolled hypertension is a significant contributor to morbidity and mortality, substantially increasing the risk of ischemic stroke, ischemic heart disease, and kidney failure. Methods: High-resolution computed tomography (CT) images of anesthetized pigs were acquired and treatment plans for each renal artery and nerve were developed using our clinically implemented treatment planning system. Stereotactic radiotherapy, 40Gy in single fraction was delivered bilaterally to the renal nerves using a state-of-the-art medical linear accelerator under image guidance utilizing dynamic conformal arcs. Dose to nearby critical organs was evaluated by dosevolume histogram analysis and correlated to toxicity data obtained through follow up pathology analysis. The animals were observed for six months with serial measurements of blood pressure, urine analysis, serum laboratories, and overall clinical and behavioral status. Results: All animals survived to the follow-up point without evidence of renal dysfunction (stable serum creatinine), skin changes, or behavioral changes that might suggest animal discomfort. Plasma norepinephrine levels (ng/ml) were followed monthly for 6 months. The average reduction observed was 63%, with the median reduction at 73.5%. Microscopic evaluation 4–6 weeks after treatment showed evidence of damage to the nerves around treated renal arteries. Considerable attenuation in pan neurofilament expression by immunohistochemistry was observed with some vacuolar changes indicative of injury. There was no histological or immunohistochemical evidence of damage to nearby spinal cord or spinal nerve root structures. Conclusion: Our preclinical studies have shown stereotactic radiotherapy to the renal sympathetic plexus to be safe and effective in reducing blood pressure, thus this approach holds great promise as a novel treatment modality for patients with refractory hypertension. This study was funded by the Stanford University Cardiovascular Institute. B. Loo and P. Maxim have received funding from RaySearch Laboratories.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-17A-02: Predictive Modeling of Outcome Following SABR for NSCLC Based On Radiomics of FDG-PET Images

Purpose: This study aims to develop predictive models of patient outcome by extracting advanced imaging features (i.e., Radiomics) from FDG-PET images. Methods: We acquired pre-treatment PET scans for 51 stage I NSCLC patients treated with SABR. We calculated 139 quantitative features from each patient PET image, including 5 morphological features, 8 statistical features, 27 texture features, and 100 features from the intensity-volume histogram. Based on the imaging features, we aim to distinguish between 2 risk groups of patients: those with regional failure or distant metastasis versus those without. We investigated 3 pattern classification algorithms: linear discriminant analysis (LDA), naive Bayes (NB), and logistic regression (LR). To avoid the curse of dimensionality, we performed feature selection by first removing redundant features and then applying sequential forward selection using the wrapper approach. To evaluate the predictive performance, we performed 10-fold cross validation with 1000 random splits of the data and calculated the area under the ROC curve (AUC). Results: Feature selection identified 2 texture features (homogeneity and/or wavelet decompositions) for NB and LR, while for LDA SUVmax and one texture feature (correlation) were identified. All 3 classifiers achieved statistically significant improvements over conventional PET imaging metrics such as tumor volume (AUC = 0.668) and SUVmax (AUC = 0.737). Overall, NB achieved the best predictive performance (AUC = 0.806). This also compares favorably with MTV using the best threshold at an SUV of 11.6 (AUC = 0.746). At a sensitivity of 80%, NB achieved 69% specificity, while SUVmax and tumor volume only had 36% and 47% specificity. Conclusion: Through a systematic analysis of advanced PET imaging features, we are able to build models with improved predictive value over conventional imaging metrics. If validated in a large independent cohort, the proposed techniques could potentially aid in identifying patients who might benefit from adjuvant therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast-enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-G-9A-01: Radiation Oncology Outcomes Informatics

The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. “How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?” To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics. Learning Objectives: Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database. Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures. Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi-disciplinary effort to aggregate data for all patients. Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-A-19A-12: A GPU-Accelerated and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization System

Purpose: To develop a clinically applicable intensity modulated proton therapy (IMPT) optimization system that utilizes more accurate Monte Carlo (MC) dose calculation, rather than analytical dose calculation. Methods: A very fast in-house graphics processing unit (GPU) based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified gradient based optimization method was used to achieve the desired dose volume histograms (DVH). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve the spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that Result from maintaining the intrinsic CT resolution and large number of proton spots. The dose effects were studied particularly in cases with heterogeneous materials in comparison with the commercial treatment planning system (TPS). Results: For a relatively large and complex three-field bi-lateral head and neck case (i.e. >100K spots with a target volume of ∼1000 cc and multiple surrounding critical structures), the optimization together with the initial MC dose influence map calculation can be done in a clinically viable time frame (i.e. less than 15 minutes) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The DVHs of the MC TPS plan compare favorably with those of a commercial treatment planning system. Conclusion: A GPU accelerated and MC-based IMPT optimization system was developed. The dose calculation and plan optimization can be performed in less than 15 minutes on a hardware system costing less than 45,000 dollars. The fast calculation and optimization makes the system easily expandable to robust and multi-criteria optimization. This work was funded in part by a grant from Varian Medical Systems, Inc.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

TH-A-9A-08: Knowledge-Based Quality Control of Clinical Stereotactic Radiosurgery Treatment Plans

Purpose: To develop a quality control tool to reduce stereotactic radiosurgery (SRS) planning variability using models that predict achievable plan quality metrics (QMs) based on individual patient anatomy. Methods: Using a knowledge-based methodology that quantitatively correlates anatomical geometric features to resultant organ-at-risk (OAR) dosimetry, we developed models for predicting achievable OAR dose-volume histograms (DVHs) by training with a cohort of previously treated SRS patients. The DVH-based QMs used in this work are the gradient measure, GM=(3/4pi)^1/3*[V50%^1/3−V100%^1/3], and V10Gy of normal brain. As GM quantifies the total rate of dose fall-off around the planning target volume (PTV), all voxels inside the patient's body contour were treated as OAR for DVH prediction. 35 previously treated SRS plans from our institution were collected; all were planned with non-coplanar volumetric-modulated arc therapy to prescription doses of 12–25 Gy. Of the 35-patient cohort, 15 were used for model training and 20 for model validation. Accuracies of the predictions were quantified by the mean and the standard deviation of the difference between clinical and predicted QMs, δQM=QM-clin−QM-pred. Results: Best agreement between predicted and clinical QMs was obtained when models were built separately for V-PTV<2.5cc and V-PTV>2.5cc. Eight patients trained the V-PTV<2.5cc model and seven patients trained the V-PTV>2.5cc models, respectively. The mean and the standard deviation of δGM were 0.3±0.4mm for the training sets and −0.1±0.6mm for the validation sets, demonstrating highly accurate GM predictions. V10Gy predictions were also highly accurate, with δV10Gy=0.8±0.7cc for the training sets and δV10Gy=0.7±1.4cc for the validation sets. Conclusion: The accuracy of the models in predicting two key SRS quality metrics highlights the potential of this technique for quality control for SRS treatments. Future investigations will seek to determine whether QM variations are due to residual model inaccuracies or true plan quality variations in the clinical sample. Support from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-BRD-08: Reducing the Effect of Respiratory Motion On the Delivered Dose in Proton Therapy Through Proper Field Angle Selection

Purpose: This work investigated a novel planning strategy of selecting radiotherapy beam angles that minimizes the change in water equivalent thickness (dWET) during respiration in order to reduce the effects of respiratory motion in passively scattered proton therapy (PSPT). Methods: In a clinical trial treating locally-advanced lung cancer with proton therapy, 2–4 co-planar beams were previously selected by dosimetrists in the design of physician-approved PSPT treatment plans. The authors identified a cohort of patients in which respiratory motion affected the planned PSPT dose delivery. For this cohort, this work analyzed dWET during respiration over a 360 degree arc of potential treatment angles around the patient: the dWET was defined as the difference in WET between the full-exhale (T50) and full-inhale (T0) phases of the simulation 4DCT. New PSPT plans were redesigned by selecting new beam angles that demonstrated significant reduction in the value of dWET. Between the T50 and T0 phases, the root-mean-square deviation of dose and the change in dose-volume histogram curves (dAUC) for anatomical structures were calculated to compare the original to dWET reduction plans. Results: To date, three plans were retrospectively redesigned based on dWET analysis. In the dWET reduction plan, the root mean square dose (T50-T0) was reduced by 15–35% and the DVH dAUC values were reduced by more than 60%.The PSPT plans redesigned by selecting appropriate field angles to minimize dWET demonstrated less dosimetric variation due to respiration. Conclusion: We have introduced the use of a new metric to quantify respiratory motion in proton therapy: dWET. The use of dWET allows us to minimize the impact of respiratory motion of the entire anatomy in the beam path. This work is a proof of principle that dWET could suggest field angles in proton therapy that are more robust to the effects of respiratory motion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-BRD-09: Successes and Limitations of Online Range Adaptive Spot Scanning Proton Therapy for NSCLC

Purpose: To determine the ability to adapt discrete spot-scanning proton therapy (SSPT) plans based on geometric changes of anatomy to minimize normal tissue dose and maintain target coverage. Methods: We developed and tested a range-correction algorithm to compensate for anatomy changes in SSPT with correction factors for target lateral size changes and energy scaling. This algorithm adjusts the energy of each spot from the original optimized treatment plan to match the new daily anatomy based on water equivalent path-length. To correct for the lateral target size changes, the peripheral spots were scaled based on phantom studies with variable target size. For energy change corrections, alternative cumulative scaling factor lookup tables were generated based on calculated central-axis and integral depth dose calculations for different energies. These various adaptive algorithms were performed on 7 lung cancer patients that were previously treated with proton therapy and who required at least one adaptive intervention. Single-field optimized SSPT plans were generated for these patients with clinical beam angles. Dose-volume histogram metrics were obtained for these patients for both the non-adaptive and the different adaptive plans applied to the last available weekly CT scan. Results: The doses to normal tissue were largely reduced for the spinal cord (Dmax), total lung (V20Gy), and contralateral lung (V20Gy) for all different methods of adaptive planning. With both corrections applied, the average changes for these metrics were −6.2Gy, −2.7%, and −4.9%, respectively. The same method generated unacceptably high target hot spots with average target V110% increase of 12.3%. Conclusion: Adaptive methods based on direct adjustments to proton range can reduce normal tissue doses under large anatomical changes but are insufficient in achieving clinically acceptable target doses and generate unacceptably sizeable hot spots. Adaptive planning methods for proton therapy will likely necessitate some aspect of reoptimization for acceptable clinical utility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.

60 APPLIED LIFE SCIENCES↗