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Purpose: To develop an automated optimization program to generate optimal beam settings for whole-breast radiation therapy driven by clinically oriented goals., Materials and Methods: Forty patients were retrospectively included in this study. Each patient's planning images, contoured structures of planning target volumes, organs-at-risk, and breast wires were used to optimize for patient-specific-beam settings. Two beam geometries were available tangential beams only and tangential plus supraclavicular beams. Beam parameters included isocenter position, gantry, collimator, couch angles, and multileaf collimator shape. A geometry-based goal function was defined to determine such beam parameters to minimize out-of-field target volume and in-field ipsilateral lung volume. For each geometry, the weighting in the goal function was trained with 10 plans and tested on 10 additional plans. For each query patient, the optimal beam setting was searched for different gantry-isocenter pairs. Optimal fluence maps were generated by an in-house automatic fluence optimization program for target coverage and homogeneous dose distribution, and dose calculation was performed in Eclipse. Automatically generated plans were compared with manually generated plans for target coverage and lung and heart sparing., Results: The program successfully produced a set of beam parameters for every patient. Beam optimization time ranged from 10 to 120 s. The automatic plans had overall comparable plan quality to manually generated plans. For all testing cases, the mean target V 95% was 91.0% for the automatic plans and 88.5% for manually generated plans. The mean ipsilateral lung V 20Gy was lower for the automatic plans (15.2% vs 17.9%). The heart mean dose, maximum dose of the body, and conformity index were all comparable., Conclusion: We developed an automated goal-driven beam setting optimization program for whole-breast radiation therapy. It provides clinically relevant solutions based on previous clinical practice as well as patient specific anatomy on a substantially faster time frame.

This paper reports an inverse arc-modulated radiation therapy planning technique based on linear models. It is implemented with a two-step procedure. First, fluence maps for 36 fixed-gantry beams are generated using a linear model-based intensity-modulated radiation therapy (IMRT) optimization algorithm. The 2D fluence maps are decomposed into 1D fluence profiles according to each leaf pair position. Second, a mixed integer linear model is used to construct the leaf motions of an arc delivery that reproduce the 1D fluence profile previously derived from the static gantry IMRT optimization. The multi-leaf collimator (MLC) sequence takes into account the starting and ending leaf positions in between the neighbouring apertures, such that the MLC segments of the entire treatment plan are deliverable in a continuous arc. Since both steps in the algorithm use linear models, implementation is simple and straightforward. Details of the algorithm are presented, and its conceptual correctness is verified with clinical cases representing prostate and head-and-neck treatments.

Use of magnetic resonance (MR) imaging in radiation therapy has increased substantially in recent years as more radiotherapy centers are having MR simulators installed, requesting more time on clinical diagnostic MR systems, or even treating with combination MR linear accelerator (MR‐linac) systems. With this increased use, to ensure the most accurate integration of images into radiotherapy (RT), RT immobilization devices and accessories must be able to be used safely in the MR environment and produce minimal perturbations. The determination of the safety profile and considerations often falls to the medical physicist or other support staff members who at a minimum should be a Level 2 personnel as per the ACR. The purpose of this guidance document will be to help guide the user in making determinations on MR Safety labeling (i.e., MR Safe, Conditional, or Unsafe) including standard testing, and verification of image quality, when using RT immobilization devices and accessories in an MR environment. [ABSTRACT FROM AUTHOR]

Purpose: There is a strong clinical need to evaluate different multi‐criteria optimization (MCO) algorithms, including inverse optimization sampling algorithms and machine learning‐based predictions. This study aims to develop and compare several interpolated Pareto surface similarity metrics. Materials and methods: The first metric is the root‐mean‐square error (RMSE) evaluated between vertices on the interpolated surfaces, augmented by intra‐simplex sampling of the barycentric coordinates of the surfaces' simplicial complexes. The second metric is the average projected distance (APD), which evaluates the displacements between the vertices and computes their projections along the mean displacement. The third metric is the average nearest‐point distance (ANPD), which numerically integrates point‐to‐simplex distances over the sampled simplices of the interpolated surfaces. These metrics were compared by their convergence rates, the times required to achieve convergence, and their representation of the underlying surface interpolations. For analysis, several interpolated Pareto surface pairs were constructed abstractly, with one pair from a nasopharyngeal treatment planning case using MCO. Results: Convergence within 1% is typically achieved at approximately 50 and 80 samples per barycentric dimension for the RMSE and the ANPD, respectively. Calculation requires approximately 1 and 10 ms to achieve convergence for the RMSE and the ANPD in two dimensions, respectively, while the APD always requires < 1 ms. These time costs are much higher in higher dimensions for just the RMSE and ANPD. The APD values more closely approximated the ANPD limits than the RMSE limits. Conclusion: The ANPD's formulation and generality make it likely more meaningful than the RMSE and APD for representing the similarity between the underlying interpolated surfaces rather than the sampling points on the surfaces. However, in situations requiring high‐speed evaluations, the APD may be more desirable due to its speed, independence from a subjectively chosen sampling rate, and similarity to the ANPD limits. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop an accurate and reliable dose volume histogram (DVH) prediction method for external beam radiation therapy plans with multiple planning target volumes (PTVs). Materials and methods: We present a novel DVH prediction workflow, including new features and a modeling methodology, that makes better use of multiple PTVs: (a) We propose a generalized feature to characterize the geometric relationship of organ‐at‐risk (OARs) with respect to two or more PTVs with different prescribed dose levels; (b) We incorporate a novel data augmentation method to improve the data distribution in the feature space; (c) A similarity metric that leverages such information is subsequently used to select a subset of similar cases from the training dataset for model building; (d) Finally, a DVH prediction model is trained with these selected cases. To evaluate this new modeling workflow, we used 120 head and neck (HN) cases to tune the model, and used a separate dataset consisting of 148 cases for validation. The proposed model has been compared with the conventional knowledge‐based model in terms of model prediction accuracy, which was measured by the root mean squared error (RMSE) between the predicted DVHs and the actual clinical plan DVHs. Furthermore, 25 randomly selected plans were replanned guided by the proposed model and evaluated against clinical plans using clinical evaluation criteria. Results: The proposed modeling workflow significantly improved DVH prediction accuracy for brainstem (P < 0.001), cord (P < 0.001), larynx (P = 0.004), mandible (P < 0.001), oral cavity (P = 0.011), parotid (P < 0.001) and pharynx (P = 0.001). Cases replanned with the guidance of the proposed model spared OARs significantly better by clinical evaluation criteria. The replanned cases showed a 15% increase in the number of satisfied criteria, compared with clinical plans. Conclusions: The proposed modeling workflow generates DVH predictions with improved accuracy and robustness when multiple PTVs exist in a plan. It has demonstrated that the improvement in the DVH prediction model translates into better plan quality in knowledge‐based planning. [ABSTRACT FROM AUTHOR]

Purpose: To develop an automatic treatment planning system for whole breast radiation therapy (WBRT) based on two intensity-modulated tangential fields, enabling near-real-time planning. Methods and Materials: A total of 40 WBRT plans from a single institution were included in this study under IRB approval. Twenty WBRT plans, 10 with single energy (SE, 6MV) and 10 with mixed energy (ME, 6/15MV), were randomly selected as training dataset to develop the methodology for automatic planning. The rest 10 SE cases and 10 ME cases served as validation. The auto-planning process consists of three steps. First, an energy prediction model was developed to automate energy selection. This model establishes an anatomy-energy relationship based on principle component analysis (PCA) of the gray level histograms from training cases' digitally reconstructed radiographs (DRRs). Second, a random forest (RF) model generates an initial fluence map using the selected energies. Third, the balance of overall dose contribution throughout the breast tissue is realized by automatically selecting anchor points and applying centrality correction. The proposed method was tested on the validation dataset. Non-parametric equivalence test was performed for plan quality metrics using one-sided Wilcoxon Signed-Rank test. Results: For validation, the auto-planning system suggested same energy choices as clinical-plans in 19 out of 20 cases. The mean (standard deviation, SD) of percent target volume covered by 100% prescription dose was 82.5% (4.2%) for auto-plans, and 79.3% (4.8%) for clinical-plans (p > 0.999). Mean (SD) volume receiving 105% Rx were 95.2 cc (90.7 cc) for auto-plans and 83.9 cc (87.2 cc) for clinical-plans (p = 0.108). Optimization time for auto-plan was <20 s while clinical manual planning takes between 30 min and 4 h. Conclusions: We developed an automatic treatment planning system that generates WBRT plans with optimal energy selection, clinically comparable plan quality, and significant reduction in planning time, allowing for near-real-time planning. [ABSTRACT FROM AUTHOR]

Purpose: Intensity‐Modulated Radiation Therapy (IMRT), including its variations (including IMRT, Volumetric Arc Therapy (VMAT), and Tomotherapy), is a widely used and critically important technology for cancer treatment. It is a knowledge‐intensive technology due not only to its own technical complexity, but also to the inherently conflicting nature of maximizing tumor control while minimizing normal organ damage. As IMRT experience and especially the carefully designed clinical plan data are accumulated during the past two decades, a new set of methods commonly termed knowledge‐based planning (KBP) have been developed that aim to improve the quality and efficiency of IMRT planning by learning from the database of past clinical plans. Some of this development has led to commercial products recently that allowed the investigation of KBP in numerous clinical applications. In this literature review, we will attempt to present a summary of published methods of knowledge‐based approaches in IMRT and recent clinical validation results. Methods: In March 2018, a literature search was conducted in the NIH Medline database using the PubMed interface to identify publications that describe methods and validations related to KBP in IMRT including variations such as VMAT and Tomotherapy. The search criteria were designed to have a broad scope to capture relevant results with high sensitivity. The authors filtered down the search results according to a predefined selection criteria by reviewing the titles and abstracts first and then by reviewing the full text. A few papers were added to the list based on the references of the reviewed papers. The final set of papers was reviewed and summarized here. Results: The initial search yielded a total of 740 articles. A careful review of the titles, abstracts, and eventually the full text and then adding relevant articles from reviewing the references resulted in a final list of 73 articles published between 2011 and early 2018. These articles described methods for developing knowledge models for predicting such parameters as dosimetric and dose‐volume points, voxel‐level doses, and objective function weights that improve or automate IMRT planning for various cancer sites, addressing different clinical and quality assurance needs, and using a variety of machine learning approaches. A number of articles reported carefully designed clinical studies that assessed the performance of KBP models in realistic clinical applications. Overwhelming majority of the studies demonstrated the benefits of KBP in achieving comparable and often improved quality of IMRT planning while reducing planning time and plan quality variation. Conclusions: The number of KBP‐related studies has been steadily increasing since 2011 indicating a growing interest in applying this approach to clinical applications. Validation studies have generally shown KBP to produce plans with quality comparable to expert planners while reducing the time and efforts to generate plans. However, current studies are mostly retrospective and leverage relatively small datasets. Larger datasets collected through multi‐institutional collaboration will enable the development of more advanced models to further improve the performance of KBP in complex clinical cases. Prospective studies will be an important next step toward widespread adoption of this exciting technology. [ABSTRACT FROM AUTHOR]

With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image‐guidance, motion tracking, quality assurance, and more. Despite this interest, practical clinical implementation of machine learning as part of the day‐to‐day clinical operations is still lagging. The aim of this white paper is to further promote progress in this new field of machine learning in radiation oncology by highlighting its untapped advantages and potentials for clinical advancement, while also presenting current challenges and open questions for future research. The targeted audience of this paper includes newcomers as well as practitioners in the field of medical physics/radiation oncology. The paper also provides general recommendations to avoid common pitfalls when applying these powerful data analytic tools to medical physics and radiation oncology problems and suggests some guidelines for transparent and informative reporting of machine learning results. [ABSTRACT FROM AUTHOR]

Background: To establish the feasibility of the dosimetric compliance criteria of the RTOG 1308 trial through testing against Intensity Modulation Radiation Therapy (IMRT) and Passive Scattering Proton Therapy (PSPT) plans.Methods: Twenty-six lung IMRT and 26 proton PSPT plans were included in the study. Dose Volume Histograms (DVHs) for targets and normal structures were analyzed. The quality of IMRT plans was assessed using a knowledge-based engineering tool.Results: Most of the RTOG 1308 dosimetric criteria were achieved. The deviation unacceptable rates were less than 10 % for most criteria; however, a deviation unacceptable rate of more than 20 % was computed for the planning target volume minimum dose compliance criterion. Dose parameters for the target volume were very close for the IMRT and PSPT plans. However, the PSPT plans led to lower dose values for normal structures. The dose parameters in which PSPT plans resulted in lower values than IMRT plans were: lung V5Gy (%) (34.4 in PSPT and 47.2 in IMRT); maximum spinal cord dose (31.7 Gy in PSPT and 43.5 Gy in IMRT); heart V5Gy (%) (19 in PSPT and 47 in IMRT); heart V30Gy (%) (11 in PSPT and 19 in IMRT); heart V45Gy (%) (7.8 in PSPT and 12.1 in IMRT); heart V50% (Gy) (7.1 in PSPT and 9.8 in IMRT) and mean heart dose (7.7 Gy in PSPT and 14.9 Gy in IMRT).Conclusions: The revised RTOG 1308 dosimetric compliance criteria are feasible and achievable. [ABSTRACT FROM AUTHOR]

Purpose: Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data. Methods: Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trained with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physicianˈs single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test. Results: Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models ( p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy ( p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models ( p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases ( p = 0.01). Conclusions: The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive radiation therapy for prostate cancer using online reoptimization provides an improved control of interfractional anatomy variations. However, the clinical implementation of online reoptimization is currently limited by the low efficiency of current strategies and the difficulties associated with integration into the current treatment planning system. This study investigates the strategies for performing fast (∼2 min) automatic online reoptimization with a clinical fluence-map-based treatment planning system; and explores the performance with different input parameters settings: dose-volume histogram (DVH) objective settings, starting stage, and iteration number (in the context of real time planning). Methods: Simulated treatments of 10 patients were reoptimized daily for the first week of treatment (5 fractions) using 12 different combinations of optimization strategies. Options for objective settings included guideline-based RTOG objectives, patient-specific objectives based on anatomy on the planning CT, and daily-CBCT anatomy-based objectives adapted from planning CT objectives. Options for starting stages involved starting reoptimization with and without the original plan's fluence map. Options for iteration numbers were 50 and 100. The adapted plans were then analyzed by statistical modeling, and compared both in terms of dosimetry and delivery efficiency. Results: All online reoptimized plans were finished within ∼2 min with excellent coverage and conformity to the daily target. The three input parameters, i.e., DVH objectives, starting stage, and iteration number, contributed to the outcome of optimization nearly independently. Patient-specific objectives generally provided better OAR sparing compared to guideline-based objectives. The benefit in high-dose sparing from incorporating daily anatomy into objective settings was positively correlated with the relative change in OAR volumes from planning CT to daily CBCT. The use of the original plan fluence map as the starting stage reduced OAR dose at the mid-dose region, but increased the monitor units by 17%. Differences of only 2cc or less in OAR V50%/V70Gy/V76Gy were observed between 100 and 50 iterations. Conclusions: It is feasible to perform automatic online reoptimization in ∼2 min using a clinical treatment planning system. Selecting optimal sets of input parameters is the key to achieving high quality reoptimized plans, and should be based on the individual patient's daily anatomy, delivery efficiency, and time allowed for plan adaptation. [ABSTRACT FROM AUTHOR]

Purpose: The authors present an evidence-based approach to quantify the effects of an array of patient anatomical features of the planning target volumes (PTVs) and organs-at-risk (OARs) and their spatial relationships on the interpatient OAR dose sparing variation in intensity modulated radiation therapy (IMRT) plans by learning from a database of high-quality prior plans. Methods: The authors formulized the dependence of OAR dose volume histograms (DVHs) on patient anatomical factors into feature models which were learned from prior plans by a stepwise multiple regression method. IMRT plans for 64 prostate, 82 head-and-neck (HN) treatments were used to train the models. Two major groups of anatomical features were considered in this study: the volumetric information and the spatial information. The geometry of OARs relative to PTV is represented by the distance-to-target histogram, DTH. Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. The final models were tested by additional 24 prostate and 24 HN plans. Results: Significant patient anatomical factors contributing to OAR dose sparing in prostate and HN IMRT plans have been analyzed and identified. They are: the median distance between OAR and PTV, the portion of OAR volume within an OAR specific distance range, and the volumetric factors: the fraction of OAR volume which overlaps with PTV and the portion of OAR volume outside the primary treatment field. Overall, the determination coefficients R2 for predicting the first principal component score (PCS1) of the OAR DVH by the above factors are above 0.68 for all the OARs and they are more than 0.53 for predicting the second principal component score (PCS2) of the OAR DVHs except brainstem and spinal cord. Thus, the above set of anatomical features combined has captured significant portions of the DVH variations for the OARs in prostate and HN plans. To test how well these features capture the interpatient organ dose sparing variations in general, the DVHs and specific dose-volume indices calculated from the regression models were compared with the actual DVHs and dose-volume indices from each patient's plan in the validation dataset. The dose-volume indices compared were V99%, V85%, and V50% for bladder and rectum in prostate plans and parotids median dose in HN plans. The authors found that for the bladder and rectum models, 17 out of 24 plans (71%) were within 6% OAR volume error and 21 plans (85%) were within 10% error; For the parotids model, the median dose values for 30 parotids out of 48 (63%) were within 6% prescription dose error and the values in 40 parotids (83%) were within 10% error. Conclusions: Quantitative analysis of patient anatomical features and their correlation with OAR dose sparing has identified a number of important factors that explain significant amount of interpatient DVH variations in OARs. These factors can be incorporated into evidence-based learning models as effective features to provide patient-specific OAR dose sparing goals. [ABSTRACT FROM AUTHOR]

Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy ∼80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART. [ABSTRACT FROM AUTHOR]

Purpose: This clinical study evaluates the feasibility of using volumetric arc–modulated treatment (VMAT) for spine stereotactic body radiotherapy (SBRT) to achieve highly conformal dose distributions that spare adjacent organs at risk (OAR) with reduced treatment time. Methods and Materials: Ten spine SBRT patients were studied retrospectively. The intensity-modulated radiotherapy (IMRT) and VMAT plans were generated using either one or two arcs. Planning target volume (PTV) dose coverage, OAR dose sparing, and normal tissue integral dose were measured and compared. Differences in treatment delivery were also analyzed. Results: The PTV DVHs were comparable between VMAT and IMRT plans in the shoulder (D99%–D90%), slope (D90%–D10%), and tail (D10%–D1%) regions. Only VMAT2arc had a better conformity index than IMRT (1.09 vs. 1.15, p = 0.007). For cord sparing, IMRT was the best, and VMAT1arc was the worst. Use of IMRT achieved greater than 10% more D1% sparing for six of 10 cases and 7% to 15% more D10% sparing over the VAMT1arc. The differences between IMRT and VAMT2arc were smaller and statistically nonsignificant at all dose levels. The differences were also small and statistically nonsignificant for other OAR sparing. The mean monitor units (MUs) were 8711, 7730, and 6317 for IMRT, VMAT1arc, and VMAT2arc plans, respectively, with a 26% reduction from IMRT to VMAT2arc. The mean treatment time was 15.86, 8.56, and 7.88 min for IMRT, VMAT1arc, and VMAT2arc. The difference in integral dose was statistically nonsignificant. Conclusions: Although VMAT provided comparable PTV coverage for spine SBRT, 1arc showed significantly worse spinal cord sparing compared with IMRT, whereas 2arc was comparable to IMRT. Treatment efficiency is substantially improved with the VMAT. [Copyright &y& Elsevier]

This study compares three online image guidance techniques (IGRT) for prostate IMRT treatment: bony-anatomy matching, soft-tissue matching, and online replanning. Six prostate IMRT patients were studied. Five daily CBCT scans from the first week were acquired for each patient to provide representative “snapshots” of anatomical variations during the course of treatment. Initial IMRT plans were designed for each patient with seven coplanar 15 MV beams on a Eclipse treatment planning system. Two plans were created, one with a PTV margin of 10 mm and another with a 5 mm PTV margin. Based on these plans, the delivered dose distributions to each CBCT anatomy was evaluated to compare bony-anatomy matching, soft-tissue matching, and online replanning. Matching based on bony anatomy was evaluated using the 10 mm PTV margin (“bone10”). Soft-tissue matching was evaluated using both the 10 mm (“soft10”) and 5 mm (“soft5”) PTV margins. Online reoptimization was evaluated using the 5 mm PTV margin (“adapt”). The replanning process utilized the original dose distribution as the basis and linear goal programming techniques for reoptimization. The reoptimized plans were finished in less than 2 min for all cases. Using each IGRT technique, the delivered dose distribution was evaluated on all 30 CBCT scans (6 patients×5CBCT/patient). The mean minimum dose (in percentage of prescription dose) to the CTV over five treatment fractions were in the ranges of 99%–100% (SD=0.1%–0.8%), 65%–98% (SD=0.4%–19.5%), 87%–99% (SD=0.7%–23.3%), and 95%–99% (SD=0.4%–10.4%) for the adapt, bone10, soft5, and soft10 techniques, respectively. Compared to patient position correction techniques, the online reoptimization technique also showed improvement in OAR sparing when organ motion/deformations were large. For bladder, the adapt technique had the best (minimum) D90, D50, and D30 values for 24, 17, and 15 fractions out of 30 total fractions, while it also had the best D90, D50, and D30 values for the rectum for 25, 16, and 19 fractions, respectively. For cases where the adapt plans did not score the best for OAR sparing, the gains of the OAR sparing in the repositioning-based plans were accompanied by an underdosage in the target volume. To further evaluate the fast online replanning technique, a gold-standard plan (“new” plan) was generated for each CBCT anatomy on the Eclipse treatment planning system. The OAR sparing from the online replanning technique was compared to the new plan. The differences in D90, D50, and D30 of the OARs between the adapt and the new plans were less than 5% in 3 patients and were between 5% and 10% for the remaining three. In summary, all IGRT techniques could be sufficient to correct simple geometrical variations. However, when a high degree of deformation or differential organ position displacement occurs, the online reoptimization technique is feasible with less than 2 min optimization time and provides improvements in both CTV coverage and OAR sparing over the position correction techniques. For these cases, the reoptimization technique can be a highly valuable online IGRT tool to correct daily treatment uncertainties, especially when hypofractionation scheme is applied and daily correction, rather than averaging over many fractions, is required to match the original plan. [ABSTRACT FROM AUTHOR]

A new Novalis Tx system equipped with a high definition multileaf collimator (HDMLC) recently became available to perform both image-guided radiosurgery and conventional radiotherapy. It is capable of delivering a highly conformal radiation dose with three energy modes: 6 MV photon energy, 15 MV photon energy, and 6 MV photon energy in a stereotactic radiosurgery mode with 1000 MU/min dose rate. Dosimetric characteristics of the new Novalis Tx treatment unit with the HDMLC are systematically measured for commissioning. A high resolution diode detector and miniion-chamber detector are used to measure dosimetric data for a range of field sizes from 4×4 mm to 400×400 mm. The commissioned Novalis Tx system has passed the RPC stereotactic radiosurgery head phantom irradiation test. The Novalis Tx system not only expands its capabilities with three energy modes, but also achieves better beam conformity and sharer beam penumbra with HDMLC. Since there is little beam data information available for the new Novalis Tx system, we present in this work the dosimetric data of the new modality for reference and comparison. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we investigate a technique of matching internal target volumes (ITVs) in four-dimensional (4D) simulation computed tomography (CT) to the composite target volume in free-breathing on-board cone-beam (CB) CT. The technique is illustrated by using both phantom and patient cases. Methods and Materials: A dynamic phantom with a target ball simulating respiratory motion with various amplitude and cycle times was used to verify localization accuracy. The dynamic phantom was scanned using simulation CT with a phase-based retrospective sorting technique. The ITV was then determined based on 10 sets of sorted images. The size and epicenter of the ITV identified from 4D simulation CT images and the composite target volume identified from on-board CBCT images were compared to assess localization accuracy. Similarly, for two clinical cases of patients with lung cancer, ITVs defined from 4D simulation CT images and CBCT images were compared. Results: For the phantom, localization accuracy between the ITV in 4D simulation CT and the composite target volume in CBCT was within 1 mm, and ITV was within 8.7%. For patient cases, ITVs on simulation CT and CBCT were within 8.0%. Conclusion: This study shows that CBCT is a useful tool to localize ITV for targets affected by respiratory motion. Verification of the ITV from 4D simulation CT using on-board free-breathing CBCT is feasible for the target localization of lung tumors. [ABSTRACT FROM AUTHOR]

Purpose: High-precision intensity-modulated radiotherapy demands high patient positioning accuracy. On-board digital tomosynthesis (DTS) provides three-dimensional (3D) image guidance for daily positioning with a lower imaging dose, faster acquisition, and more geometric flexibility than 3D cone-beam computed tomography (CBCT). This clinical study evaluated DTS as a daily imaging technique for patient positioning and compared the results with 3D CBCT and two-dimensional (2D) radiography. Methods and Materials: Head and neck cancer patients undergoing intensity-modulated radiotherapy were studied. For each session, the patient was positioned using laser marks. On-board imaging data sets, including 2D kilovoltage radiographs, DTS, and CBCT, were obtained to measure the daily patient positioning variations. The mean and standard deviations of the positioning variations in the translational and rotational directions were calculated. The positioning differences among 2D radiography, DTS, and CBCT were analyzed. Results: Image data sets were collected from 65 treatment fractions for 10 patients. The systematic patient positioning variation was <0.10 cm and 1.0 degrees one dimensionally. The random variations were 0.27-0.34 cm in the translational and 0.93 degrees -1.99 degrees in the rotational direction. The mean vector isocenter variation was 0.48 cm. DTS with 40 degrees and 20 degrees scan angles in the coronal or sagittal directions yielded the same results for patient positioning. DTS performance was comparable to that of CBCT, with positioning differences of <0.1 cm and 0.5 degrees . The positioning difference between 2D radiography and DTS was approximately 0.1 cm and 0.2 cm in the vertical/longitudinal and lateral directions. Conclusion: Our results have demonstrated that DTS is a comparable 3D imaging technique to CBCT for daily patient positioning of head-and-neck patients as determined by manual registration of bony anatomy. [ABSTRACT FROM AUTHOR]

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations. [ABSTRACT FROM AUTHOR]

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution. Methods: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol. Results: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct. Conclusions: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results. The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique. [ABSTRACT FROM AUTHOR]

Purpose: To explore an effective and efficient end-to-end patient-specific quality-assurance (QA) protocol for volumetric modulated arc radiotherapy (VMAT) and to evaluate the suitability of a stationary radiotherapy QA device (two-dimensional [2D] ion chamber array) for VMAT QA. Methods and Materials: Three methods were used to analyze 39 VMAT treatment plans for brain, spine, and prostate: ion chamber (one-dimensional absolute, n = 39), film (2D relative, coronal/sagittal, n = 8), and 2D ion chamber array (ICA, 2D absolute, coronal/sagittal, n = 39) measurements. All measurements were compared with the treatment planning system dose calculation either via gamma analysis (3%, 3- to 4-mm distance-to-agreement criteria) or absolute point dose comparison. The film and ion chamber results were similarly compared with the ICA measurements. Results: Absolute point dose measurements agreed well with treatment planning system computed doses (ion chamber: median deviation, 1.2%, range, −0.6% to 3.3%; ICA: median deviation, 0.6%, range, −1.8% to 2.9%). The relative 2D dose measurements also showed good agreement with computed doses (>93% of pixels in all films passing gamma, >90% of pixels in all ICA measurements passing gamma). The ICA relative dose results were highly similar to those of film (>90% of pixels passing gamma). The coronal and sagittal ICA measurements were statistically indistinguishable by the paired t test with a hypothesized mean difference of 0.1%. The ion chamber and ICA absolute dose measurements showed a similar trend but had disparities of 2-3% in 18% of plans. Conclusions: After validating the new VMAT implementation with ion chamber, film, and ICA, we were able to maintain an effective yet efficient patient-specific VMAT QA protocol by reducing from five (ion chamber, film, and ICA) to two measurements (ion chamber and single ICA) per plan. The ICA (Matrixx®, IBA Dosimetry) was validated for VMAT QA, but ion chamber measurements are recommended for absolute dose comparison until future developments correct the ICA angular dependence. [ABSTRACT FROM AUTHOR]

Purpose: Pancreas stereotactic body radiation therapy (SBRT) treatment planning requires planners to make sequential, time-consuming interactions with the treatment planning system to reach the optimal dose distribution. We sought to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently.Methods and Materials: The focus of pancreas SBRT planning is finding a balance between organ-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to organ-at-risk dose constraints. We formulated such interactions between the planner and treatment planning system into a finite-horizon RL model. First, planning status features were evaluated based on human planners' experience and defined as planning states. Second, planning actions were defined to represent steps that planners would commonly implement to address different planning needs. Finally, we derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients, and generated plans for 24 cases in a separate validation set.Results: All 24 bot-generated plans achieved similar PTV coverages compared with clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot could be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions were consistent, indicating reproducibility of the learning process.Conclusions: We developed a planning bot that generates high-quality treatment plans for pancreas SBRT. We demonstrated that the training phase of the bot is tractable and reproducible, and the knowledge acquired is interpretable. As a result, the RL planning bot can potentially be incorporated into the clinical workflow and reduce planning inefficiencies. [ABSTRACT FROM AUTHOR]

Purpose: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning.Methods and Materials: We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose-volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose-volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation.Results: The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable.Conclusions: Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework. [ABSTRACT FROM AUTHOR]

Purpose: To use knowledge-based planning (KBP) as a method of producing high-quality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG) 0631.Methods and Materials: An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues.Results: The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3% ± 3.2% to 98.3% ± 1.4% (P = .01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cm3 for manual plans to 1.8 ± 0.6 cm3 with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledge-based planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target.Conclusion: Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard. [ABSTRACT FROM AUTHOR]

Purpose: To provide a benchmark for seminal vesicle (SV) margin selection to account for intrafractional motion and to investigate the effectiveness of 2 motion surrogates in predicting intrafractional SV coverage.Methods and Materials: Fifteen prostate patients were studied. Each patient had 5 pairs (1 patient had 4 pairs) of pretreatment and posttreatment cone beam CTs (CBCTs). Each pair of CBCTs was registered on the basis of prostate fiducial markers. All pretreatment SVs were expanded with 1-, 2-, 3-, 4-, 5-, and 8-mm isotropic margins to form a series of planning target volumes, and their intrafractional coverage to the posttreatment SV determined the "ground truth" for exact coverage. Two motion surrogates, the center of mass (COM) and the border of contour, were evaluated by the use of Pearson product-moment correlation coefficient and exponential fitting for predicting SV underdosage. Action threshold of each surrogate was calculated. The margin for each surrogate was calculated according to a traditional margin recipe.Results: Ninety-five percent posttreatment SV coverage was achieved in 9%, 53%, 73%, 86%, 95%, and 97% of fractions with 1-, 2-, 3-, 4-, 5-, and 8-mm margins, respectively. The 5-mm margins provided 95% intrafractional SV coverage in over 90% of fractions. The correlation between the COM and border was weak, moderate, and strong in the left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. Exponential fitting gave the underdosage threshold of 4.5 and 7.0 mm for the COM and border. The Van Herk margin recipe recommended 0-, 0.5-, and 0.8-mm margins in the L-R, A-P, and S-I directions based on the COM, and 1.2-, 3.9-, and 2.5-mm margins based on the border.Conclusions: Five-millimeter isotropic margins for the SV constitute the minimum required to mitigate the intrafractional motion. Both the COM and the border are acceptable predictors for SV underdosage with 4.5- and 7.0-mm action threshold. Traditional margin based on the COM or border underestimates the margin. [ABSTRACT FROM AUTHOR]