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Gay SS, Cardenas CE, Nguyen C, Netherton TJ, Yu C, Zhao Y, Skett S, Patel T, Adjogatse D, Guerrero Urbano T, Naidoo K, Beadle BM, Yang J, Aggarwal A, Court LE. Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy. Sci Rep 2023; 13:21797. [PMID: 38066074 PMCID: PMC10709623 DOI: 10.1038/s41598-023-48944-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.
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Affiliation(s)
- Skylar S Gay
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
| | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Callistus Nguyen
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Tucker J Netherton
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Cenji Yu
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Yao Zhao
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | | | | | | | | | | | | | - Jinzhong Yang
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | | | - Laurence E Court
- Unit 1472, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
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Gay SS, Kisling KD, Anderson BM, Zhang L, Rhee DJ, Nguyen C, Netherton T, Yang J, Brock K, Jhingran A, Simonds H, Klopp A, Beadle BM, Court LE, Cardenas CE. Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy. J Appl Clin Med Phys 2023; 24:e14131. [PMID: 37670488 PMCID: PMC10691634 DOI: 10.1002/acm2.14131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/08/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
PURPOSE Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.
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Affiliation(s)
- Skylar S. Gay
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | | | | | - Lifei Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Dong Joo Rhee
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Callistus Nguyen
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tucker Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Jinzhong Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Kristy Brock
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Hannah Simonds
- University Hospitals Plymouth NHS TrustPlymouthUnited Kingdom
| | - Ann Klopp
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Beth M. Beadle
- Department of Radiation OncologyStanford UniversityPalo AltoCaliforniaUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
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Gronberg MP, Jhingran A, Netherton TJ, Gay SS, Cardenas CE, Chung C, Fuentes D, Fuller CD, Howell RM, Khan M, Lim TY, Marquez B, Olanrewaju AM, Peterson CB, Vazquez I, Whitaker TJ, Wooten Z, Yang M, Court LE. Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers. Med Phys 2023; 50:6639-6648. [PMID: 37706560 PMCID: PMC10947338 DOI: 10.1002/mp.16735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 08/22/2023] [Accepted: 08/26/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics ofD 1 % ${D}_{1{\mathrm{\% }}}$ andD 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.
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Affiliation(s)
- Mary P. Gronberg
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Anuja Jhingran
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tucker J. Netherton
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Skylar S. Gay
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Carlos E. Cardenas
- Department of Radiation OncologyThe University of Alabama at BirminghamBirminghamAlabamaUSA
| | - Christine Chung
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - David Fuentes
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Imaging PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Clifton D. Fuller
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of Radiation OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Rebecca M. Howell
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Meena Khan
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Tze Yee Lim
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Barbara Marquez
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Adenike M. Olanrewaju
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Christine B. Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Ivan Vazquez
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Thomas J. Whitaker
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Zachary Wooten
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of StatisticsRice UniversityHoustonTexasUSA
| | - Ming Yang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
| | - Laurence E. Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical SciencesHoustonTexasUSA
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Gronberg MP, Gay SS, Netherton TJ, Rhee DJ, Court LE, Cardenas CE. Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture. Med Phys 2021; 48:5567-5573. [PMID: 34157138 DOI: 10.1002/mp.14827] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/17/2021] [Accepted: 02/24/2021] [Indexed: 12/31/2022] Open
Abstract
PURPOSE Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reducing planning times. This technical note describes our participation in the American Association of Physicists in Medicine Open Knowledge-Based Planning Challenge (OpenKBP), a competition to accurately predict radiation therapy dose distributions. METHODS A three-dimensional (3D) densely connected U-Net with dilated convolutions was developed to predict 3D dose distributions given contoured CT images of head and neck patients as input. While traditional augmentation techniques such as rotations and translations were explored, it was found that training on random patches alone resulted in the greatest model performance. A custom-weighted mean squared error loss function was employed. Finally, an ensemble of best-performing networks was used to generate the final challenge predictions. RESULTS Our team (SuperPod) placed second in the dose stream of the OpenKBP challenge. The average mean absolute difference between the predicted and clinical dose distributions of the testing dataset was 2.56 Gy. On average, the predicted normalized target DVH metrics were within 3% of the clinical plans, and the predicted organ at risk DVH metrics were within 2 Gy of the clinical plans. CONCLUSIONS The developed 3D dense dilated U-Net architecture can accurately predict 3D radiotherapy dose distributions and can be used as part of a fully automated radiation therapy planning pipeline.
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Affiliation(s)
- Mary P Gronberg
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Tucker J Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
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5
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Hernandez S, Sjogreen C, Gay SS, Nguyen C, Netherton T, Olanrewaju A, Zhang LJ, Rhee DJ, Méndez JD, Court LE, Cardenas CE. Development and dosimetric assessment of an automatic dental artifact classification tool to guide artifact management techniques in a fully automated treatment planning workflow. Comput Med Imaging Graph 2021; 90:101907. [PMID: 33845433 PMCID: PMC8180493 DOI: 10.1016/j.compmedimag.2021.101907] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 02/05/2021] [Accepted: 03/14/2021] [Indexed: 12/03/2022]
Abstract
Purpose: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. Methods: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. Results: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. Conclusion: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.
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Affiliation(s)
- Soleil Hernandez
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA.
| | - Carlos Sjogreen
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Skylar S Gay
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Callistus Nguyen
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Tucker Netherton
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Adenike Olanrewaju
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Lifei Joy Zhang
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Dong Joo Rhee
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - José David Méndez
- The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Laurence E Court
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
| | - Carlos E Cardenas
- The University of Texas MD Anderson Cancer Center Graduate School of Biomedical Sciences, Houston, TX, USA; The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, Houston, TX, USA
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Cardenas CE, Beadle BM, Garden AS, Skinner HD, Yang J, Rhee DJ, McCarroll RE, Netherton TJ, Gay SS, Zhang L, Court LE. Generating High-Quality Lymph Node Clinical Target Volumes for Head and Neck Cancer Radiation Therapy Using a Fully Automated Deep Learning-Based Approach. Int J Radiat Oncol Biol Phys 2020; 109:801-812. [PMID: 33068690 PMCID: PMC9472456 DOI: 10.1016/j.ijrobp.2020.10.005] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/12/2020] [Accepted: 10/06/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE To develop a deep learning model that generates consistent, high-quality lymph node clinical target volumes (CTV) contours for head and neck cancer (HNC) patients, as an integral part of a fully automated radiation treatment planning workflow. METHODS AND MATERIALS Computed tomography (CT) scans from 71 HNC patients were retrospectively collected and split into training (n = 51), cross-validation (n = 10), and test (n = 10) data sets. All had target volume delineations covering lymph node levels Ia through V (Ia-V), Ib through V (Ib-V), II through IV (II-IV), and retropharyngeal (RP) nodes, which were previously approved by a radiation oncologist specializing in HNC. Volumes of interest (VOIs) about nodal levels were automatically identified using computer vision techniques. The VOI (cropped CT image) and approved contours were used to train a U-Net autosegmentation model. Each lymph node level was trained independently, with model parameters optimized by assessing performance on the cross-validation data set. Once optimal model parameters were identified, overlap and distance metrics were calculated between ground truth and autosegmentations on the test set. Lastly, this final model was used on 32 additional patient scans (not included in original 71 cases) and autosegmentations visually rated by 3 radiation oncologists as being "clinically acceptable without requiring edits," "requiring minor edits," or "requiring major edits." RESULTS When comparing ground truths to autosegmentations on the test data set, median Dice Similarity Coefficients were 0.90, 0.90, 0.89, and 0.81, and median mean surface distance values were 1.0 mm, 1.0 mm, 1.1 mm, and 1.3 mm for node levels Ia-V, Ib-V, II-IV, and RP nodes, respectively. Qualitative scoring varied among physicians. Overall, 99% of autosegmented target volumes were either scored as being clinically acceptable or requiring minor edits (ie, stylistic recommendations, <2 minutes). CONCLUSIONS We developed a fully automated artificial intelligence approach to autodelineate nodal CTVs for patients with intact HNC. Most autosegmentations were found to be clinically acceptable after qualitative review when considering recommended stylistic edits. This promising work automatically delineates nodal CTVs in a robust and consistent manner; this approach can be implemented in ongoing efforts for fully automated radiation treatment planning.
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Affiliation(s)
- Carlos E Cardenas
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Beth M Beadle
- Department of Radiation Oncology, Stanford University, Palo Alto, California
| | - Adam S Garden
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Heath D Skinner
- Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jinzhong Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rachel E McCarroll
- Department of Radiation Oncology, University of Maryland Medical System, Baltimore, Maryland
| | - Tucker J Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Skylar S Gay
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Lifei Zhang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
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Gay SS, Netherton TJ, Cardenas CE, Ger RB, Balter PA, Dong L, Mihailidis D, Court LE. Dosimetric impact and detectability of multi-leaf collimator positioning errors on Varian Halcyon. J Appl Clin Med Phys 2019; 20:47-55. [PMID: 31294923 PMCID: PMC6698762 DOI: 10.1002/acm2.12677] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/06/2019] [Accepted: 06/21/2019] [Indexed: 11/11/2022] Open
Abstract
The purpose of this study is to investigate the dosimetric impact of multi‐leaf collimator (MLC) positioning errors on a Varian Halcyon for both random and systematic errors, and to evaluate the effectiveness of portal dosimetry quality assurance in catching clinically significant changes caused by these errors. Both random and systematic errors were purposely added to 11 physician‐approved head and neck volumetric modulated arc therapy (VMAT) treatment plans, yielding a total of 99 unique plans. Plans were then delivered on a preclinical Varian Halcyon linear accelerator and the fluence was captured by an opposed portal dosimeter. When comparing dose–volume histogram (DVH) values of plans with introduced MLC errors to known good plans, clinically significant changes to target structures quickly emerged for plans with systematic errors, while random errors caused less change. For both error types, the magnitude of clinically significant changes increased as error size increased. Portal dosimetry was able to detect all systematic errors, while random errors of ±5 mm or less were unlikely to be detected. Best detection of clinically significant errors, while minimizing false positives, was achieved by following the recommendations of AAPM TG‐218. Furthermore, high‐ to moderate correlation was found between dose DVH metrics for normal tissues surrounding the target and portal dosimetry pass rates. Therefore, it may be concluded that portal dosimetry on the Halcyon is robust enough to detect errors in MLC positioning before they introduce clinically significant changes to VMAT treatment plans.
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Affiliation(s)
- Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker J Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Peter A Balter
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Lei Dong
- Radiation Oncology, Hospital, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitris Mihailidis
- Radiation Oncology, Hospital, University of Pennsylvania, Philadelphia, PA, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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8
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Kisling KD, Ger RB, Netherton TJ, Cardenas CE, Owens CA, Anderson BM, Lee J, Rhee DJ, Edward SS, Gay SS, He Y, David SD, Yang J, Nitsch PL, Balter PA, Urbauer DL, Peterson CB, Court LE, Dube S. A snapshot of medical physics practice patterns. J Appl Clin Med Phys 2018; 19:306-315. [PMID: 30272385 PMCID: PMC6236839 DOI: 10.1002/acm2.12464] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 05/16/2018] [Accepted: 08/16/2018] [Indexed: 11/17/2022] Open
Abstract
A large number of surveys have been sent to the medical physics community addressing many clinical topics for which the medical physicist is, or may be, responsible. Each survey provides an insight into clinical practice relevant to the medical physics community. The goal of this study was to create a summary of these surveys giving a snapshot of clinical practice patterns. Surveys used in this study were created using SurveyMonkey and distributed between February 6, 2013 and January 2, 2018 via the MEDPHYS and MEDDOS listserv groups. The format of the surveys included questions that were multiple choice and free response. Surveys were included in this analysis if they met the following criteria: more than 20 responses, relevant to radiation therapy physics practice, not single‐vendor specific, and formatted as multiple‐choice questions (i.e., not exclusively free‐text responses). Although the results of free response questions were not explicitly reported, they were carefully reviewed, and the responses were considered in the discussion of each topic. Two‐hundred and fifty‐two surveys were available, of which 139 passed the inclusion criteria. The mean number of questions per survey was 4. The mean number of respondents per survey was 63. Summaries were made for the following topics: simulation, treatment planning, electron treatments, linac commissioning and quality assurance, setup and treatment verification, IMRT and VMAT treatments, SRS/SBRT, breast treatments, prostate treatments, brachytherapy, TBI, facial lesion treatments, clinical workflow, and after‐hours/emergent treatments. We have provided a coherent overview of medical physics practice according to surveys conducted over the last 5 yr, which will be instructive for medical physicists.
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Affiliation(s)
- Kelly D Kisling
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rachel B Ger
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker J Netherton
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E Cardenas
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Constance A Owens
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Brian M Anderson
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joonsang Lee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dong Joo Rhee
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sharbacha S Edward
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yulun He
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shaquan D David
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jinzhong Yang
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Paige L Nitsch
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter A Balter
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Diana L Urbauer
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Christine B Peterson
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Scott Dube
- Morton Plant Mease Health System, Clearwater, FL, USA
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9
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Rubinstein AE, Ingram WS, Anderson BM, Gay SS, Fave XJ, Ger RB, McCarroll RE, Owens CA, Netherton TJ, Kisling KD, Court LE, Yang J, Li Y, Lee J, Mackin DS, Cardenas CE. Cost-effective immobilization for whole brain radiation therapy. J Appl Clin Med Phys 2017; 18:116-122. [PMID: 28585732 PMCID: PMC5874864 DOI: 10.1002/acm2.12101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 02/24/2017] [Accepted: 04/05/2017] [Indexed: 11/23/2022] Open
Abstract
To investigate the inter‐ and intra‐fraction motion associated with the use of a low‐cost tape immobilization technique as an alternative to thermoplastic immobilization masks for whole‐brain treatments. The results of this study may be of interest to clinical staff with severely limited resources (e.g., in low‐income countries) and also when treating patients who cannot tolerate standard immobilization masks. Setup reproducibility of eight healthy volunteers was assessed for two different immobilization techniques. (a) One strip of tape was placed across the volunteer's forehead and attached to the sides of the treatment table. (b) A second strip was added to the first, under the chin, and secured to the table above the volunteer's head. After initial positioning, anterior and lateral photographs were acquired. Volunteers were positioned five times with each technique to allow calculation of inter‐fraction reproducibility measurements. To estimate intra‐fraction reproducibility, 5‐minute anterior and lateral videos were taken for each technique per volunteer. An in‐house software was used to analyze the photos and videos to assess setup reproducibility. The maximum intra‐fraction displacement for all volunteers was 2.8 mm. Intra‐fraction motion increased with time on table. The maximum inter‐fraction range of positions for all volunteers was 5.4 mm. The magnitude of inter‐fraction and intra‐fraction motion found using the “1‐strip” and “2‐strip” tape immobilization techniques was comparable to motion restrictions provided by a thermoplastic mask for whole‐brain radiotherapy. The results suggest that tape‐based immobilization techniques represent an economical and useful alternative to the thermoplastic mask.
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Affiliation(s)
- Ashley E Rubinstein
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - W Scott Ingram
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Brian M Anderson
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Skylar S Gay
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xenia J Fave
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Rachel B Ger
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Rachel E McCarroll
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Constance A Owens
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Tucker J Netherton
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Kelly D Kisling
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Jinzhong Yang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Yuting Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
| | - Joonsang Lee
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dennis S Mackin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E Cardenas
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Graduate School of Biomedical Sciences, The University of Texas Health Sciences Center, Houston, TX, USA
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