Evaluation of the accuracy of deformable image registration on MRI with a physical phantom.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) has gained popularity in radiation therapy simulation because it provides superior soft tissue contrast, which facilitates more accurate target delineation compared with computed tomography (CT) and does not expose the patient to ionizing radiation. However, image registration errors in commercial software have not been widely reported. Here we evaluated the accuracy of deformable image registration (DIR) by using a physical phantom for MRI. METHODS AND MATERIALS: We used the "Wuphantom" for end-to-end testing of DIR accuracy for MRI. This acrylic phantom is filled with water and includes several fillable inserts to simulate various tissue shapes and properties. Deformations and changes in anatomic locations are simulated by changing the rotations of the phantom and inserts. We used Varian Velocity DIR software (v4.0) and CT (head and neck protocol) and MR (T1- and T2-weighted head protocol) images to test DIR accuracy between image modalities (MRI vs CT) and within the same image modality (MRI vs MRI) in 11 rotation deformation scenarios. Large inserts filled with Mobil DTE oil were used to simulate fatty tissue, and small inserts filled with agarose gel were used to simulate tissues slightly denser than water (e.g., prostate). Contours of all inserts were generated before DIR to provide a baseline for contour size and shape. DIR was done with the MR Correctable Deformable DIR method, and all deformed contours were compared with the original contours. The Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were used to quantitatively validate DIR accuracy. We also used large and small regions of interest (ROIs) during between-modality DIR tests to simulate validation of DIR accuracy for organs at risk (OARs) and propagation of individual clinical target volume (CTV) contours. RESULTS: No significant differences in DIR accuracy were found for T1:T1 and T2:T2 comparisons (P > 0.05). DIR was less accurate for between-modality comparisons than for same-modality comparisons, and was less accurate for T1 vs CT than for T2 vs CT (P < 0.001). For between-modality comparisons, use of a small ROI improved DIR accuracy for both T1 and T2 images. CONCLUSION: The simple design of the Wuphantom allows seamless testing of DIR; here we validated the accuracy of MRI DIR in end-to-end testing. T2 images had superior DIR accuracy compared with T1 images. Use of small ROIs improves DIR accuracy for target contour propagation.

Uncertainty incorporated beam angle optimization for IMPT treatment planning.

PURPOSE: Beam angle optimization (BAO) by far remains an important and challenging problem in external beam radiation therapy treatment planning. Conventional BAO algorithms discussed in previous studies all focused on photon-based therapies. Impact of BAO on proton therapy is important while proton therapy increasingly receives great interests. This study focuses on potential benefits of BAO on intensity-modulated proton therapy (IMPT) that recently began available to clinical cancer treatment. METHODS: The authors have developed a novel uncertainty incorporated BAO algorithm for IMPT treatment planning in that IMPT plan quality is highly sensitive to uncertainties such as proton range and setup errors. A linear programming was used to optimize robust intensity maps to scenario-based uncertainties for an incident beam angle configuration. Unlike conventional intensity-modulated radiation therapy with photons (IMXT), the search space for IMPT treatment beam angles may be relatively small but optimizing an IMPT plan may require higher computational costs due to larger data size. Therefore, a deterministic local neighborhood search algorithm that only needs a very limited number of plan objective evaluations was used to optimize beam angles in IMPT treatment planning. RESULTS: Three prostate cancer cases and two skull base chordoma cases were studied to demonstrate the dosimetric advantages and robustness of optimized beam angles from the proposed BAO algorithm. Two- to four-beam plans were optimized for prostate cases, and two- and three-beam plans were optimized for skull base cases. By comparing plans with conventional two parallel-opposed angles, all plans with optimized angles consistently improved sparing at organs at risks, i.e., rectum and femoral heads for prostate, brainstem for skull base, in either nominal dose distribution or uncertainty-based dose distributions. The efficiency of the BAO algorithm was demonstrated by comparing it with alternative methods including simulated annealing and genetic algorithm. The numbers of IMPT plan objective evaluations required were reduced by up to a factor of 5 while the same optimal angle plans were converged in selected comparisons. CONCLUSIONS: Uncertainty incorporated BAO may introduce pronounced improvement of IMPT plan quality including dosimetric benefits and robustness over uncertainties, based on the five clinical studies in this paper. In addition, local search algorithms may be more efficient in finding optimal beam angles than global optimization approaches for IMPT BAO.

Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy.

This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non-small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman-Kutcher-Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R2, area under the receiver operator curve (AUC), and Hosmer-Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763-0.854), and the best overall performance with a Negelkerke R2 value of 0.332 (95% CI: 0.266-0.486). Both of the optimism-corrected Negelkerke R2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.

Clinical outcomes after intensity-modulated proton therapy with concurrent chemotherapy for inoperable non-small cell lung cancer.

BACKGROUND & PURPOSE: We report disease control, survival, and toxicity in patients with advanced inoperable non-small cell lung cancer (NSCLC) receiving concurrent chemotherapy and intensity-modulated proton therapy (IMPT) at a single institution. MATERIAL AND METHODS: All patients were treated with IMPT with concurrent chemotherapy. Endpoints assessed were local, regional, and distant control, disease-free survival (DFS), and overall survival (OS). RESULTS: Fifty-one patients were enrolled with a median follow-up time of 23.0 months; 39 (76%) were treated with a simultaneous integrated boost to the gross tumor volume (GTV). The median GTV dose was 67.3 CGE and the median CTV dose was 60.0 CGE. Median OS and DFS times were 33.9 months and 12.6 months. The 3-year local control rate was 78.3%. Treatment was well tolerated, with a grade 3 toxicity rate of 18% (9 events: 4 esophagitis, 3 dermatitis, 1 esophageal stricture, and 1 fatigue) and no grade 4 or 5 toxicity. The most common grade 2 toxic effects were esophagitis (22 [43%]), dermatitis (16 [31%]), pain (15 [29%]), and fatigue (14 [27%]). CONCLUSIONS: Treatment of inoperable NSCLC with IMPT and concurrent chemotherapy achieves excellent disease control with tolerable toxicity.

Effectiveness of robust optimization in intensity-modulated proton therapy planning for head and neck cancers.

PURPOSE: Intensity-modulated proton therapy (IMPT) is highly sensitive to uncertainties in beam range and patient setup. Conventionally, these uncertainties are dealt using geometrically expanded planning target volume (PTV). In this paper, the authors evaluated a robust optimization method that deals with the uncertainties directly during the spot weight optimization to ensure clinical target volume (CTV) coverage without using PTV. The authors compared the two methods for a population of head and neck (H&N) cancer patients. METHODS: Two sets of IMPT plans were generated for 14 H&N cases, one being PTV-based conventionally optimized and the other CTV-based robustly optimized. For the PTV-based conventionally optimized plans, the uncertainties are accounted for by expanding CTV to PTV via margins and delivering the prescribed dose to PTV. For the CTV-based robustly optimized plans, spot weight optimization was guided to reduce the discrepancy in doses under extreme setup and range uncertainties directly, while delivering the prescribed dose to CTV rather than PTV. For each of these plans, the authors calculated dose distributions under various uncertainty settings. The root-mean-square dose (RMSD) for each voxel was computed and the area under the RMSD-volume histogram curves (AUC) was used to relatively compare plan robustness. Data derived from the dose volume histogram in the worst-case and nominal doses were used to evaluate the plan optimality. Then the plan evaluation metrics were averaged over the 14 cases and were compared with two-sided paired t tests. RESULTS: CTV-based robust optimization led to more robust (i.e., smaller AUCs) plans for both targets and organs. Under the worst-case scenario and the nominal scenario, CTV-based robustly optimized plans showed better target coverage (i.e., greater D95%), improved dose homogeneity (i.e., smaller D5% - D95%), and lower or equivalent dose to organs at risk. CONCLUSIONS: CTV-based robust optimization provided significantly more robust dose distributions to targets and organs than PTV-based conventional optimization in H&N using IMPT. Eliminating the use of PTV and planning directly based on CTV provided better or equivalent normal tissue sparing.

A novel dose-based positioning method for CT image-guided proton therapy.

PURPOSE: Proton dose distributions can potentially be altered by anatomical changes in the beam path despite perfect target alignment using traditional image guidance methods. In this simulation study, the authors explored the use of dosimetric factors instead of only anatomy to set up patients for proton therapy using in-room volumetric computed tomographic (CT) images. METHODS: To simulate patient anatomy in a free-breathing treatment condition, weekly time-averaged four-dimensional CT data near the end of treatment for 15 lung cancer patients were used in this study for a dose-based isocenter shift method to correct dosimetric deviations without replanning. The isocenter shift was obtained using the traditional anatomy-based image guidance method as the starting position. Subsequent isocenter shifts were established based on dosimetric criteria using a fast dose approximation method. For each isocenter shift, doses were calculated every 2 mm up to ± 8 mm in each direction. The optimal dose alignment was obtained by imposing a target coverage constraint that at least 99% of the target would receive at least 95% of the prescribed dose and by minimizing the mean dose to the ipsilateral lung. RESULTS: The authors found that 7 of 15 plans did not meet the target coverage constraint when using only the anatomy-based alignment. After the authors applied dose-based alignment, all met the target coverage constraint. For all but one case in which the target dose was met using both anatomy-based and dose-based alignment, the latter method was able to improve normal tissue sparing. CONCLUSIONS: The authors demonstrated that a dose-based adjustment to the isocenter can improve target coverage and/or reduce dose to nearby normal tissue.

Multifield optimization intensity-modulated proton therapy (MFO-IMPT) for prostate cancer: Robustness analysis through simulation of rotational and translational alignment errors.

To evaluate the dosimetric consequences of rotational and translational alignment errors in patients receiving intensity-modulated proton therapy with multifield optimization (MFO-IMPT) for prostate cancer. Ten control patients with localized prostate cancer underwent treatment planning for MFO-IMPT. Rotational and translation errors were simulated along each of 3 axes: anterior-posterior (A-P), superior-inferior (S-I), and left-right. Clinical target-volume (CTV) coverage remained high with all alignment errors simulated. Rotational errors did not result in significant rectum or bladder dose perturbations. Translational errors resulted in larger dose perturbations to the bladder and rectum. Perturbations in rectum and bladder doses were minimal for rotational errors and larger for translational errors. Rectum V45 and V70 increased most with A-P misalignment, whereas bladder V45 and V70 changed most with S-I misalignment. The bladder and rectum V45 and V70 remained acceptable even with extreme alignment errors. Even with S-I and A-P translational errors of up to 5mm, the dosimetric profile of MFO-IMPT remained favorable. MFO-IMPT for localized prostate cancer results in robust coverage of the CTV without clinically meaningful dose perturbations to normal tissue despite extreme rotational and translational alignment errors.