Knowledge-based planning for multi-isocenter VMAT total marrow irradiation

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. 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-specific average 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. 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. 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.

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. 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. 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 approximately 80% in prediction and effectiveness in improving ART planning quality. An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

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. 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. 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 <30 Gy). Before script implementation, an average of 30.1 Gy was delivered to the salvageable cases, with an average predicted dose of 20.3 Gy. After implementation, an average of 18.7 Gy was delivered to salvageable cases, with an average predicted dose of 17.2 Gy. In the prostate cases, the rectum model excess was reduced from δ = 0.28 ± 0.20 to δ = 0.07 ± 0.15. On surveying dosimetrists at the end of the study, most reported that the script both improved their IMRT planning (8 of 10) and increased their efficiency (6 of 10). This tool proved successful in increasing normal tissue sparing and reducing interclinician variability, providing effective quality control of the IMRT plan development process. Copyright © 2011 Elsevier Inc. All rights reserved.