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A Learning-Based Approach to Derive Electron Density from Anatomical MRI for Radiation Therapy Treatment Planning
Volume 99 Number 2S Supplement 2017 17% in the FSRT alone group (P Z 0.024). The Kaplan-Meier estimate of 12-month local control was 71% in the S+SRS group as compared to 67% in the FSRT alone group (P Z 0.789). Conclusion: Although many patients need surgical resection of a brain metastasis due to mass effect, it appears that S+SRS may increase the risk of LMD as compared to FSRT alone. As the two treatment strategies seem to have similar LC, FSRT appears to be a viable alternative to S+SRS in selected patients with large brain metastases. Author Disclosure: S. Marcrom: I serve on the executive committee of ARRO, and we work to improve the training process for Radiation Oncology Residents; ARRO. P.M. Foreman: None. A. McDonald: None. K. Riley: None. B.L. Guthrie: None. J.M. Markert: None. C.D. Willey: Research Grant; American Cancer Society, NIH. Consultant; North, Pursell & Ramos, PLC, Varian Medical Systems. M. Bredel: None. J.B. Fiveash: Research Grant; Varian Research Contract. Honoraria; Varian Research Contract. Travel Expenses; Varian Research Contract.
1008 Surgical Resection and Posterior Fossa Location Increase the Incidence of Leptomeningeal Disease in Patients Treated with Stereotactic Radiosurgery for Brain Metastases R. Katipally,1 P.P. Koffer,1,2 P.S. Rava,3 D. Cielo,4 S.A. Toms,4 T.A. DiPetrillo,1,2 and J.T. Hepel1,2; 1Radiation Oncology, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI, 2 Radiation Oncology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, 3Department of Radiation Oncology, University of Massachusetts Memorial Medical Center, Worcester, MA, 4Neurosurgery, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI Purpose/Objective(s): Stereotactic radiosurgery (SRS), with or without prior surgical resection, has emerged as the primary and most efficacious alternative to whole-brain radiation therapy (WBRT) in the treatment of brain metastases. Leptomeningeal disease (LMD) is a severe, but rare, occurrence following local therapy. Growing evidence suggests an increased risk of LMD in the setting of prior surgical resection and posterior fossa location. The purpose of this study is to assess the factors predicting greater risk of LMD in a large cohort of consecutively treated patients who had not received WBRT. Materials/Methods: Three hundred six patients treated with single-fraction SRS without upfront WBRT at a single institution between 2001 and 2013 were identified. Ninety-five (31%) patients had prior surgical resection. One hundred and sixteen (38%) patients had metastatic disease located in the posterior fossa. LMD was determined by chart and imaging review. A logistic regression model was constructed to assess the association of prior surgical resection, posterior fossa location, size of largest metastasis, number of metastases, and type of primary with the incidence of LMD. Results: With a median follow-up time of 9.7 months, LMD occurred in 31 (10%) patients at a median time of 12.3 months after SRS. Median overall survival after developing LMD was 2.3 months. A greater incidence of LMD was present for patients who underwent surgical resection (16.8%) compared to those treated with SRS alone (7.1%) (P Z 0.009). The incidence in patients with posterior fossa metastases was 12.9% vs. 8.4% in patients with only supratentorial disease (P Z 0.2). Upon evaluating the influence of histology, patients with breast cancers had a LMD rate of 27.8% vs. 7.8% when comparing to patients with all other primary sites (P < 0.001). In the logistic regression model, prior surgical resection (P Z 0.044) and breast primary (P Z 0.002) were both associated with statistically significant odds ratios (ORs), 2.59 and 4.14, respectively, for developing LMD. A posterior fossa metastasis was associated with an OR of 2.18 (P Z 0.076). LMD risk did not correlate with either the number of metastases or size of largest metastasis. When repeating the analysis in only patients with posterior fossa metastases, prior surgical resection was an even stronger
ePoster Sessions S173 predictor of LMD (OR Z 5.88, P Z 0.009). Breast was excluded from subset analysis due to insufficient number of cases. Conclusion: Prior surgical resection and breast cancer primary place patients at increased risk of developing LMD. Surgical resection is an even greater risk factor for patients with posterior fossa metastases. Further investigation is necessary to determine whether SRS alone, cavity-directed SRS, or surgical resection followed by WBRT is most appropriate in this patient group. Author Disclosure: R. Katipally: None. P.P. Koffer: None. P.S. Rava: None. D. Cielo: None. S.A. Toms: None. T.A. DiPetrillo: Patent/License Fees/Copyright; Thomas DiPetrillo. J.T. Hepel; ABS Annual Meeting 2015, ACRO Accreditation.
1009 A Learning-Based Approach to Derive Electron Density from Anatomical MRI for Radiation Therapy Treatment Planning X. Yang,1 Y. Lei,1 H.K.G. Shu,2 P.J. Rossi,1 H. Mao,3 H. Shim,2 W.J. Curran Jr,2 and T. Liu2; 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 2Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, 3Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA Purpose/Objective(s): The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiation therapy due to its superior soft tissue contrast as compared with CT. A treatment planning process with MRI as the sole imaging modality could eliminate systematic MR-CT co-registration errors, reduce medical cost, spare the patient from CT x-ray exposure, and simplify clinical workflow. However, MRI data do not contain electron density information that is necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a learningbased method to derive electron density from routine anatomical MRI for MRI-only radiotherapy treatment planning. Materials/Methods: We initially build a set of paired training MRI and CT images with the CT images serving as the regression target of the MRI. Then we perform a preprocessing by removing uninformative regions and reducing noise, followed by aligning MRI and CT images. The prediction of CT images consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative MR-CT features are identified by feature selection to train a random forest. During the prediction stage, we extract the selected features from the new (target) MR image and feed them into the well-trained forests for the CT image prediction. This prediction technique was tested with brain MR and CT images of 10 patients. We performed leave-one-out cross-validation method to evaluate the proposed CT prediction algorithm. Results: Our predicted CT images generated from MRI were compared with the original CT images. In order to get a quantitative evaluation, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. Overall the mean MAE, PSNR, and FSIM were 16.26 2.64, 33.57 0.66, and 0.82 0.03 for 10 patients’ data, which demonstrated the CT prediction accuracy of the proposed learning-based method. Conclusion: We have investigated a novel learning-based approach to generate CT images from routine MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. We have demonstrated that the reported method is capable of reliably predicting CT images from the MR images. This CT image prediction technique could be a useful tool for MRI-based radiation treatment planning or attenuation correction for quantifying PET images when using a PET/MRI scanner.
S174 Author Disclosure: X. Yang: None. Y. Lei: None. H. Shu: None. P.J. Rossi: None. H. Mao: None. H. Shim: None. W.J. Curran: None. T. Liu: None.
1010 Using Machine Learning to Predict Physician-Approved Dose Distributions for Pancreatic SBRT W. Campbell,1 L.A. Olsen,2 M. Miften,1 K.A. Goodman,1 T. Schefter,1 and B.L. Jones1; 1Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, 2Memorial Hospital e UCHealth, Colorado Springs, CO Purpose/Objective(s): Stereotactic body radiation therapy (SBRT) is emerging as an attractive treatment option for patients with advanced pancreatic cancer. However, retrospective analysis of the RTOG-9704 phase III trial recently revealed that failure to adhere to radiation therapy protocols was significantly associated with reduced median survival. This suggests that ensuring high quality radiation therapy is crucial, and that future pancreatic radiation therapy trials should include more robust methods for providing patient-specific plan quality validation. This study trains artificial neural network dose models (ANN-DMs) for pancreatic SBRT, and validates their ability to predict patient-specific dose distributions similar to physician-approved treatment plans. Materials/Methods: Arc-based SBRT treatment plans for 49 pancreatic cancer patients were prepared, delivering 30-33 Gy in five fractions. Treatments were overseen by one of two physicians, each with their own treatment protocol including dosage prescribed, volumes treated, and primary organs-at-risk (OARs). Physician-approved treatment plans were used to train artificial neural network dose models (ANN-DMs) that could predict physician-approved dose distributions based on a set of geometric and plan parameters. For each individual voxel, these parameters were used as inputs to a neural network, and a single output of dose was compared against that voxel’s dose, as calculated by the treatment planning system. Patient datasets were randomly allocated, with roughly 2/3rds used for training and 1/3rd used for validation. Differences between clinical and ANN-DM dose distributions were used to evaluate model performance. Results: Mean dose errors were less than 5% at all distances from the PTV, and mean absolute dose errors were on the order of 5%, but no more than 10%. Dose-volume histogram errors demonstrated good model performance above 25 Gy, but larger errors were seen at lower doses. Remarkable improvements in ANN-DM accuracy (from >30% to <5% mean absolute dose error) were achieved by training separate dose models for each set of OAR constraints and prescription doses. Following one treatment protocol, 6 plans prepared at another institution were used for model validation without being included in model training and no significant increases in model errors were observed. Conclusion: Dose distributions predicted by trained pancreatic SBRT ANN-DMs showed excellent overall agreement with physician-approved dose distributions, and predictive accuracy was substantially improved by developing separate neural network models for differing treatment protocols. Model accuracy for a given treatment protocol was also maintained for patients planned and treated at another institution. For future largescale trials of pancreatic SBRT, such a model could be trained using consensus guidelines for high-quality plans, allowing for patient-specific plan quality validation in a multi-institutional setting. Author Disclosure: W. Campbell: Research Grant; Varian Medical Systems. L.A. Olsen: None. M. Miften: Research Grant; Varian Medical Systems. K.A. Goodman: None. T. Schefter: Honoraria; Sirtex. Travel Expenses; Sirtex. B.L. Jones: Research Grant; Varian Medical Systems.
1011 Knowledge Engineering-Based Quality Evaluation of NRG Oncology RTOG 0522 Treatment Plans H. Geng,1 T.G. Giaddui,1 H. Zhong,2 D.I. Rosenthal,3 J.M. Galvin,4 Y. Xiao,2 and N. Linnemann5; 1University of Pennsylvania, Philadelphia, PA,
International Journal of Radiation Oncology Biology Physics 2
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 3MD Anderson Head and Neck Cancer Symptom Working Group, The University of Texas MD Anderson Cancer Center, Houston, TX, 4IROC, Philadelphia, PA, 5American College of Radiology, Philadelphia, PA Purpose/Objective(s): To evaluate radiotherapy treatment plan quality of IMRT plans submitted for RTOG 0522 clinical trial (A Randomized Phase III Trial of Concurrent Accelerated Radiation and Cisplatin versus Concurrent Accelerated Radiation, Cisplatin, and Cetuximab (C225) [Followed by Surgery for Selected Patients] for Stage III and IV Head and Neck Carcinomas). To use knowledge engineering based models from RapidPlan (built from HN002) for the quality study, and to re-optimize the plans by the model with the aim of further sparing the organs at risk and improve the quality of these plans in general. Materials/Methods: A head and neck RapidPlan model was initially built in Varian Eclipse treatment planning system (Version 13.6.15) using treatment plans from the NRGHN002 clinical trial (A Randomized Phase II Trial for patients with p16 positive, non-smoking-associated, locoregionally advanced oropharyngeal cancer). The model was duplicated and tuned to be used for QA and re-optimizing treatment plans from RTOG 0522. The modified model was then used to QA and re-optimize plans with deviation unacceptable scores (i.e., failed to pass the dosimetric compliance criteria of RTOG 0522). Results: Initial quality review of 748 IMRT plans submitted to RTOG0522 show that 21 plans did not meet the spinal cord dose constraint (D0.03 cc
1012 Investigation of Organs-at-Risk Contouring Accuracy for MR-Only Radiation Therapy in the Cranial Region C.H. Li,1 W.W.K. Fung,1 and G. Chiu2; 1Hong Kong Sanatorium and Hospital, Happy Valley, Hong Kong, 2Department of Radiotherapy, Ho ng Kong Sanatorium and Hospital, Happy Valley, Hong Kong Purpose/Objective(s): Accurate structure delineation on MR is crucial for MR-only RT planning. This study assessed the accuracy and consistency of OAR contouring on high-resolution 3D isotropic MR compared
1008 Surgical Resection and Posterior Fossa Location Increase the Incidence of Leptomeningeal Disease in Patients Treated with Stereotactic Radiosurgery for Brain Metastases R. Katipally,1 P.P. Koffer,1,2 P.S. Rava,3 D. Cielo,4 S.A. Toms,4 T.A. DiPetrillo,1,2 and J.T. Hepel1,2; 1Radiation Oncology, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI, 2 Radiation Oncology, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, 3Department of Radiation Oncology, University of Massachusetts Memorial Medical Center, Worcester, MA, 4Neurosurgery, Rhode Island Hospital, Alpert Medical School of Brown University, Providence, RI Purpose/Objective(s): Stereotactic radiosurgery (SRS), with or without prior surgical resection, has emerged as the primary and most efficacious alternative to whole-brain radiation therapy (WBRT) in the treatment of brain metastases. Leptomeningeal disease (LMD) is a severe, but rare, occurrence following local therapy. Growing evidence suggests an increased risk of LMD in the setting of prior surgical resection and posterior fossa location. The purpose of this study is to assess the factors predicting greater risk of LMD in a large cohort of consecutively treated patients who had not received WBRT. Materials/Methods: Three hundred six patients treated with single-fraction SRS without upfront WBRT at a single institution between 2001 and 2013 were identified. Ninety-five (31%) patients had prior surgical resection. One hundred and sixteen (38%) patients had metastatic disease located in the posterior fossa. LMD was determined by chart and imaging review. A logistic regression model was constructed to assess the association of prior surgical resection, posterior fossa location, size of largest metastasis, number of metastases, and type of primary with the incidence of LMD. Results: With a median follow-up time of 9.7 months, LMD occurred in 31 (10%) patients at a median time of 12.3 months after SRS. Median overall survival after developing LMD was 2.3 months. A greater incidence of LMD was present for patients who underwent surgical resection (16.8%) compared to those treated with SRS alone (7.1%) (P Z 0.009). The incidence in patients with posterior fossa metastases was 12.9% vs. 8.4% in patients with only supratentorial disease (P Z 0.2). Upon evaluating the influence of histology, patients with breast cancers had a LMD rate of 27.8% vs. 7.8% when comparing to patients with all other primary sites (P < 0.001). In the logistic regression model, prior surgical resection (P Z 0.044) and breast primary (P Z 0.002) were both associated with statistically significant odds ratios (ORs), 2.59 and 4.14, respectively, for developing LMD. A posterior fossa metastasis was associated with an OR of 2.18 (P Z 0.076). LMD risk did not correlate with either the number of metastases or size of largest metastasis. When repeating the analysis in only patients with posterior fossa metastases, prior surgical resection was an even stronger
ePoster Sessions S173 predictor of LMD (OR Z 5.88, P Z 0.009). Breast was excluded from subset analysis due to insufficient number of cases. Conclusion: Prior surgical resection and breast cancer primary place patients at increased risk of developing LMD. Surgical resection is an even greater risk factor for patients with posterior fossa metastases. Further investigation is necessary to determine whether SRS alone, cavity-directed SRS, or surgical resection followed by WBRT is most appropriate in this patient group. Author Disclosure: R. Katipally: None. P.P. Koffer: None. P.S. Rava: None. D. Cielo: None. S.A. Toms: None. T.A. DiPetrillo: Patent/License Fees/Copyright; Thomas DiPetrillo. J.T. Hepel; ABS Annual Meeting 2015, ACRO Accreditation.
1009 A Learning-Based Approach to Derive Electron Density from Anatomical MRI for Radiation Therapy Treatment Planning X. Yang,1 Y. Lei,1 H.K.G. Shu,2 P.J. Rossi,1 H. Mao,3 H. Shim,2 W.J. Curran Jr,2 and T. Liu2; 1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 2Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, 3Department of Radiology and Imaging Sciences and Winship Cancer Institute, Emory University, Atlanta, GA Purpose/Objective(s): The application of MRI significantly improves the accuracy and reliability of target delineation for many disease sites in radiation therapy due to its superior soft tissue contrast as compared with CT. A treatment planning process with MRI as the sole imaging modality could eliminate systematic MR-CT co-registration errors, reduce medical cost, spare the patient from CT x-ray exposure, and simplify clinical workflow. However, MRI data do not contain electron density information that is necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a learningbased method to derive electron density from routine anatomical MRI for MRI-only radiotherapy treatment planning. Materials/Methods: We initially build a set of paired training MRI and CT images with the CT images serving as the regression target of the MRI. Then we perform a preprocessing by removing uninformative regions and reducing noise, followed by aligning MRI and CT images. The prediction of CT images consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative MR-CT features are identified by feature selection to train a random forest. During the prediction stage, we extract the selected features from the new (target) MR image and feed them into the well-trained forests for the CT image prediction. This prediction technique was tested with brain MR and CT images of 10 patients. We performed leave-one-out cross-validation method to evaluate the proposed CT prediction algorithm. Results: Our predicted CT images generated from MRI were compared with the original CT images. In order to get a quantitative evaluation, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. Overall the mean MAE, PSNR, and FSIM were 16.26 2.64, 33.57 0.66, and 0.82 0.03 for 10 patients’ data, which demonstrated the CT prediction accuracy of the proposed learning-based method. Conclusion: We have investigated a novel learning-based approach to generate CT images from routine MR images based on a random forest regression with patch-based anatomical signatures to effectively capture the relationship between the CT and MR images. We have demonstrated that the reported method is capable of reliably predicting CT images from the MR images. This CT image prediction technique could be a useful tool for MRI-based radiation treatment planning or attenuation correction for quantifying PET images when using a PET/MRI scanner.
S174 Author Disclosure: X. Yang: None. Y. Lei: None. H. Shu: None. P.J. Rossi: None. H. Mao: None. H. Shim: None. W.J. Curran: None. T. Liu: None.
1010 Using Machine Learning to Predict Physician-Approved Dose Distributions for Pancreatic SBRT W. Campbell,1 L.A. Olsen,2 M. Miften,1 K.A. Goodman,1 T. Schefter,1 and B.L. Jones1; 1Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, CO, 2Memorial Hospital e UCHealth, Colorado Springs, CO Purpose/Objective(s): Stereotactic body radiation therapy (SBRT) is emerging as an attractive treatment option for patients with advanced pancreatic cancer. However, retrospective analysis of the RTOG-9704 phase III trial recently revealed that failure to adhere to radiation therapy protocols was significantly associated with reduced median survival. This suggests that ensuring high quality radiation therapy is crucial, and that future pancreatic radiation therapy trials should include more robust methods for providing patient-specific plan quality validation. This study trains artificial neural network dose models (ANN-DMs) for pancreatic SBRT, and validates their ability to predict patient-specific dose distributions similar to physician-approved treatment plans. Materials/Methods: Arc-based SBRT treatment plans for 49 pancreatic cancer patients were prepared, delivering 30-33 Gy in five fractions. Treatments were overseen by one of two physicians, each with their own treatment protocol including dosage prescribed, volumes treated, and primary organs-at-risk (OARs). Physician-approved treatment plans were used to train artificial neural network dose models (ANN-DMs) that could predict physician-approved dose distributions based on a set of geometric and plan parameters. For each individual voxel, these parameters were used as inputs to a neural network, and a single output of dose was compared against that voxel’s dose, as calculated by the treatment planning system. Patient datasets were randomly allocated, with roughly 2/3rds used for training and 1/3rd used for validation. Differences between clinical and ANN-DM dose distributions were used to evaluate model performance. Results: Mean dose errors were less than 5% at all distances from the PTV, and mean absolute dose errors were on the order of 5%, but no more than 10%. Dose-volume histogram errors demonstrated good model performance above 25 Gy, but larger errors were seen at lower doses. Remarkable improvements in ANN-DM accuracy (from >30% to <5% mean absolute dose error) were achieved by training separate dose models for each set of OAR constraints and prescription doses. Following one treatment protocol, 6 plans prepared at another institution were used for model validation without being included in model training and no significant increases in model errors were observed. Conclusion: Dose distributions predicted by trained pancreatic SBRT ANN-DMs showed excellent overall agreement with physician-approved dose distributions, and predictive accuracy was substantially improved by developing separate neural network models for differing treatment protocols. Model accuracy for a given treatment protocol was also maintained for patients planned and treated at another institution. For future largescale trials of pancreatic SBRT, such a model could be trained using consensus guidelines for high-quality plans, allowing for patient-specific plan quality validation in a multi-institutional setting. Author Disclosure: W. Campbell: Research Grant; Varian Medical Systems. L.A. Olsen: None. M. Miften: Research Grant; Varian Medical Systems. K.A. Goodman: None. T. Schefter: Honoraria; Sirtex. Travel Expenses; Sirtex. B.L. Jones: Research Grant; Varian Medical Systems.
1011 Knowledge Engineering-Based Quality Evaluation of NRG Oncology RTOG 0522 Treatment Plans H. Geng,1 T.G. Giaddui,1 H. Zhong,2 D.I. Rosenthal,3 J.M. Galvin,4 Y. Xiao,2 and N. Linnemann5; 1University of Pennsylvania, Philadelphia, PA,
International Journal of Radiation Oncology Biology Physics 2
Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 3MD Anderson Head and Neck Cancer Symptom Working Group, The University of Texas MD Anderson Cancer Center, Houston, TX, 4IROC, Philadelphia, PA, 5American College of Radiology, Philadelphia, PA Purpose/Objective(s): To evaluate radiotherapy treatment plan quality of IMRT plans submitted for RTOG 0522 clinical trial (A Randomized Phase III Trial of Concurrent Accelerated Radiation and Cisplatin versus Concurrent Accelerated Radiation, Cisplatin, and Cetuximab (C225) [Followed by Surgery for Selected Patients] for Stage III and IV Head and Neck Carcinomas). To use knowledge engineering based models from RapidPlan (built from HN002) for the quality study, and to re-optimize the plans by the model with the aim of further sparing the organs at risk and improve the quality of these plans in general. Materials/Methods: A head and neck RapidPlan model was initially built in Varian Eclipse treatment planning system (Version 13.6.15) using treatment plans from the NRGHN002 clinical trial (A Randomized Phase II Trial for patients with p16 positive, non-smoking-associated, locoregionally advanced oropharyngeal cancer). The model was duplicated and tuned to be used for QA and re-optimizing treatment plans from RTOG 0522. The modified model was then used to QA and re-optimize plans with deviation unacceptable scores (i.e., failed to pass the dosimetric compliance criteria of RTOG 0522). Results: Initial quality review of 748 IMRT plans submitted to RTOG0522 show that 21 plans did not meet the spinal cord dose constraint (D0.03 cc
1012 Investigation of Organs-at-Risk Contouring Accuracy for MR-Only Radiation Therapy in the Cranial Region C.H. Li,1 W.W.K. Fung,1 and G. Chiu2; 1Hong Kong Sanatorium and Hospital, Happy Valley, Hong Kong, 2Department of Radiotherapy, Ho ng Kong Sanatorium and Hospital, Happy Valley, Hong Kong Purpose/Objective(s): Accurate structure delineation on MR is crucial for MR-only RT planning. This study assessed the accuracy and consistency of OAR contouring on high-resolution 3D isotropic MR compared