Experience-based quality control of clinical intensity-modulated radiotherapy planning.
01 Oct 2011-International Journal of Radiation Oncology Biology Physics (Elsevier)-Vol. 81, Iss: 2, pp 545-551
TL;DR: This quality control tool was incorporated into the intensity-modulated radiotherapy (IMRT) plan generation process and proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.
Abstract: Purpose To incorporate a quality control tool, according to previous planning experience and patient-specific anatomic information, into the intensity-modulated radiotherapy (IMRT) plan generation process and to determine whether the tool improved treatment plan quality. Methods and Materials A retrospective study of 42 IMRT plans demonstrated a correlation between the fraction of organs at risk (OARs) overlapping the planning target volume and the mean dose. This yielded a model, predicted dose = prescription dose (0.2 + 0.8 [1 − exp(−3 overlapping planning target volume/volume of OAR)]), that predicted the achievable mean doses according to the planning target volume overlap/volume of OAR and the prescription dose. The model was incorporated into the planning process by way of a user-executable script that reported the predicted dose for any OAR. The script was introduced to clinicians engaged in IMRT planning and deployed thereafter. The script’s effect was evaluated by tracking δ = (mean dose-predicted dose)/predicted dose, the fraction by which the mean dose exceeded the model. Results All OARs under investigation (rectum and bladder in prostate cancer; parotid glands, esophagus, and larynx in head-and-neck cancer) exhibited both smaller δ and reduced variability after script implementation. These effects were substantial for the parotid glands, for which the previous δ = 0.28 ± 0.24 was reduced to δ = 0.13 ± 0.10. The clinical relevance was most evident in the subset of cases in which the parotid glands were potentially salvageable (predicted dose Conclusions This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process.
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TL;DR: Two major strategies, acting synergistically, will enable further widening of the therapeutic window of radiation oncology in the era of precision medicine: technology-driven improvement of treatment conformity, including advanced image guidance and particle therapy, and novel biological concepts for personalized treatment.
Abstract: Technological advances and clinical research over the past few decades have given radiation oncologists the capability to personalize treatments for accurate delivery of radiation dose based on clinical parameters and anatomical information. Eradication of gross and microscopic tumours with preservation of health-related quality of life can be achieved in many patients. Two major strategies, acting synergistically, will enable further widening of the therapeutic window of radiation oncology in the era of precision medicine: technology-driven improvement of treatment conformity, including advanced image guidance and particle therapy, and novel biological concepts for personalized treatment, including biomarker-guided prescription, combined treatment modalities and adaptation of treatment during its course.
592 citations
TL;DR: The results demonstrate that a mathematical framework and modest training cohorts successfully predict achievable OAR DVHs based on individual patient anatomy using mathematical models that predict achievable organ-at-risk dose-volume histograms (DVHs) based onindividual patient anatomy.
Abstract: Purpose:
The objective of this work was to develop a quality control (QC) tool to reduce intensity modulated radiotherapy (IMRT) planning variability and improve treatment plan quality using mathematical models that predict achievable organ-at-risk (OAR) dose-volume histograms (DVHs) based on individual patient anatomy.
Methods:
A mathematical framework to predict achievable OAR DVHs was derived based on the correlation of expected dose to the minimum distance from a voxel to the PTV surface. OAR voxels sharing a range of minimum distances were computed as subvolumes. A three-parameter, skew-normal probability distribution was used to fit subvolume dose distributions, and DVH prediction models were developed by fitting the evolution of the skew-normal parameters as a function of distance with polynomials. Cohorts of 20 prostate and 24 head-and-neck IMRT plans with identical clinical objectives were used to train organ-specificaverage models for rectum, bladder, and parotids. A sum of residuals analysis quantifying the integrated difference between the clinically approved DVH and predicted DVH evaluated similarity between DVHs. The ability of the average models to prospectively predict DVHs was evaluated on an independent validation cohort of 20 prostate plans. Statistical comparison of the sums of residuals between training and validation cohorts quantified the accuracy of the average model. Restricted sums of residuals (RSR) were used to identify potential outliers, where large values of RSR indicate a clinical DVH that exceeds the predicted DVH by a considerable amount. A refined model was obtained for each organ by excluding outliers with large RSR values from the training cohort. The refined model was applied to the original training cohort and restricted sums of residuals were utilized to estimate potential DVH improvements. All cases were replanned and evaluated by the physician that approved the original plan. The ability of the refined models to correctly identify outliers was assessed using the residual sum between the original and replanned DVHs to quantify dosimetric gains realized under replanning.
Results:
Statistical analysis of average sum of residuals for rectum (SR¯rectum=0.003±0.037), bladder (SR¯bladder=−0.008±0.037), and parotid (SR¯parotid=−0.003±0.060) training cohorts yielded mean values near zero and small with respect to the standard deviations, indicating that the average models are capturing the essential behavior of the training cohorts. The predictive abilities of the average rectum and bladder models were statistically indistinguishable between the training and validation sets, with SR¯rectum=0.002±0.044 and SR¯bladder=−0.018±0.058 for the validation set. The refined models’ ability to detect outliers and predict achievable OAR DVHs was demonstrated by a strong correlation between predicted gains (RSR) and realized gains after replanning with sample correlation coefficients of r = 0.92 for the rectum, r = 0.88 for the bladder, and r = 0.84 for the parotid glands.
Conclusions:
The results demonstrate that our mathematical framework and modest training cohorts successfully predict achievable OAR DVHs based on individual patient anatomy. The models correctly identified suboptimal plans that demonstrated further OAR sparing after replanning. This modeling technique requires no manual intervention except for appropriate selection of a training set with identical evaluation criteria. Clinical implementation is in progress to evaluate impact on real-time IMRT QC.
336 citations
TL;DR: 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.
Abstract: 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 organdose 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.
265 citations
TL;DR: A new automated radiotherapy treatment planning system based on a deep learning-based three-dimensional dose prediction and 3D dose distribution-based optimization is developed, which is a promising approach for realizing automated treatment planning in the future.
Abstract: Purpose To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm. Methods and materials A residual neural network-based deep learning model is trained to predict a dose distribution based on patient-specific geometry and prescription dose. A total of 270 head-and-neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation. Results Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose-volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV70.4 , but the difference is only 0.5% which is still clinically acceptable. Conclusion This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution-based optimization. It is a promising approach for realizing automated treatment planning in the future.
225 citations
TL;DR: RapidPlan knowledge-based treatment plans were comparable to clinical plans if the patient's OAR-planning target volume geometry was within the range of those included in the models.
Abstract: Purpose Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries. Methods and Materials Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model 30A and Model 30B , and were combined in a third model, Model 60 . Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HI B /HI E = 100 × (D2% − D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (D sal , D swal , and D oc , respectively). Results For EG1, RapidPlan improved HI B and HI E values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable D sal and D swal values were seen in Model 30A , Model 30B , and Model 60 , decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model 30B increasing D oc by 0.1, 3.2, and 2.8 Gy compared with CP, Model 30A , and Model 60 . Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased D sal by 4.1 to 4.9 Gy on average, whereas HI B and HI E decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively. Conclusions RapidPlan knowledge-based treatment plans were comparable to CP if the patient's OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.
225 citations
Cites background or methods from "Experience-based quality control of..."
...Being able to rationally predict OAR dose-volume histograms (DVHs) could remove the need for performing interactive optimization or multiple iterative optimizations and could increase consistency in treatment planning (14-18)....
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...et al (15), evaluated the correlation between the fraction of OARs overlapping with the PTVs and the mean OAR doses and were able to identify patients in whom large gains in parotid gland sparing could be achieved....
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References
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TL;DR: A new method, intensity-modulated arc therapy (IMAT), for delivering optimized treatment plans to improve the therapeutic ratio andvantages of this new technique over tomotherapy and other slice-based delivery schemes are discussed.
Abstract: The desire to improve local tumour control and cure more cancer patients, coupled with advances in computer technology and linear accelerator design, has spurred the developments of three-dimensional conformal radiotherapy techniques. Optimized treatment plans, aiming to deliver high dose to the target while minimizing dose to the surrounding tissues, can be delivered with multiple fields each with spatially modulated beam intensities or with multiple-slice treatments. This paper introduces a new method, intensity-modulated arc therapy (IMAT), for delivering optimized treatment plans to improve the therapeutic ratio. It utilizes continuous gantry motion as in conventional arc therapy. Unlike conventional arc therapy, the field shape, which is conformed with the multileaf collimator, changes during gantry rotation. Arbitrary two-dimensional beam intensify distributions at different beam angles are delivered with multiple superimposing arcs. A system capable of delivering the IMAT has been implemented. An example is given that illustrates the feasibility of this new method. Advantages of this new technique over tomotherapy and other slice-based delivery schemes are also discussed.
738 citations
TL;DR: In this article, the available dose/volume/outcome data for rectal injury were reviewed, and the authors found that the volume of rectum receiving ≥60Gy is consistently associated with the risk of Grade ≥2 rectal toxicity or rectal bleeding.
Abstract: The available dose/volume/outcome data for rectal injury were reviewed. The volume of rectum receiving ≥60Gy is consistently associated with the risk of Grade ≥2 rectal toxicity or rectal bleeding. Parameters for the Lyman-Kutcher-Burman normal tissue complication probability model from four clinical series are remarkably consistent, suggesting that high doses are predominant in determining the risk of toxicity. The best overall estimates (95% confidence interval) of the Lyman-Kutcher-Burman model parameters are n = 0.09 (0.04–0.14); m = 0.13 (0.10–0.17); and TD50 = 76.9 (73.7–80.1) Gy. Most of the models of late radiation toxicity come from three-dimensional conformal radiotherapy dose-escalation studies of early-stage prostate cancer. It is possible that intensity-modulated radiotherapy or proton beam dose distributions require modification of these models because of the inherent differences in low and intermediate dose distributions.
633 citations
"Experience-based quality control of..." refers background in this paper
...For example, the increased risk of Grade 2 or greater rectal toxicity or rectal bleeding has been associated with the volume of tissue receiving $60 Gy (11)....
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TL;DR: An inverse planning algorithm for determining the intensity-modulated beams that will most closely generate a desired dose distribution is presented, and the optimization algorithm yielded plans that featured higher target dose homogeneity, compared with the human planner's plan, while selectively sparing more of the normal organs at the desired dose regions.
Abstract: An inverse planning algorithm for determining the intensity-modulated beams that will most closely generate a desired dose distribution is presented. The algorithm is three-dimensional and does not explicitly depend on beam energies and modalities. It allows a single prescription dose or a window of acceptable doses to be specified for the target, with additional constraints to account for under- or over-dosing. For the protection of organs at risk, it provides maximum-dose and dose-volume constraints. The latter apply to the entire volume of the organ exposed to the corresponding dose levels. Several levels of each type of constraint, with varying penalty weights, may be specified for each organ. The objective function that serves as the measure of the goodness of the solution is of the least-squares type and is minimized using conjugate gradient methods. Typical clinical cases involving 40,000 points and 4000 rays to be determined require about 10 min of CPU time on a DEC AlphaStation. Results are presented for two clinical sites, prostate and lung. The optimization algorithm yielded plans that featured higher target dose homogeneity, compared with the human planner's plan, while selectively sparing more of the normal organs at the desired dose regions.
377 citations
"Experience-based quality control of..." refers background in this paper
...Techniques such as constrained (1, 2), prioritized (3), and multi-criteria (4, 5) optimization might be more consistent in their output than unconstrained optimization....
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TL;DR: A method of IMRT treatment plan quality control that helps planners to evaluate the doses of the OARs upon completion of a new plan and introduces the concept of a shape relationship descriptor and the overlap volume histogram (OVH) to describe the spatial configuration of an OAR with respect to a target.
Abstract: Purpose: Intensity modulated radiation therapy (IMRT) treatment plan quality depends on the planner's level of experience and the amount of time the planner invests in developing the plan. Planners often unwittingly accept plans when further sparing of the organs at risk (OARs) is possible. The authors propose a method of IMRT treatment plan quality control that helps planners to evaluate the doses of the OARs upon completion of a new plan. Methods: It is achieved by comparing the geometric configurations of the OARs and targets of a new patient with those of prior patients, whose plans are maintained in a database. They introduce the concept of a shape relationship descriptor and, specifically, the overlap volume histogram (OVH) to describe the spatial configuration of an OAR with respect to a target. The OVH provides a way to infer the likely DVHs of the OARs by comparing the relative spatial configurations between patients. A database of prior patients is built to serve as an external reference. At the conclusion of a new plan, planners search through the database and identify related patients by comparing the OAR-target geometric relationships of the new patient with those of prior patients. The treatment plans of thesemore » related patients are retrieved from the database and guide planners in determining whether lower doses delivered to the OARs in the new plan are feasible. Results: Preliminary evaluation is promising. In this evaluation, they applied the analysis to the parotid DVHs of 32 prior head-and-neck patients, whose plans are maintained in a database. Each parotid was queried against the other 63 parotids to determine whether a lower dose was possible. The 17 parotids that promised the greatest reduction in D{sub 50} (DVH dose at 50% volume) were flagged. These 17 parotids came from 13 patients. The method also indicated that the doses of the other nine parotids of the 13 patients could not be reduced, so they were included in the replanning process as controls. Replanning with an effort to reduce D{sub 50} was conducted on these 26 parotids. After replanning, the average reductions for D{sub 50} of the 17 flagged parotids and nine unflagged parotids were 6.6 and 1.9 Gy, respectively. These results demonstrate that the quality control method has accurately identified not only the parotids that require dose reductions but also those for which dose reductions are marginal. Originally, 11 of out the 17 flagged parotids did not meet the Radiation Therapy Oncology Group sparing goal of V(30 Gy)<50%. Replanning reduced them to three. Additionally, PTV coverage and OAR sparing of the original plans were compared to those of the replans by using pairwise Wilcoxon p test. The statistical comparisons show that replanning compromised neither PTV coverage nor OAR sparing. Conclusions: This method provides an effective quality control mechanism for evaluating the DVHs of the OARs. Adoption of such a method will advance the quality of current IMRT planning, providing better treatment plan consistency.« less
298 citations
"Experience-based quality control of..." refers background in this paper
...(9); however, it was chosen in the present study because of its straightforward development into...
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Journal Article•
TL;DR: The Fox Chase Cancer Center and its affiliate network have evaluated induction chemotherapy with paclitaxel plus carboplatin with or without granulocyte colony-stimulating factor priming followed by concurrent systemic chemotherapy and radiation therapy in patients with locally advanced non-small cell lung cancer.
266 citations