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DOE OSTI2016

SU-D-BRB-01: A Comparison of Learning Methods for Knowledge Based Dose Prediction for Coplanar and Non-Coplanar Liver Radiotherapy

Purpose: The predictive power of knowledge based planning (KBP) has considerable potential in the development of automated treatment planning. Here, we examine the predictive capabilities and accuracy of previously reported KBP methods, as well as an artificial neural networks (ANN) method. Furthermore, we compare the predictive accuracy of these methods on coplanar volumetric-modulated arc therapy (VMAT) and non-coplanar 4π radiotherapy. Methods: 30 liver SBRT patients previously treated using coplanar VMAT were selected for this study. The patients were re-planned using 4π radiotherapy, which involves 20 optimally selected non-coplanar IMRT fields. ANNs were used to incorporate enhanced geometric information including liver and PTV size, prescription dose, patient girth, and proximity to beams. The performance of ANN was compared to three methods from statistical voxel dose learning (SVDL), wherein the doses of voxels sharing the same distance to the PTV are approximated by either taking the median of the distribution, non-parametric fitting, or skew-normal fitting. These three methods were shown to be capable of predicting DVH, but only median approximation can predict 3D dose. Prediction methods were tested using leave-one-out cross-validation tests and evaluated using residual sum of squares (RSS) for DVH and 3D dose predictions. Results: DVH prediction using non-parametric fitting had the lowest average RSS with 0.1176(4π) and 0.1633(VMAT), compared to 0.4879(4π) and 1.8744(VMAT) RSS for ANN. 3D dose prediction with median approximation had lower RSS with 12.02(4π) and 29.22(VMAT), compared to 27.95(4π) and 130.9(VMAT) for ANN. Conclusion: Paradoxically, although the ANNs included geometric features in addition to the distances to the PTV, it did not perform better in predicting DVH or 3D dose compared to simpler, faster methods based on the distances alone. The study further confirms that the prediction of 4π non-coplanar plans were more accurate than VMAT. NIH R43CA183390 and R01CA188300.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-355: Evaluation of Knowledge-Based Planning Model for the Cervical Cancer Radiotherapy

Purpose: The Varian RapidPlan™ is a commercial knowledge-based optimization process which uses a set of clinically used treatment plans to train a model that can predict individualized dose-volume objectives. The purpose of this study is to evaluate the performance of RapidPlan to generate intensity modulated radiation therapy (IMRT) plans for cervical cancer. Methods: Totally 70 IMRT plans for cervical cancer with varying clinical and physiological indications were enrolled in this study. These patients were all previously treated in our institution. There were two prescription levels usually used in our institution: 45Gy/25 fractions and 50.4Gy/28 fractions. 50 of these plans were selected to train the RapidPlan model for predicting dose-volume constraints. After model training, this model was validated with 10 plans from training pool(internal validation) and additional other 20 new plans(external validation). All plans used for the validation were re-optimized with the original beam configuration and the generated priorities from RapidPlan were manually adjusted to ensure that re-optimized DVH located in the range of the model prediction. DVH quantitative analysis was performed to compare the RapidPlan generated and the original manual optimized plans. Results: For all the validation cases, RapidPlan based plans (RapidPlan) showed similar or superior results compared to the manual optimized ones. RapidPlan increased the result of D98% and homogeneity in both two validations. For organs at risk, the RapidPlan decreased mean doses of bladder by 1.25Gy/1.13Gy (internal/external validation) on average, with p=0.12/p<0.01. The mean dose of rectum and bowel were also decreased by an average of 2.64Gy/0.83Gy and 0.66Gy/1.05Gy,with p<0.01/ p<0.01and p=0.04/<0.01 for the internal/external validation, respectively. Conclusion: The RapidPlan model based cervical cancer plans shows ability to systematically improve the IMRT plan quality. It suggests that RapidPlan has great potential to make the treatment planning process more efficient.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data

This work describes a new approach to increase the accuracy of Reynolds-averaged Navier–Stokes (RANS) in modeling turbulence flow leveraging the machine learning technique. Traditionally, different turbulence models for Reynolds stress are developed for different flow patterns based on human knowledge. Each turbulence model has a certain application domain and prediction uncertainty. In recent years, with the rapid improvements of machine learning techniques, researchers start to develop an approach to compensate for the prediction discrepancy of traditional turbulence models with statistical models and data. However, the approach has deficiencies in several aspects. For example, the amount of human knowledge introduced to the statistical model couldn’t be controlled, which makes the statistical model learn from a very naïve stage and limits its application. In this work, a new approach is developed to address those deficiencies. Here, the new approach uses the “ensemble learning” technique to control the amount of human knowledge introduced into the statistical model. Therefore, the new approach could be adaptive to the multiple application domains. In conclusion, according to the results of case study, the new approach shows higher accuracy than both traditional turbulence models and the previous machine learning approach.

42 ENGINEERING↗

DOE OSTI2019

Solar soft cost ontology for multi-stakeholder knowledge base

Introduction: Solar soft costs can account for 60% of the cost of an installed solar system, thus cost reductions for solar hinge on reducing soft costs. Soft costs are most frequently defined as non-hardware costs. Defining something by what it isn’t makes it inherently difficult to identify what exactly it is, which in turn makes it difficult to study. Our goal is to develop an ontology that identifies 1) a consistent and defined soft cost vocabulary, 2) who creates, shares and acquires soft cost knowledge, and 3) the learning processes used to facilitate knowledge flows. Ultimately, identifying these aspects of solar soft costs will provide a pathway for public policy and market interventions to reduce soft costs. Methods: To build the solar soft cost ontology (SSCO) we built a corpus of solar soft cost literature (through keyword search), then coded the corpus for all concepts related to or influencing costs, barriers, or time. Finally, we organized the concepts into taxonomies, including soft costs and network actors, and developed the properties (i.e., attributes and relationships) that link the concepts. In phase two, we will code a set of solar installer case studies and complimentary solar sector (e.g., distributors, software providers, trade organizations) firm case studies to update the SSCO, documenting changes and additions resulting from the use of practitioner feedback. Results and Discussion: The resulting SSCO includes concepts, attributes, and relationships that provide a rich and deep knowledge base that merges academic and practitioner expertise to create a shared and exhaustive vocabulary related to soft costs, a network of actors that impact those soft costs, and the attributes and relationships that connect them. The applications of the SSCO extend to a diverse group of stakeholders, including researchers, policy makers, and practitioners, engaged in reducing solar soft costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-T-357: Semi-Automated Knowledge-Based Radiation Therapy (KBRT) Planning for Head-And-Neck Cancer (HNC): Can KBRT Plans Achieve Better Results Than Manual Planning?

Purpose: Knowledge Based Radiation Therapy Treatment (KBRT) planning can be used to semi-automatically generate IMRT plans for new patients using constraints derived from previously manually-planned, geometrically similar patients. We investigate whether KBRT plans can achieve greater dose sparing than manual plans using optimized, organspecific constraint weighting factors. Methods: KBRT planning of HNC radiotherapy cases geometrically matched each new (query) case to one of the 105 clinically approved plans in our database. The dose distribution of the planned match was morphed to fit the querys geometry. Dose-volume constraints extracted from the morphed dose distribution were used to run the IMRT optimization with no user input. In the first version, all constraints were multiplied by a weighting factor of 0.7. The weighting factors were then systematically optimized (in order of OARs with increasing separation from the target) to maximize sparing to each OAR without compromising other OARs. The optimized, second version plans were compared against the first version plans and the clinically approved plans for 45 unilateral/bilateral target cases using the dose metrics: mean, median and maximum (brainstem and cord) doses. Results: Compared to the first version, the second version significantly reduced mean/median contralateral parotid doses (>2Gy) for bilateral cases. Other changes between the two versions were not clinically meaningful. Compared to the original clinical plans, both bilateral and unilateral plans in the second version had lower average dose metrics for 5 of the 6 OARs. Compared to the original plans, the second version achieved dose sparing that was at least as good for all OARs and better for the ipsilateral parotid (bilateral) and oral cavity (bilateral/unilateral). Differences in planning target volume coverage metrics were not clinically significant. Conclusion: HNC-KBRT planning generated IMRT plans with at least equivalent dose sparing to manually generated plans; greater dose sparing was achieved in selected OARs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Enhancing EV Motor Design Through Knowledge-Based AI and Hierarchical Fuzzy Logic Model

This work presents a novel approach to optimizing electric vehicle motor design through the integration of Knowledge-Based Artificial Intelligence (KB-AI) and Hierarchical Fuzzy Logic. Traditional motor design processes are time-intensive, relying heavily on iterative simulations and domain-specific expertise. These processes are further complicated by the nonlinear relationships between key design parameters. The proposed framework addresses these challenges by systematically encoding expert knowledge from scientific literature into a fuzzy logic system, allowing for the efficient handling of complex design variables. The hierarchical fuzzy logic model reduces computational complexity by decomposing the nonlinear relationships into manageable rule sets while maintaining design accuracy. The proposed methodology was applied to the design of a 100 kW motor, yielding optimal values for key parameters. This resulted in a compact motor design with a volume of 2.2 liters, showcasing the framework’s ability to deliver high-performance, application-specific motor configurations.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗

DOE OSTI2017

Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials

Purpose: To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system. Methods and Materials: We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V{sub 10} (percentage volume of PBM receiving at least 10 Gy dose) and V{sub 20} (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V{sub 40} (absolute volume of bowel receiving at least 40 Gy dose) and V{sub 45} (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning “routine”). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBP{sub FINAL}), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBP{sub FINAL} plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets. Results: KBP{sub FINAL} plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBP{sub FINAL} versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBP{sub FINAL} plans, indicating lowered probability of hematologic toxicity. Conclusions: This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Developing a Fundamental Knowledge Base for the Interaction of Ruthenium with Natural and Engineered Iron-Bearing Materials

The intent of this project is to build a fundamental knowledge base concerning the redox coupling between Ru and Fe in heterogeneous systems with predictive applications towards the fate, speciation, and transport of Ru in the environment and its capture using available technologies. RuNOCl 3 and Fe 3+ are redox active over a broad range of pH. Initial assessment of effect of redox coupling between Ru and Fe indicates that Fe(III) in ferrihydrite can oxidize Ru 2+ in RuNOCl3 to Ru(IV) during coprecipitation at ambient conditions and alkaline pH. Further, evidence suggests that Ru(IV) is incorporated in the structure of fh. Likewise, Fe 3+ (aq) also reduces Ru 2+ in RuNOCl 3 in the homogenous system at acidic pH although the kinetics of reaction appear to be slow. Rufh annealed to 500 0C transforms to Ru(III) doped hematite. AIMD informed EXAFS indicates that Ru substitutes for Fe(III) in regular octahedral sites of hematite but requires three distinct configurations involving edge-sharing protonated Fe vacancies and straight substitution with no local defects. Fe 2+ (aq) appears to oxidize at the surface of RuO 2 , but the amount is too low to reasonably expect detectable reduced Ru. Future experiments at higher pH are warranted to make Fe 2+ (aq) less stable (i.e., make more redox active). We also demonstrated that we can functionalize Si aerogels with Fe(II) and Fe(III) but likely need to go to higher T to make Feo nanoparticles.

36 MATERIALS SCIENCE↗

DOE OSTI2015

SU-E-J-244: Development and Validation of a Knowledge Based Planning Model for External Beam Radiation Therapy of Locally Advanced Non-Small Cell Lung Cancer

Purpose: The study aims to develop and validate a knowledge based planning (KBP) model for external beam radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). Methods: RapidPlan™ technology was used to develop a lung KBP model. Plans from 65 patients with LA-NSCLC were used to train the model. 25 patients were treated with VMAT, and the other patients were treated with IMRT. Organs-at-risk (OARs) included right lung, left lung, heart, esophagus, and spinal cord. DVH and geometric distribution DVH were extracted from the treated plans. The model was trained using principal component analysis and step-wise multiple regression. Box plot and regression plot tools were used to identify geometric outliers and dosimetry outliers and help fine-tune the model. The validation was performed by (a) comparing predicted DVH boundaries to actual DVHs of 63 patients and (b) using an independent set of treatment planning data. Results: 63 out of 65 plans were included in the final KBP model with PTV volume ranging from 102.5cc to 1450.2cc. Total treatment dose prescription varied from 50Gy to 70Gy based on institutional guidelines. One patient was excluded due to geometric outlier where 2.18cc of spinal cord was included in PTV. The other patient was excluded due to dosimetric outlier where the dose sparing to spinal cord was heavily enforced in the clinical plan. Target volume, OAR volume, OAR overlap volume percentage to target, and OAR out-of-field volume were included in the trained model. Lungs and heart had two principal component scores of GEDVH, whereas spinal cord and esophagus had three in the final model. Predicted DVH band (mean ±1 standard deviation) represented 66.2±3.6% of all DVHs. Conclusion: A KBP model was developed and validated for radiotherapy of LA-NSCLC in a commercial treatment planning system. The clinical implementation may improve the consistency of IMRT/VMAT planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance

Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Knowledge-based treatment planning and its potential role in the transition between treatment planning systems

Commissioning a new treatment planning system (TPS) involves many time-consuming tasks. We investigated the role that knowledge-based planning (KBP) can play in aiding a clinic's transition to a new TPS. Sixty clinically treated prostate/prostate bed intensity-modulated radiation therapy (IMRT) plans were exported from an in-house TPS and were used to create a KBP model in a newly implemented commercial application. To determine the benefit that KBP may have in a TPS transition, the model was tested on 2 groups of patients. Group 1 consisted of the first 10 prostate/prostate bed patients treated in the commercial TPS after the transition from the in-house TPS. Group 2 consisted of 10 patients planned in the commercial TPS after 8 months of clinical use. The KBP-generated plan was compared with the clinically used plan in terms of plan quality (ability to meet planning objectives and overall dose metrics) and planning efficiency (time required to generate clinically acceptable plans). The KBP-generated plans provided a significantly improved target coverage (p = 0.01) compared with the clinically used plans for Group 1, but yielded plans of comparable target coverage to the clinically used plans for Group 2. For the organs at risk, the KBP-generated plans produced lower doses, on average, for every normal-tissue objective except for the maximum dose to 0.1 cc of rectum. The time needed for the KBP-generated plans ranged from 6 to 15 minutes compared to 30 to 150 and 15 to 60 minutes for manual planning in Groups 1 and 2, respectively. KBP is a promising tool to aid in the transition to a new TPS. Our study indicates that high-quality treatment plans could have been generated in the newly implemented TPS more efficiently compared with not using KBP. Even after 8 months of the clinical use, KBP still showed an increase in plan quality and planning efficiency compared with manual planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

SU-E-P-58: Dosimetric Study of Conventional Intensity-Modulated Radiotherapy and Knowledge-Based Radiation Therapy for Postoperation of Cervix Cancer

Purpose: To compare the dosimetric difference of the target volume and organs at risk(OARs) between conventional intensity-modulated radiotherapy(C-IMRT) and knowledge-based radiation therapy (KBRT) plans for cervix cancer. Methods: 39 patients with cervical cancer after surgery were randomly selected, 20 patient plans were used to create the model, the other 19 cases used for comparative evaluation. All plans were designed in Eclipse system. The prescription dose was 30.6Gy, 17 fractions, OARs dose satisfied to the clinical requirement. A paired t test was used to evaluate the differences of dose-volume histograms (DVH). Results: Comparaed to C-IMRT plan, the KBRT plan target can achieve the similar target dose coverage, D98,D95,D2,HI and CI had no difference (P≥0.05). The dose of rectum, bladder and femoral heads had no significant differences(P≥0.05). The time was used to design treatment plan was significant reduced. Conclusion: This study shows that postoperative radiotherapy of cervical KBRT plans can achieve the similar target and OARs dose, but the shorter designing time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated using a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A knowledge based approach to matching human neurodegenerative disease and animal models

Neurodegenerative diseases present a wide and complex range of biological and clinical features. Animal models are key to translational research, yet typically only exhibit a subset of disease features rather than being precise replicas of the disease. Consequently, connecting animal to human conditions using direct data-mining strategies has proven challenging, particularly for diseases of the nervous system, with its complicated anatomy and physiology. To address this challenge we have explored the use of ontologies to create formal descriptions of structural phenotypes across scales that are machine processable and amenable to logical inference. As proof of concept, we built a Neurodegenerative Disease Phenotype Ontology (NDPO) and an associated Phenotype Knowledge Base (PKB) using an entity-quality model that incorporates descriptions for both human disease phenotypes and those of animal models. Entities are drawn from community ontologies made available through the Neuroscience Information Framework (NIF) and qualities are drawn from the Phenotype and Trait Ontology (PATO). We generated ~1200 structured phenotype statements describing structural alterations at the subcellular, cellular and gross anatomical levels observed in 11 human neurodegenerative conditions and associated animal models. PhenoSim, an open source tool for comparing phenotypes, was used to issue a series of competency questions to compare individual phenotypes among organisms and to determine which animal models recapitulate phenotypic aspects of the human disease in aggregate. Overall, the system was able to use relationships within the ontology to bridge phenotypes across scales, returning non-trivial matches based on common subsumers that were meaningful to a neuroscientist with an advanced knowledge of neuroanatomy. The system can be used both to compare individual phenotypes and also phenotypes in aggregate. This proof of concept suggests that expressing complex phenotypes using formal ontologies provides considerable benefit for comparing phenotypes across scales and species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

MO-G-201-04: Knowledge-Based Planning for Single-Isocenter Stereotactic Radiosurgery to Multiple Brain Metastases

Purpose: Single-isocenter, linac-based SRS for multiple brain metastases (multi-mets) can deliver highly conformal radiation doses and reduce overall patient treatment time compared to other therapy techniques. This study aims to quantify the dosimetric benefits of knowledge-based planning (KBP) for multi-met treatments. Methods: Using a previously-published KBP methodology (an artificial neural network (ANN) trained on single-target linac-based SRS plans), 3D dose distribution predictions for multi-met patients were obtained by treating each brain lesion as a solitary target and subsequently combining individual predictions into a single distribution using a dose-weighted geometric averaging to obtain the best results in the inter-target space. 17 previously-treated multi-met plans, with target numbers ranging from N=2–5, were used to validate the ANN predictions and subsequent KBP auto-planning routine. The fully-deliverable KBP plans were developed by converting dose distribution predictions into patient-specific optimization objectives while maintaining identical target normalizations (typically PTV V100%=D98%). Plan quality improvements were quantified by the difference between SRS quality metrics (QMs): δdQM=QM(clinical)-QM(KBP). QMs of interest were: gradient measure (GM), conformity index (CI), brain V10 and V5, brainstem D0.1cc and heterogeneity index (HI). Finally, overall plan quality was judged via blinded plan comparison by SRS-specializing physicians. Results: Two clinical plans were found to be significant outliers wherein plan quality was dramatically worse than KBP. Despite indicating KBP superiority, these were removed from the QM analysis to prevent skewing the results. In the remaining cases, clinical and KBP QMs were nearly identical with modest improvements in the KBP sample: δGM=0.12±0.56mm, δCI=−0.01±0.04, Brain δV10=0.8±2.6cc, brain δV5=6.3 ±10.7cc, brainstem δD0.1cc=0.06±1.19Gy and δHI= −0.04±0.05. Ultimately, 13/17 KBP plans were deemed superior to the manual plans in blinded physician review. Conclusion: The results demonstrate that KBP-driven automated planning in linac-based single-isocenter treatments for multiple brain metastases is indistinguishable from, or even better than, traditional manual planning. J. Hattangadi: Research Grant; Varian Medical Systems; K.L. Moore: Research Grant: Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-97: An Analysis of Knowledge Based Planning for Stereotactic Body Radiation Therapy of the Spine

Purpose: Creating high quality SBRT treatment plans for the spine is often tedious and time consuming. In addition, the quality of treatment plans can vary greatly between treatment facilities due to inconsistencies in planning methods. This study investigates the performance of knowledge-based planning (KBP) for spine SBRT. Methods: Treatment plans were created for 28 spine SBRT patients. Each case was planned to meet strict dose objectives and guidelines. After physician and physicist approval, the plans were added to a custom model in a KBP system (RapidPlan, Varian Eclipse v13.5). The model was then trained to be able to predict estimated DVHs and provide starting objective functions for future patients based on both generated and manual objectives. To validate the model, ten additional spine SBRT cases were planned manually as well as using the model objectives. Plans were compared based on planning time and quality (ability to meet the plan objectives, including dose metrics and conformity). Results: The average dose to the spinal cord and the cord PRV differed between the validation and control plans by <0.25% demonstrating iso-toxicity. Six out of 10 validation plans met all dose objectives without the need for modifications, and overall, target dose coverage was increased by about 4.8%. If the validation plans did not meet the dose requirements initially, only 1–2 iterations of modifying the planning parameters were required before an acceptable plan was achieved. While manually created plans usually required 30 minutes to 3 hours to create, KBP can be used to create similar quality plans in 15–20 minutes. Conclusion: KBP for spinal tumors has shown to greatly decrease the amount of time required to achieve high quality treatment plans with minimal human intervention and could feasibly be used to standardize plan quality between institutions. Supported by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Comparison of Planning Quality and Efficiency Between Conventional and Knowledge-based Algorithms in Nasopharyngeal Cancer Patients Using Intensity Modulated Radiation Therapy

Purpose: Intensity modulated radiation therapy (IMRT) is widely used to achieve a highly conformal dose and improve treatment outcome. However, plan quality and planning time are institute and planner dependent, and no standardized tool exists to recognize an optimal plan. RapidPlan, a knowledge-based algorithm, can generate constraints to assist optimization and produce high-quality IMRT plans. This report evaluated the quality and efficiency of using RapidPlan in nasopharyngeal carcinoma (NPC) IMRT planning. Methods and Materials: RapidPlan was configured using 79 radical IMRT plans for NPC; 20 consecutive NPC patients indicated for radical radiation therapy between October 2014 and May 2015 were then recruited to assess its performance. The ability of RapidPlan to produce acceptable plans was evaluated. For plans that could not achieve clinical acceptance, manual touch-up was performed. The IMRT plans produced without RapidPlan (manual plans) and with RapidPlan (RP-2 plans, including those with manual touch-up) were compared in terms of dosimetric quality and planning efficiency. Results: RapidPlan by itself could produce clinically acceptable plans for 9 of the 20 patients; manual touch-up increased the number of acceptable plans (RP-2 plans) to 19. The target dose coverage and conformity were very similar. No difference was found in the maximum dose to the brainstem and optic chiasm. RP-2 plans delivered a higher maximum dose to the spinal cord (46.4 Gy vs 43.9 Gy, P=.002) but a lower dose to the parotid (mean dose to right parotid, 37.3 Gy vs 45.4 Gy; left, 34.4 Gy vs 43.1 Gy; P<.001) and the right cochlea (mean dose, 48.6 Gy vs 52.6 Gy; P=.02). The total planning time for RP-2 plans was significantly less than that for manual plans (64 minutes vs 295 minutes, P<.001). Conclusions: This study shows that RapidPlan can significantly improve planning efficiency and produce quality IMRT plans for NPC patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗