Engineering topics

Schild, S

Publications and source records attributed to Schild, S.

8 records

DOE OSTI2016

SU-D-204-03: Comparison of Patient Positioning Methods Through Modeling of Acute Rectal Toxicity in Intensity Modulated Radiation Therapy for Prostate Cancer. Does Quality of Data Matter More Than the Quantity?

Purpose: To determine if differences in patient positioning methods have an impact on the incidence and modeling of grade >=2 acute rectal toxicity in prostate cancer patients who were treated with Intensity Modulated Radiation Therapy (IMRT). Methods: We compared two databases of patients treated with radiation therapy for prostate cancer: a database of 79 patients who were treated with 7 field IMRT and daily image guided positioning based on implanted gold markers (IGRTdb), and a database of 302 patients who were treated with 5 field IMRT and daily positioning using a trans-abdominal ultrasound system (USdb). Complete planning dosimetry was available for IGRTdb patients while limited planning dosimetry, recorded at the time of planning, was available for USdb patients. We fit Lyman-Kutcher-Burman (LKB) model to IGRTdb only, and Univariate Logistic Regression (ULR) NTCP model to both databases. We perform Receiver Operating Characteristics analysis to determine the predictive power of NTCP models. Results: The incidence of grade >= 2 acute rectal toxicity in IGRTdb was 20%, while the incidence in USdb was 54%. Fits of both LKB and ULR models yielded predictive NTCP models for IGRTdb patients with Area Under the Curve (AUC) in the 0.63 – 0.67 range. Extrapolation of the ULR model from IGRTdb to planning dosimetry in USdb predicts that the incidence of acute rectal toxicity in USdb should not exceed 40%. Fits of the ULR model to the USdb do not yield predictive NTCP models and their AUC is consistent with AUC = 0.5. Conclusion: Accuracy of a patient positioning system affects clinically observed toxicity rates and the quality of NTCP models that can be derived from toxicity data. Poor correlation between planned and clinically delivered dosimetry may lead to erroneous or poorly performing NTCP models, even if the number of patients in a database is large.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-FG-CAMPUS-TeP3-04: Deliverable Robust Optimization in IMPT Using Quadratic Objective Function

Purpose: To find and evaluate the way of applying deliverable MU constraints into robust spot intensity optimization in Intensity-Modulated- Proton-Therapy (IMPT) to prevent plan quality and robustness from degrading due to machine deliverable MU-constraints. Methods: Currently, the influence of the deliverable MU-constraints is retrospectively evaluated by post-processing immediately following optimization. In this study, we propose a new method based on the quasi-Newton-like L-BFGS-B algorithm with which we turn deliverable MU-constraints on and off alternatively during optimization. Seven patients with two different machine settings (small and large spot size) were planned with both conventional and new methods. For each patient, three kinds of plans were generated — conventional non-deliverable plan (plan A), conventional deliverable plan with post-processing (plan B), and new deliverable plan (plan C). We performed this study with both realistic (small) and artificial (large) deliverable MU-constraints. Results: With small minimum MU-constraints considered, new method achieved a slightly better plan quality than conventional method (D95% CTV normalized to the prescription dose: 0.994[0.992∼0.996] (Plan C) vs 0.992[0.986∼0.996] (Plan B)). With large minimum MU constraints considered, results show that the new method maintains plan quality while plan quality from the conventional method is degraded greatly (D95% CTV normalized to the prescription dose: 0.987[0.978∼0.994] (Plan C) vs 0.797[0.641∼1.000] (Plan B)). Meanwhile, plan robustness of these two method’s results is comparable. (For all 7 patients, CTV DVH band gap at D95% normalized to the prescription dose: 0.015[0.005∼0.043] (Plan C) vs 0.012[0.006∼0.038] (Plan B) with small MU-constraints and 0.019[0.009∼0.039] (Plan C) vs 0.030[0.015∼0.041] (Plan B) with large MU-constraints) Conclusion: Positive correlation has been found between plan quality degeneration and magnitude of deliverable minimal MU. Compared to conventional post-processing method, our new method of incorporating deliverable minimal MU-constraints directly into plan optimization, can produce machine-deliverable plans with better plan qualities and non-compromised plan robustness. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant and by The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-209-04: Fuzzy Robust Optimization in Intensity-Modulated Proton Therapy Planning to Account for Range and Patient Setup Uncertainties

Purpose: We propose to apply a robust optimization model based on fuzzy-logic constraints in the intensity-modulated proton therapy (IMPT) planning subject to range and patient setup uncertainties. The purpose is to ensure the plan robustness under uncertainty and obtain the best trade-off between tumor dose coverage and organ-at-risk(OAR) sparing. Methods: Two IMPT plans were generated for 3 head-and-neck cancer patients: one used the planning target volume(PTV) method; the other used the fuzzy robust optimization method. In the latter method, nine dose distributions were computed - the nominal one and one each for ±3mm setup uncertainties along three cardinal axes and for ±3.5% range uncertainty. For tumors, these nine dose distributions were explicitly controlled by adding hard constraints with adjustable parameters. For OARs, fuzzy constraints that allow the dose to vary within a certain range were used so that the tumor dose distribution was guaranteed by minimum compromise of that of OARs. We rendered this model tractable by converting the fuzzy constraints to linear constraints. The plan quality was evaluated using dose-volume histogram(DVH) indices such as tumor dose coverage(D95%), homogeneity(D5%-D95%), plan robustness(DVH band at D95%), and OAR sparing like D1% of brain and D1% of brainstem. Results: Our model could yield clinically acceptable plans. The fuzzy-logic robust optimization method produced IMPT plans with comparable target dose coverage and homogeneity compared to the PTV method(unit: Gy[RBE]; average[min, max])(CTV D95%: 59 [52.7, 63.5] vs 53.5[46.4, 60.1], CTV D5% - D95%: 11.1[5.3, 18.6] vs 14.4[9.2, 21.5]). It also generated more robust plans(CTV DVH band at D95%: 3.8[1.2, 5.6] vs 11.5[6.2, 16.7]). The parameters of tumor constraints could be adjusted to control the tradeoff between tumor coverage and OAR sparing. Conclusion: The fuzzy-logic robust optimization generates superior IMPT with minimum compromise of OAR sparing. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation. eRA Person ID(s) for the Principal Investigator: 11017970 (Research Supported by National Institutes of Health)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-CD-209-05: Impact of Spot Size and Spacing On the Quality of Robustly-Optimized Intensity-Modulated Proton Therapy Plans for Lung Cancer

Purpose: To investigate how spot size and spacing affect plan quality, especially, plan robustness and the impact of interplay effect, of robustly-optimized intensity-modulated proton therapy (IMPT) plans for lung cancer. Methods: Two robustly-optimized IMPT plans were created for 10 lung cancer patients: (1) one for a proton beam with in-air energy dependent large spot size at isocenter (σ: 5–15 mm) and spacing (1.53σ); (2) the other for a proton beam with small spot size (σ: 2–6 mm) and spacing (5 mm). Both plans were generated on the average CTs with internal-gross-tumor-volume density overridden to irradiate internal target volume (ITV). The root-mean-square-dose volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under RVH curves were used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Dose-volume-histogram indices including ITV coverage, homogeneity, and organs-at-risk (OAR) sparing were compared using Student-t test. Results: Compared to large spots, small spots resulted in significantly better OAR sparing with comparable ITV coverage and homogeneity in the nominal plan. Plan robustness was comparable for ITV and most OARs. With interplay effect considered, significantly better OAR sparing with comparable ITV coverage and homogeneity is observed using smaller spots. Conclusion: Robust optimization with smaller spots significantly improves OAR sparing with comparable plan robustness and similar impact of interplay effect compare to larger spots. Small spot size requires the use of larger number of spots, which gives optimizer more freedom to render a plan more robust. The ratio between spot size and spacing was found to be more relevant to determine plan robustness and the impact of interplay effect than spot size alone. This research was supported by the National Cancer Institute Career Developmental Award K25CA168984, by the Fraternal Order of Eagles Cancer Research Fund Career Development Award, by The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, by Mayo Arizona State University Seed Grant, and by The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-229: Magnetic Resonance Imaging of Small Fiducial Markers for Proton Beam Therapy

Purpose: For proton beam therapy, small fiducial markers are preferred for patient alignment due to less interference with the proton beam. Visualizing small fiducial markers can be challenging in MRI. This study intends to investigate MRI imaging protocols for better visualization of small fiducial markers. Methods: Two carbon and two coil-shaped gold markers were placed into a gel phantom. Both carbon markers had a diameter of 1mm and a length of 3mm. Both gold markers had a length of 5mm. One gold marker had a diameter of 0.5mm and the other had a diameter of 0.75mm. T1 VIBE, T2 SPACE, TrueFISP and susceptibility weighted (SW) images were acquired. To improve marker contrast, high spatial resolution was used to reduce partial volume effect. Slice thickness was 1.5mm for all four sequences and in-plane resolution was 0.6mm for TrueFISP, 0.7mm for T1 VIBE, and 0.8mm for T2 SPACE and SW. For comparison purpose, a 3D T1 VIBE image set at 3mm slice thickness and 1.2mm in-plane resolution was also acquired. Results: All markers were visible in all high-resolution image sets. In each image set, marker-induced signal void was the smallest (in diameter) for carbon markers, followed by the 0.5mm gold marker and the largest for the 0.75mm gold marker. The SW images had the largest marker-induced signal void. However, those might be confused by susceptibility-gradient-induced signal voids. T1 VIBE had good visualization of markers with nicely defined edges. T2 SPACE had reasonable visualization of markers but edges were slightly blurred. TrueFISP had good visualization of markers only if they were not masked by banding artifacts. As a comparison, all markers were hardly visible in the standard resolution T1 VIBE images. Conclusion: 3D high-resolution T1 VIBE and SW have great potential in providing good visualization of small fiducial markers for proton beam therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-618: Plan Robustness Study of Volumetric-Modulated Arc Therapy Vs. Intensity-Modulated Radiation Therapy for Head and Neck Cancer

Purpose: Lack of plan robustness may contribute to local failure in volumetric-modulated arc therapy (VMAT) to treat head and neck (H&N) cancer. Thus we compared plan robustness of VMAT with intensity-modulated radiation therapy (IMRT). Methods: VMAT and IMRT plans were created for 9 H&N cancer patients. For each plan, six new perturbed dose distributions were computed — one each for ± 3mm setup deviations along the S-I, A-P and L-R directions. We used three robustness quantification tools: (1) worst-case analysis (WCA); (2) dose-volume histograms (DVHs) band (DVHB); and (3) root-mean-square-dose deviation (RMSD) volume histogram (DDVH). DDVH represents the relative volume (y) on the vertical axis and the RMSD (x) on the horizontal axis. Similar to DVH, this means that y% of the volume of the indicated structure has the RMSD at least x Gy[RBE].The width from the first two methods at different target DVH indices (such as D95 and D5) and the area under the DDVH curves (AUC) for the target were used to indicate plan robustness. In these robustness quantification tools, the smaller the value, the more robust the plan is. Plan robustness evaluation metrics were compared using Wilcoxon test. Results: DVHB showed the width at D95 from IMRT to be larger than from VMAT (unit Gy) [1.59 vs 1.18 (p=0.49)], while the width at D5 from IMRT was found to be slightly larger than from VMAT [0.59 vs 0.54 (p=0.84)]. WCA showed similar results [D95: 3.28 vs 3.00 (p=0.56); D5: 1.68 vs 1.95 (p=0.23)]. DDVH showed the AUC from IMRT to be slightly smaller than from VMAT [1.13 vs 1.15 (p=0.43)]. Conclusion: VMAT plan robustness is comparable to IMRT plan robustness. The plan robustness conclusions from WCA and DVHB are DVH parameter dependent. On the other hand DDVH captures the overall effect of uncertainties on the dose to a volume of interest. NIH/NCI K25CA168984; Eagles Cancer Research Career Development; The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research Mayo ASU Seed Grant; The Kemper Marley Foundation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

SU-E-T-642: PTV Is the Voxel-Wise Worst-Case of CTV in Prostate Photon Therapy

Purpose: To examine the adequacy of the planning target volume (PTV) dose distribution as the worst-case representation of clinical target volume (CTV) dose distribution in prostate volumetric-modulated arc therapy (VMAT) plans. Methods: Ten intact prostate cancer cases treated by VMAT at our institution were randomly selected. Isocenter was shifted in the three cardinal directions by a displacement equal to the PTV expansion on the CTV (±3 mm) for a total of six shifted plans per original plan. Rotationally-perturbed plans were generated with a couch rotation of ±1° to simulate patient yaw. The eight perturbed dose distributions were recalculated in the treatment planning system using the same, fixed fluence map as the original plan. The voxel-wise worst-case CTV dose distribution was constructed from the minimum value per voxel from the eight perturbed doses. The resulting dose volume histograms (DVH) were evaluated for statistical correlation between the worst-case CTV and nominal PTV dose distributions based on D95% by Wilcoxon signed-rank test with significance level p ≤ 0.05. Results: Inspection demonstrates the PTV DVH in the nominal dose distribution is bounded by the CTV DVH in the worst-case dose distribution. Comparison of D95% for the two dose distributions by Wilcoxon signed-rank test gives p = 0.131. Therefore the null hypothesis cannot be rejected since the difference in median values is not statistically significant. Conclusion: The assumption that the nominal dose distribution for PTV represents the worst-case dose distribution for CTV appears valid for the ten plans under examination. Although the worst-case dose distribution is unphysical since the dose per voxel is chosen independently, it serves as a lower bound for the possible CTV coverage. Furthermore, this is consistent with the unphysical nature of the PTV. Minor discrepancies between the two dose distributions are expected since the dose cloud is not strictly static. Funding Support: NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

SU-F-BRD-01: A Novel 4D Robust Optimization Mitigates Interplay Effect in Intensity-Modulated Proton Therapy for Lung Cancer

Purpose: To compare the impact of interplay effect on 3D and 4D robustly optimized intensity-modulated proton therapy (IMPT) plans to treat lung cancer. Methods: Two IMPT plans were created for 11 non-small-cell-lung-cancer cases with 6–14 mm spots. 3D robust optimization generated plans on average CTs with the internal gross tumor volume density overridden to deliver 66 CGyE in 33 fractions to the internal target volume (ITV). 4D robust optimization generated plans on 4D CTs with the delivery of prescribed dose to the clinical target volume (CTV). In 4D optimization, the CTV of individual 4D CT phases received non-uniform doses to achieve a uniform cumulative dose. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Patient anatomy voxels were mapped from phase to phase via deformable image registration to score doses. Indices from dose-volume histograms were used to compare target coverage, dose homogeneity, and normal-tissue sparing. DVH indices were compared using Wilcoxon test. Results: Given the presence of interplay effect, 4D robust optimization produced IMPT plans with better target coverage and homogeneity, but slightly worse normal tissue sparing compared to 3D robust optimization (unit: Gy) [D95% ITV: 63.5 vs 62.0 (p=0.014), D5% - D95% ITV: 6.2 vs 7.3 (p=0.37), D1% spinal cord: 29.0 vs 29.5 (p=0.52), Dmean total lung: 14.8 vs 14.5 (p=0.12), D33% esophagus: 33.6 vs 33.1 (p=0.28)]. The improvement of target coverage (D95%,4D – D95%,3D) was related to the ratio RMA3/(TVx10−4), with RMA and TV being respiratory motion amplitude (RMA) and tumor volume (TV), respectively. Peak benefit was observed at ratios between 2 and 10. This corresponds to 125 – 625 cm3 TV with 0.5-cm RMA. Conclusion: 4D optimization produced more interplay-effect-resistant plans compared to 3D optimization. It is most effective when respiratory motion is modest compared to TV. NIH/NCI K25CA168984; Eagles Cancer Research Career Development; The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research; Mayo ASU Seed Grant; The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗