ABSTRACT

Introduction: High radiation doses to the heart have been correlated with poor overall survival in patients receiving radiotherapy for stage III non-small cell lung cancer (NSCLC). We built a knowledge-based planning (KBP) tool to limit dose to the heart during creation of volumetric modulated arc therapy (VMAT) treatment plans for patients being treated to 60 Gy in 30 fractions for stage III NSCLC.

Methods: A previous study at our institution retrospectively delineated intracardiac volumes and optimized VMAT treatment plans to reduce dose to these substructures and to the whole heart. Two RapidPlan (RP) KBP models were built from this cohort, one model using the clinical plans and a separate model using the cardiac-optimized plans. Using target volumes and six organs-at-risk (OARs), models were trained to generate treatment plans in a semi-automated process. The cardiac-sparing KBP model was tested in the same cohort used for training and both models were tested on an external validation cohort of 30 patients.

Results: Both RP models produced clinically acceptable plans in terms of target coverage, dose uniformity, and dose to OARs. As compared to the previously created cardiac-optimized plans, cardiac-sparing RPs showed significant reductions in mean dose to the esophagus and lungs while performing similar or better in all evaluated heart dose metrics. When comparing between the two models, the cardiac-sparing RP showed reduced (p<0.05) heart mean and max doses, as well as volumes receiving 60 Gy, 50 Gy, and 30 Gy.

Conclusions: By using a set of cardiac-optimized treatment plans for training, the proposed KBP model provides a means to reduce dose to the heart and its substructures without the need to explicitly delineate cardiac substructures. This tool offers reduced planning time, improved plan quality, and can be used to drive patient outcomes.

KeywordsINTRODUCTION

Radiation therapy to the thorax is correlated with cardiovascular toxicity in patients treated for lymphoma1, breast cancer2, esophageal cancer3, and lung cancer4,5. Although radiation-induced congestive heart failure or myocardial infarction can take years to manifest6,7, acute toxicities, such as pericarditis, have also been found among patients receiving high cardiac doses8. In the NRG Oncology RTOG 0617 radiation dose escalation trial, increased cardiac dose was associated with poorer survival outcomes in the high dose arm9. Specifically, the cardiac volumes receiving 5 Gy and 30 Gy (V5 and V30, respectively) were associated with higher death rates. Other studies have shown increased max dose to the heart correlates to higher death rates as soon as six months after treatment10,11. There has been a recent focus in investigating radiation dose to specific cardiac substructures12-14. McWilliam et al. found that max dose to a cardiac region consisting of the right atrium, right coronary artery, and ascending aorta had the greatest impact on survival compared to mean and max dose to the whole heart, as well as other regions of the heart15. In a pre-clinical murine model study, Ghita et al. found that the base of the heart was more radiosensitive than the middle or apex, and that whole heart dosimetric parameters did not predict physiological changes from irradiation of sub-volumes of the heart16.

When cardiac substructures are contoured at the treatment planning stage, radiation treatment plans can be designed to limit dose to these structures17. However, the task of delineating cardiac substructures can be time consuming and it is not typically clinically feasible to optimize treatment plans to limit dose to cardiac substructures. Another way to translate substructure dose reduction to all cases is to use knowledge-based planning (KBP) to incorporate geometric and dose information from a set of treatment plans to drive the optimization process for new cases. KBP can be used to estimate 3D dose distributions or dose-volume histograms (DVH)18. KBP has been widely implemented across many different disease sites and allows for a means of partially automating the treatment planning process and reducing variability in plan quality19. In the setting of lung irradiation, KBP studies have shown improvements in V5, V20, and mean lung dose20. Other KBP models have been used to incorporate more information to the treatment planning process, such as training a model with functional lung volumes to allow for more patient-specific optimization21. Similar to the study by Faught et al21, where the authors took a set of highly curated plans with lung functional avoidance to build a model, in this study, we train a KBP model for thoracic irradiation using a subset of treatment plans which were optimized to reduce dose to the heart and substructures of the heart. The purpose of this study is to determine if using a KBP model trained with cardiac-sparing lung radiotherapy plans can reduce cardiac dose without compromising target coverage or increasing doses to other organs-at-risk (OARs). Furthermore, using a subset of validation cases, we test the model's ability to reduce dose to cardiac substructures in a separate patient cohort without substructure delineation.

METHODS

To generate plans for model input, clinical treatment plan data were retrospectively collected for 31 patients treated to a standard regimen of 60 Gy in 30 fractions for stage III non-small cell lung cancer (NSCLC) at our institution. Approval for this retrospective study was obtained by our internal review board. This cohort of patients all have two pre-existing treatment plans. The first group of plans are the clinically-used plans which were planned by our dosimetry team and approved by the treating radiation oncologist. In addition to the clinical plans, our dosimetry team retrospectively generated a cardiac-optimized plan for each patient. To generate the cardiac-optimized plans, 15 cardiac substructures were delineated on free-breathing CT scans taken acquired at the time of CT simulation. Substructures delineated were the left and right atria and ventricles (LA, RA, LV, RV), coronary arteries [left anterior descending (LAD), left circumflex (LCFLX), left main coronary artery (LMCA), and right coronary artery (RCA)], ascending aorta (AA), pulmonary artery (PA), superior vena cava (SVC), and valves of the heart [atrial (AV), mitral (MV), pulmonary (PV), and tricuspid (TV)].

Contours were transferred via rigid registration to the averaged 4DCT for dose calculation. Volumetric modulated arc therapy (VMAT) treatment plans using 2-3 arcs were optimized to meet clinical DVH constraints for the heart, lungs, esophagus, and spinal cord. Additionally, in the re-optimized cardiac-sparing plans, dose to cardiac substructures was reduced according to as low as reasonably achievable (ALARA) principles. The differences between the clinical plans and the cardiac-optimized plans will be discussed in more detail below, and full dosimetric comparisons have been previously reported17.

RapidPlan (RP) is a model-based KBP module integrated within the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA). It extracts treatment planning knowledge embedded in prior treatment plans by establishing correlations between plan DVHs and patient anatomy/beam geometry features22. Once trained, an RP model can generate OAR DVH estimates for a future patient based on structure contours and beam placements. During training, input structures are decomposed into four functional subregions (out-of-field, MLC transmission, overlap, and in-field) which are modeled separately to reproduce input DVHs. A means-and-standard deviation models is used for low-dose modeling in the out-of-field and MLC transmission regions. The overlap subregion contains voxels that lie in both the target and an OAR. These voxels are assumed to receive full prescription dose. The in-field subregion contains voxels that lie in the beam's-eye-view but not within the target volume, and this portion of the DVH is modeled by applying principal component analysis to the known in-field DVH and a geometry-based expected dose (GED) histogram, which is analogous to a distance between a voxel and the target volume. Stepwise multiple regression is used to fit principal component scores of the DVH to a set of anatomical features, including GED principal scores, PTV volume, OAR volume, OAR/PTV overlap, and percentage of the OAR that is outside the treatment fields. For a new patient, DVHs of the four subregions are estimated and combined to form the whole OAR DVH. In addition to producing DVH estimates, RP allows users to define optimization constraint templates with constraints based on DVH predictions.

Two models were trained for this study. The proposed cardiac-sparing model used the cardiac-optimized treatment plans as input. Input structures were the clinical target volume (CTV), planning target volume (PTV), and eight OAR structures: heart, lungs (ipsilateral, contralateral, whole lungs, and lungs cropped out of CTV), esophagus, spinal cord, and a spinal cord planning-risk-volume (PRV) based on a 5 mm expansion. In addition to the line constraints generated by RP for optimization, specific DVH dose constraints were added to the optimization template in order to meet certain standard clinical metrics, such as the volume of lungs receiving 20 Gy (V20). The full optimization template can be found in Table S1.

RP was used to retrospectively replan cases for the 31 patients used to build the model, comparing model inputs directly to model outputs. For the rest of this manuscript we will refer to this set of plans as the training cohort. When using RP to generate models, optimization objectives were not modified in any way as optimization progressed. RP-generated plans were compared only to the cardiac sparing VMAT plans since our previous study showed that these plans improved dose metrics, as compared to the clinical plans, for all thoracic OARs in addition to the heart17. Beam parameters such as arc length, collimator angle, and energy were held constant between all initial plans and replans. Patients were treated on either Varian Trilogy or TrueBeam with 6 MV beams.

The cardiac-sparing RP model was then used to retrospectively generate plans in a separate cohort of 30 patients treated for stage III NSCLC. These patients will be referred to as the validation cohort throughout the rest of this manuscript, and they were all treated to 60 Gy in 30 fractions and had target volumes near or overlapping the heart. PTV sizes were similar between the two groups, with means of 442.5±254.5 cm3 in the training cohort and 460.2±246.7 cm3 in the validation cohort.

To further test the cardiac-sparing model, a second independent RP model was trained using the clinical plans from the training cohort, which we call the clinical RP model. The same optimization objective template was paired with DVH estimates from this model and plans were re-generated for the validation cohort. This allowed us to evaluate if changes in plan quality are due to the input data to the cardiac-optimized RP model or the optimization objective template used alongside the DVH estimates. As an example of the difference in model inputs, Fig. S1 shows the mean input heart DVH for both models. Further dosimetric differences between the input plan models can be seen in Table S2.

To investigate dose changes to the cardiac substructures in addition to whole heart for the validation cohort, we retrospectively delineated the cardiac substructures of 22 patients. Max and mean doses to these structures were evaluated for the original plan, the clinical RP, and the cardiac-sparing RP.

Paired, two-tailed t-tests were applied to resulting DVH metrics, comparing the cardiac-sparing RP first to the clinical plans and then to the clinical RP. Statistical significance was assessed at the 95% confidence level (p-value < 0.05). All dose calculations were carried out in Eclipse using the Anisotropic Analytical Algorithm (version 15.6.05). When generating plans with the RP models, optimization was initialized using the DVH estimation tool and then proceeded without intervention. Arc parameters, including numbers of arcs and control points were held constant when re-planning using RP. For the final evaluation, all plans were normalized so that prescription dose covered 95% of the PTV volume.

RESULTS Training CohortDose distributions from five patients in the training cohort are shown in Fig. 1. These patients were chosen to show how the RP model performs on the most challenging cases in the dataset. Table 1 summarizes quantitative results for all patients in the training cohort. As shown in Fig. 1, the RP model produces plans with similar dose distributions to the model input plans. The data in Table 1 show that there are significant reductions in the volume of heart receiving prescription dose, the mean lung dose, and V20 in the lungs. The only dose evaluation metric in which the RP is significantly (p

Figure 1Dose distributions from five patients with the highest mean heart dose from the training cohort. The clinical plans are the original plans for treatment, the cardiac optimized plans are the RP model input, and the cardiac-sparing RPs were generated using the model proposed in this work. Each column shows three plans from one patient and all images correspond to the same CT slice. The PTV is shown in red, heart in pink, spinal cord in cyan, and esophagus in blue. The dose color wash ranges from 30 Gy (blue) to 66 Gy (red).

Table 1Selected DVH metrics for the PTV and OARs for patients in the training cohort. Mean ± standard deviations are shown from the 31 plans from the patient cohort. Bold text indicates statistically significantly improved performance with the cardiac-sparing RP model (p<0.05).

 Validation CohortDose distributions from five patients in the validation cohort are shown in Fig. 2. Both the clinical and cardiac-sparing RP models produced more conformal plans and both allow for cardiac sparing. Average quantitative results from all patients in the validation cohort are shown in Table 2. While both RP models showed improvements over the clinical plan, the cardiac-sparing model showed statistically significant (p

Figure 2Dose distributions from five patients with the highest mean heart dose from the validation cohort. The first row shows dose from the plans that were used for treatment, the second row shows dose from plans generated with the clinical RP model, and the final row shows dose from plans generated with the cardiac-sparing RP model. Each column shows the three plans from one patient and all images correspond to the same CT slice. The PTV is shown in red, heart in pink, spinal cord in cyan, and esophagus in blue. The dose color wash ranges from 30 Gy (blue) to 66 Gy (red).

Table 2Selected DVH metrics from the validation cohort. Mean ± standard deviations are shown from the 30 plans from the validation cohort. All p-values are compared to the cardiac-sparing RP, and bolded text indicates statistically significant differences between the cardiac-sparing RP and the comparison plan (p<0.05).

Fig. 3 shows both RP models improved upon the clinical plans at all dose levels in the heart and for doses below 35 Gy in the lungs. The cardiac-sparing RP showed a lower mean cardiac DVH than the clinical RP at all dose levels between 0 and 66 Gy, with the largest benefit being in the 10-25 Gy range. While both models produced smaller low-dose volumes in the lungs, the clinical plans reduced volumes receiving doses greater than 45 Gy. Additionally, inset (d) shows that while the clinical RP and cardiac-sparing RPs performed similarly in the lungs, the clinical RP mean DVH was lower by almost as much as 0.5%, with differences being largest in between the 20 and 40 Gy levels.

Figure 3Mean heart (a) and lung (b) DVHs over all patients in the validation cohort, with shaded regions showing plus/minus one standard deviation for each dataset. Insets (c) and (d) show differences in the DVHs between all three plan types.

 Substructure ValidationFinally, as mentioned in the methods section, one of the secondary endpoints of this study is to encode the information provided by the substructure contours and subsequent optimization of the cardiac sparing plans into the RP model. Table 3 shows the resulting mean and max dose for cardiac substructures grouped together by type, evaluated in 22 patients from the validation cohort. Individual substructure dose metrics can be seen in Table S3. While the cardiac-sparing RP does not statistically significantly outperform the clinical RP in all metrics, it produces lower max doses for all grouped substructures that lie within the heart (chambers, coronary arteries, and valves).

Table 3Maximum and mean doses in Gy for all cardiac substructures. Mean ± standard deviations are calculated from the 22 patient datasets in the validation cohort with cardiac substructure contours. All p-values are compared to the cardiac-sparing RP, and bolded text indicates statistically significant differences between the cardiac-sparing RP and the comparison plan (p<0.05).

DISCUSSION

Radiation-induced heart disease (RIHD) remains a post-treatment toxicity that needs further study, however recent literature has supported the hypothesis that the incidence of RIHD increases with radiation dose, possibly with no threshold2,23. In addition to influencing post-treatment toxicities, radiation dose to the heart can lead to decreased patient activity levels during the course of radiation therapy24. While recent studies have demonstrated correlation between cardiac toxicities and dose to specific cardiac substructures15,25, a full understanding of the relationship between dose to these substructures and specific toxicities is lacking. Given this lack of data for toxicity analysis for cardiac substructures, it is reasonable to follow ALARA principles when creating radiotherapy treatment plans with the heart in the radiation field, especially since survival in stage III NSCLC patients continues to increase26.

One advantage of the proposed method is the ability to spare cardiac substructures without the need for substructure contours. While improvements over the clinical RP in Table 3 were not all statistically significant, the improvements from the clinical plans to the cardiac-sparing RP were large, reductions in mean dose of 1.4 Gy, 3.3 Gy, and 3.0 Gy to the chambers, coronary arteries, and valves, respectively. On average, it can take 1-2 hours to delineate cardiac substructures, and optimization using these substructures can add up to an hour to the treatment planning process. By implementing the proposed method, we were able to save these 2-3 hours per plan and maintain plan quality while seeing reduction in cardiac and substructure doses.

The RP approach allows for hands-free optimization, however, it does have some limitations. Since the optimization is not actively monitored, there are certain OARs that are not pushed to the lowest dose possible. For example, the max dose to the spinal cord is close to 30 Gy for all RPs even though it may have been pushed to below 20 Gy in the original clinical plans. This may also be due to RP's approach to DVH estimation which relies on principal component analysis (PCA). Since the max dose is typically the only metric of interest for the cord, treatment planners typically restrict the max dose and do not limit the entire DVH curve. Therefore, larger variability is expected in cord DVHs. As a result, spinal cord predictions likely were not as accurate as other organs, and when the prediction is higher, the final dose will be higher.

There is some tradeoff necessary to achieve the cardiac sparing shown in this study. In the validation study, the clinical RP model significantly outperformed the cardiac-sparing RP model in esophagus mean dose, lung mean dose, and lung V20. The dosimetric improvements for both RP models also came at the cost of an increase in monitor units MU, 489±78 original vs 566±79 for the cardiac-sparing RP (p<0.001) and 569±76 for the clinical RP (p<0.001). This finding is consistent with that of Tahmbe et al who found that for their lung KBP model, re-optimized plans statistically significantly increased plan complexity in both MU and MU/degree20. Tahmbe et al further found that this increased complexity did not impact plan deliverability.

A limitation of this study design is that all plans used for model training were developed as part of a previous retrospective study. A single observer delineated all thoracic OARs and a two dosimetrists generated all treatment plans with a specific list of optimization goals. All plans were generated within one institution and the sample size is small. Further validation using multi-institutional prospective data is necessary to validate the findings of this model and to account for differences between planners.

The nature of the model input plans also mean the input dataset for model training can be expected to show less variation than a standard clinical dataset. While the sample size for this study was small, Fogliata et al previously found that an RP model for thoracic radiation therapy could be adequately trained with 27 patients, similar to the 31 used in this study27. They found that for the heart DVH, more than 99% of cases were reproduced in the DVH or GED components with an average reduced chi-square value of 1.20 in their study. DVH modeling of the heart in the proposed model also produced a reduced chi-square of 1.20.

CONCLUSIONS

The KBP model proposed in this work is applied to patients receiving 60 Gy in 2 Gy fractions for stage III NSCLC. The model preserves the cardiac sparing that was shown in a previous study without the need for contouring intracardiac structures17. The proposed model allows for reduction in cardiac dose without compromising target coverage or increasing dose to other thoracic OARs. Additionally, the model is able to capture dosimetric information about cardiac substructures which allow for decreased dose to these substructures even if they are not delineated as part of the treatment planning process. Use of this model allows for semi-automated treatment planning in a cohort of patients that are typically difficult to plan, and the reduction in heart dose achieved by the model could potentially limit future toxicities.

Summary

Increased cardiac radiation dose has been correlated with poor overall survival in patients receiving radiotherapy. With volumetric modulated arc therapy, dose to cardiac substructures can be minimized. However, optimizing plans based on these substructures is not feasible in most clinics. To solve this problem, we trained a knowledge-based planning model to reduce radiation dose to the heart and its substructures. This model allows for decreased cardiac and substructure dose while reducing planning time.

References1

Ng AK. Review of the cardiac long-term effects of therapy for Hodgkin lymphoma. Br J Haematol. 2011;154(1):23-31.

2

Darby SC, Ewertz M, McGale P, et al. Risk of Ischemic Heart Disease in Women after Radiotherapy for Breast Cancer. N Engl J Med. 2013;368(11):987-998.

3

Beukema JC, van Luijk P, Widder J, Langendijk JA, Muijs CT. Is cardiac toxicity a relevant issue in the radiation treatment of esophageal cancer? Radiother Oncol. 2015;114(1):85-90.

4

Douillard J-Y, Rosell R, De Lena M, Riggi M, Hurteloup P, Mahe M-A. Impact of Postoperative Radiation Therapy on Survival in Patients With Complete Resection and Stage I, II, or IIIA Non–Small-Cell Lung Cancer Treated With Adjuvant Chemotherapy: The Adjuvant Navelbine International Trialist Association (ANITA) Randomized Trial. Int J Radiat Oncol Biol Phys. 2008;72(3):695-701.

5

Ferris MJ, Jiang R, Behera M, Ramalingam SS, Curran WJ, Higgins KA. Radiation Therapy Is Associated With an Increased Incidence of Cardiac Events in Patients with Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys. 2018;102(2):383-390.

6

Darby SC, McGale P, Taylor CW, Peto R. Long-term mortality from heart disease and lung cancer after radiotherapy for early breast cancer: prospective cohort study of about 300 000 women in US SEER cancer registries. Lancet Oncol. 2005;6(8):557-565.

7

van Nimwegen FA, Schaapveld M, Janus CPM, et al. Cardiovascular Disease After Hodgkin Lymphoma Treatment: 40-Year Disease Risk. JAMA Intern Med. 2015;175(6):1007-1017.

8

Darby SC, Cutter DJ, Boerma M, et al. Radiation-Related Heart Disease: Current Knowledge and Future Prospects. Int J Radiat Oncol Biol Phys. 2010;76(3):656-665.

9

Bradley JD, Paulus R, Komaki R, et al. Standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage IIIA or IIIB non-small-cell lung cancer (RTOG 0617): a randomised, two-by-two factorial phase 3 study. Lancet Oncol. 2015;16(2):187-199.

10

Vivekanandan S, Landau DB, Counsell N, et al. The Impact of Cardiac Radiation Dosimetry on Survival After Radiation Therapy for Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys. 2017;99(1):51-60.

11

Haq R, Hotca A, Apte A, Rimner A, Deasy JO, Thor M. Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis. Phys Imag Radiat Oncol. 2020;14:61-66.

12

van den Bogaard VAB, Ta BDP, van der Schaaf A, et al. Validation and Modification of a Prediction Model for Acute Cardiac Events in Patients With Breast Cancer Treated With Radiotherapy Based on Three-Dimensional Dose Distributions to Cardiac Substructures [published online ahead of print 2017/01/17]. J Clin Oncol. 2017;35(11):1171-1178.

13

Patel SA, Mahmood S, Nguyen T, et al. Comparing Whole Heart Versus Coronary Artery Dosimetry in Predicting the Risk of Cardiac Toxicity Following Breast Radiation Therapy. Int J Radiat Oncol Biol Phys. 2018;102(3, Supplement):S46.

14

Abouegylah M, Braunstein LZ, Alm El-Din MA, et al. Evaluation of radiation-induced cardiac toxicity in breast cancer patients treated with Trastuzumab-based chemotherapy. Breast Cancer Res Treat. 2019;174(1):179-185.

15

McWilliam A, Khalifa J, Vasquez Osorio E, et al. Novel methodology to investigate the impact of radiation dose to heart sub-structures on overall survival. Int J Radiat Oncol Biol Phys. 2020.

16

Ghita M, Gill EK, Walls GM, et al. Cardiac sub-volume targeting demonstrates regional radiosensitivity in the mouse heart. Radiother Oncol. 2020.

17

[REDACTED]

18

Ge Y, Wu QJ. Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches. Med Phys. 2019;46(6):2760-2775.

19

Moore KL, Appenzoller LM, Tan J, Michalski JM, Thorstad WL, Mutic S. Clinical implementation of dose-volume histogram predictions for organs-at-risk in IMRT planning. Journal of Physics: Conference Series. 2014;489:012055.

20

Tambe NS, Pires IM, Moore C, Cawthorne C, Beavis AW. Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy. Br J Radiol. 2020;93(1106):20190535.

21

Faught AM, Olsen L, Schubert L, et al. Functional-guided radiotherapy using knowledge-based planning. Radiother Oncol. 2018;129(3):494-498.

22

Systems VM. Elcipse Photon and Electron Algorithms Reference Guide. Vol P1020505-003-C: Varian Medical Systems, Inc.; 2018.

23

Wang K, Eblan MJ, Deal AM, et al. Cardiac Toxicity After Radiotherapy for Stage III Non-Small-Cell Lung Cancer: Pooled Analysis of Dose-Escalation Trials Delivering 70 to 90 Gy. J Clin Oncol. 2017;35(13):1387-1394.

24

Paul S, Bodner WR, Garg M, Tang J, Ohri N. Cardiac Irradiation Predicts Activity Decline in Patients Receiving Concurrent Chemoradiation for Locally Advanced Lung Cancer. Int J Radiat Oncol Biol Phys. 2020;108(3):597-601.

25

Atkins KM, Chaunzwa TL, Lamba N, et al. Association of Left Anterior Descending Coronary Artery Radiation Dose With Major Adverse Cardiac Events and Mortality in Patients With Non–Small Cell Lung Cancer. JAMA Oncol. 2021;7(2):206-219.

26

Faivre-Finn C, Vicente D, Kurata T, et al. LBA49 Durvalumab after chemoradiotherapy in stage III NSCLC: 4-year survival update from the phase III PACIFIC trial. Ann Oncol. 2020;31:S1178-S1179.

27

Fogliata A, Belosi F, Clivio A, et al. On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiother Oncol. 2014;113(3):385-391.

Appendix. Supplementary materialsArticle InfoPublication History

Accepted: June 11, 2021

Received in revised form: June 1, 2021

Received: March 26, 2021

Publication stageIn Press Journal Pre-ProofFootnotes

Author Responsible for Statistical Analysis: Joseph Harms

Conflict of Interest Statement: Dr. Higgins reports grants from RefleXion, personal fees from Astra Zeneca, personal fees from Genetech, personal fees from Presica, and personal fees from Varian, outside the submitted work.

Funding: None

Data sharing statement: Research data are not available at this time.

Identification

DOI: https://doi.org/10.1016/j.adro.2021.100745

Copyright

© 2021 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology.

User License Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) | How you can reuse Permitted For non-commercial purposes: Read, print & download Redistribute or republish the final article Text & data mine Translate the article (private use only, not for distribution) Reuse portions or extracts from the article in other worksNot PermittedSell or re-use for commercial purposes Distribute translations or adaptations of the article
Elsevier's open access license policy ScienceDirectAccess this article on ScienceDirect Related Articles