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Tsang DS, Tsui G, Santiago AT, Keller H, Purdie T, Mcintosh C, Bauman G, La Macchia N, Parent A, Dama H, Ahmed S, Laperriere N, Millar BA, Liu V, Hodgson DC. A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain. Int J Radiat Oncol Biol Phys 2024; 119:1429-1436. [PMID: 38432285 DOI: 10.1016/j.ijrobp.2024.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/11/2024] [Accepted: 02/10/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planning efficiency for adults and children with primary brain tumors. METHODS AND MATERIALS In this prospective study, children and adults receiving 54 Gy fractionated RT for a primary brain tumor were enrolled. For each patient, one ML-assisted RT plan was created and compared with 1 or 2 plans created using standard ("manual") planning procedures. Plans were evaluated by the treating oncologist, who was blinded to the method of plan creation. The primary endpoint was the proportion of ML plans that were clinically acceptable for treatment. Secondary endpoints included the frequency with which ML plans were selected as preferable for treatment, and dosimetric differences between ML and manual plans. RESULTS A total of 116 manual plans and 61 ML plans were evaluated across 61 patients. Ninety-four percent of ML plans and 93% of manual plans were judged to be clinically acceptable (P = 1.0). Overall, the quality of ML plans was similar to manual plans. ML plans comprised 34.5% of all plans evaluated and were selected for treatment in 36.1% of cases (P = .82). Similar tumor target coverage was achieved between both planning methods. Normal brain (brain minus planning target volume) received an average of 1 Gy less mean dose with ML plans (compared with manual plans, P < .001). ML plans required an average of 45.8 minutes less time to create, compared with manual plans (P < .001). CONCLUSIONS ML-assisted automated planning creates high-quality plans for patients with brain tumors, including children. Plans created with ML assistance delivered slightly less dose to normal brain tissues and can be designed in less time.
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Affiliation(s)
- Derek S Tsang
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
| | - Grace Tsui
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Anna T Santiago
- Department of Biostatistics, University Health Network, Toronto, Ontario, Canada
| | - Harald Keller
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Thomas Purdie
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Chris Mcintosh
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Glenn Bauman
- London Regional Cancer Program, London, Ontario, Canada
| | - Nancy La Macchia
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Amy Parent
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Hitesh Dama
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Sameera Ahmed
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Normand Laperriere
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Barbara-Ann Millar
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Valerie Liu
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - David C Hodgson
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
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Han F, Xue Y, Huang S, Lu T, Yang Y, Cao Y, Chen J, Hou H, Sun Y, Wang W, Yuan Z, Tao Z, Jiang S. Development and validation of an automated Tomotherapy planning method for cervical cancer. Radiat Oncol 2024; 19:88. [PMID: 38978062 PMCID: PMC11232346 DOI: 10.1186/s13014-024-02482-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
PURPOSE This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness. MATERIALS AND METHODS The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. We compared A-TOMO with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis, with a P-value < 0.05 indicating statistical significance. RESULTS A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+ < 0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V50Gy, V40Gy and Dmean for the bladder and rectum, as well as Dmean for the bowel bag, femoral heads, and kidneys (all P+ < 0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 min. CONCLUSION We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.
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Affiliation(s)
- Feiru Han
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yi Xue
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Sheng Huang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Tong Lu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yining Yang
- Department of Radiation Oncology, Tianjin First Central Hospital, Tianjin, China
| | - Yuanjie Cao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Jie Chen
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Hailing Hou
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yao Sun
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wei Wang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhiyong Yuan
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Zhen Tao
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
| | - Shengpeng Jiang
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
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Moore LC, Ahern F, Li L, Kallis K, Kisling K, Cortes KG, Nwachukwu C, Rash D, Yashar CM, Mayadev J, Zou J, Vasconcelos N, Meyers SM. Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models. Med Phys 2024; 51:4591-4606. [PMID: 38814165 PMCID: PMC11309769 DOI: 10.1002/mp.17230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. PURPOSE The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. METHODS Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. RESULTS Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = -0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder = -0.77%/1.00%/-0.66%/-1.53%, rectum = 1.11%/-0.22%/-0.29%/-3.37%, sigmoid = -0.47%/-0.06%/-2.37%/-1.40%, and ME D90 = 2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. CONCLUSIONS 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.
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Affiliation(s)
- Lance C Moore
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Fritz Ahern
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Lingyi Li
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Karoline Kallis
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Kelly Kisling
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Katherina G Cortes
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Chika Nwachukwu
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Dominique Rash
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Catheryn M Yashar
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Jyoti Mayadev
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego and Moores Cancer Center, La Jolla, California, USA
| | - Nuno Vasconcelos
- Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA
| | - Sandra M Meyers
- Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
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Duan Y, Wang J, Wu P, Shao Y, Chen H, Wang H, Cao H, Gu H, Feng A, Huang Y, Shen Z, Lin Y, Kong Q, Liu J, Li H, Fu X, Yang Z, Cai X, Xu Z. AS-NeSt: A Novel 3D Deep Learning Model for Radiation Therapy Dose Distribution Prediction in Esophageal Cancer Treatment With Multiple Prescriptions. Int J Radiat Oncol Biol Phys 2024; 119:978-989. [PMID: 38159780 DOI: 10.1016/j.ijrobp.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 11/06/2023] [Accepted: 12/03/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE Implementing artificial intelligence technologies allows for the accurate prediction of radiation therapy dose distributions, enhancing treatment planning efficiency. However, esophageal cancers present unique challenges because of tumor complexity and diverse prescription types. Additionally, limited data availability hampers the effectiveness of existing artificial intelligence models. This study developed a deep learning model, trained on a diverse data set of esophageal cancer prescriptions, to improve dose prediction accuracy. METHODS AND MATERIALS We retrospectively collected data from 530 patients with esophageal cancer, including single-target and simultaneous integrated boost prescriptions, for model building. The proposed Asymmetric ResNeSt (AS-NeSt) model features novel 3-dimensional (3D) ResNeSt blocks and an asymmetrical architecture. We constructed a loss function targeting global and local doses and validated the model's performance against existing alternatives. Model-assisted experiments were used to validate its clinical benefits. RESULTS The AS-NeSt model maintained an absolute prediction error below 5% for each dosimetric metric. The average Dice similarity coefficient for isodose volumes was 0.93. The model achieved an average relative prediction error of 2.02%, statistically lower than Hierarchically Densely Connected U-net (4.17%), DoseNet (2.35%), and Densely Connected Network (3.65%). It also demonstrated significantly fewer parameters and shorter prediction times. Clinically, the AS-NeSt model raised physicians' ability to accurately preassess appropriate treatment methods before planning from 95.24% to 100%, reduced planning time by over 61% for junior dosimetrists and 52% for senior dosimetrists, and decreased both inter- and intra-dosimetrist discrepancies by more than 50%. CONCLUSIONS The AS-NeSt model, developed with innovative 3D ResNeSt blocks and an asymmetrical encoder-decoder structure, has been validated using clinical esophageal cancer patient data. It accurately predicts 3D dose distributions for various prescriptions, including simultaneous integrated boost, showing potential to improve the management of esophageal cancer treatment in a clinical setting.
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Affiliation(s)
- Yanhua Duan
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Jiyong Wang
- Shanghai Pulse Medical Technology Inc, Shanghai, China
| | - Puyu Wu
- Verisk Information Technology Ltd, Shanghai, China
| | - Yan Shao
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hua Chen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Wang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongbin Cao
- Department of Radiation Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengle Gu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Aihui Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Ying Huang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Modern Physics, Fudan University, Shanghai, China
| | - Zhenjiong Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Lin
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qing Kong
- Institute of Modern Physics, Fudan University, Shanghai, China
| | - Jun Liu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongxuan Li
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaolong Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhangru Yang
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xuwei Cai
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zhiyong Xu
- Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Douglas R, Olanrewaju A, Mumme R, Zhang L, Beadle BM, Court LE. Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning. J Appl Clin Med Phys 2024; 25:e14338. [PMID: 38610118 PMCID: PMC11244666 DOI: 10.1002/acm2.14338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 03/05/2024] [Accepted: 03/15/2024] [Indexed: 04/14/2024] Open
Abstract
PURPOSE Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool. METHODS For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria. RESULTS For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively. CONCLUSIONS This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.
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Affiliation(s)
- Raphael Douglas
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Adenike Olanrewaju
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Raymond Mumme
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Lifei Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Beth M. Beadle
- Department of Radiation OncologyStanford UniversityStanfordCaliforniaUSA
| | - Laurence Edward Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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Scaggion A, Cavinato S, Dusi F, El Khouzai B, Guida F, Paronetto C, Rossato MA, Sapignoli S, Scott ASA, Sepulcri M, Paiusco M. On the necessity of specialized knowledge-based models for SBRT prostate treatments plans. Phys Med 2024; 121:103364. [PMID: 38701626 DOI: 10.1016/j.ejmp.2024.103364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 03/21/2024] [Accepted: 04/19/2024] [Indexed: 05/05/2024] Open
Abstract
PURPOSE Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments. MATERIALS AND METHODS A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm. RESULTS The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans. CONCLUSION A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.
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Affiliation(s)
- Alessandro Scaggion
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy.
| | - Samuele Cavinato
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | - Francesca Dusi
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | - Badr El Khouzai
- S.C. Radioterapia, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | - Federica Guida
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | - Chiara Paronetto
- S.C. Radioterapia, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | | | - Sonia Sapignoli
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | | | - Matteo Sepulcri
- S.C. Radioterapia, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
| | - Marta Paiusco
- S.C. Fisica Sanitaria, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
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Wheeler PA, West NS, Powis R, Maggs R, Chu M, Pearson RA, Willis N, Kurec B, Reed KL, Lewis DG, Staffurth J, Spezi E, Millin AE. Multi-institutional evaluation of a Pareto navigation guided automated radiotherapy planning solution for prostate cancer. Radiat Oncol 2024; 19:45. [PMID: 38589961 PMCID: PMC11003074 DOI: 10.1186/s13014-024-02404-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/15/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer. METHODS The implemented 'Pareto Guided Automated Planning' (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a 'Protocol Based Automatic Iterative Optimisation' planning framework. 30 previous patients were randomly selected by each institution (IA and IB), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions' clinical preferences. A single automated plan (VMATAuto) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMATClinical) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution. RESULTS PGAP led to marked improvements across the majority of rectal dose metrics, with Dmean reduced by 3.7 Gy and 1.8 Gy for IA and IB respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for IB but increased for IA. Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D98% was generally improved with VMATAuto), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMATAuto, with 31/40 considered superior to VMATClinical upon blind review. CONCLUSIONS PGAP enabled intuitive adaptation of automated protocols to an institution's planning aims and yielded plans more congruent with the institution's clinical preference than the locally produced manual clinical plans.
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Affiliation(s)
- Philip A Wheeler
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK.
| | - Nicholas S West
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Richard Powis
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Rhydian Maggs
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Michael Chu
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - Rachel A Pearson
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University Centre for Cancer, Newcastle University, Newcastle upon Tyne, UK
| | - Nick Willis
- Northern Centre for Cancer Care, Cancer Services and Clinical Haematology, Newcastle upon Tyne, UK
| | - Bartlomiej Kurec
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Katie L Reed
- Worcester Oncology Centre, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - David G Lewis
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
| | - John Staffurth
- School of Medicine, Cardiff University, Cardiff, Wales, UK
- Velindre Cancer Centre, Medical Directorate, Cardiff, Wales, UK
| | - Emiliano Spezi
- School of Engineering, Cardiff University, Cardiff, Wales, UK
| | - Anthony E Millin
- Radiotherapy Physics Department, Velindre Cancer Centre, CF14 2TL, Cardiff, Wales, UK
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8
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Kaderka R, Dogan N, Jin W, Bossart E. Effects of model size and composition on quality of head-and-neck knowledge-based plans. J Appl Clin Med Phys 2024; 25:e14168. [PMID: 37798910 PMCID: PMC10860434 DOI: 10.1002/acm2.14168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/23/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
PURPOSE Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions. METHODS The six models differed in training size and plan composition: The KBPFull (n = 203 plans), KBP101 (n = 101), KBP50 (n = 50), and KBP25 (n = 25) were trained with plans from two head-and-neck physicians. KBPA and KBPB each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000-7000 cGy by a third physician was re-planned with all KBP models for validation. Standard head-and-neck dosimetric parameters were used to compare resulting plans. KBPFull plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBPFull plans were presented to another physician for blind review. Dosimetric comparison of KBPFull against KBP101 , KBP50 , and KBP25 investigated the effect of model size. Finally, KBPA versus KBPB tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t-test (p < 0.05). RESULTS Compared to manual plans, KBPFull significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBPFull plan over the manual plan in 20/39 cases. Dosimetric differences between KBPFull , KBP101 , KBP50 , and KBP25 plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBPA and KBPB were below 110 cGy. CONCLUSIONS Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.
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Affiliation(s)
- Robert Kaderka
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - Nesrin Dogan
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - William Jin
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
| | - Elizabeth Bossart
- Department of Radiation OncologyUniversity of Miami Miller School of MedicineMiamiFloridaUSA
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9
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Takano S, Tomita N, Niwa M, Torii A, Takaoka T, Kita N, Uchiyama K, Nakanishi-Imai M, Ayakawa S, Iida M, Tsuzuki Y, Otsuka S, Manabe Y, Nomura K, Ogawa Y, Miyakawa A, Miyamoto A, Takemoto S, Yasui T, Hiwatashi A. Impact of radiation doses on clinical relapse of biochemically recurrent prostate cancer after prostatectomy. Sci Rep 2024; 14:113. [PMID: 38167430 PMCID: PMC10761985 DOI: 10.1038/s41598-023-50434-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
The relationship between radiation doses and clinical relapse in patients receiving salvage radiotherapy (SRT) for biochemical recurrence (BCR) after radical prostatectomy (RP) remains unclear. We identified 292 eligible patients treated with SRT between 2005 and 2018 at 15 institutions. Clinical relapse-free survival (cRFS) between the ≥ 66 Gy (n = 226) and < 66 Gy groups (n = 66) were compared using the Log-rank test, followed by univariate and multivariate analyses and a subgroup analysis. After a median follow-up of 73 months, 6-year biochemical relapse-free survival, cRFS, cancer-specific survival, and overall survival rates were 58, 92, 98, and 94%, respectively. Six-year cRFS rates in the ≥ 66 Gy and < 66 Gy groups were 94 and 87%, respectively (p = 0.022). The multivariate analysis revealed that Gleason score ≥ 8, seminal vesicle involvement, PSA at BCR after RP ≥ 0.5 ng/ml, and a dose < 66 Gy correlated with clinical relapse (p = 0.015, 0.012, 0.024, and 0.0018, respectively). The subgroup analysis showed the consistent benefit of a dose ≥ 66 Gy in patients across most subgroups. Doses ≥ 66 Gy were found to significantly, albeit borderline, increase the risk of late grade ≥ 2 GU toxicity compared to doses < 66 Gy (14% vs. 3.2%, p = 0.055). This large multi-institutional retrospective study demonstrated that a higher SRT dose (≥ 66 Gy) resulted in superior cRFS.
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Affiliation(s)
- Seiya Takano
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Natsuo Tomita
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan.
| | - Masanari Niwa
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Akira Torii
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Taiki Takaoka
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Nozomi Kita
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Kaoru Uchiyama
- Department of Radiology, Kariya Toyota General Hospital, 5-15 Sumiyoshi-Cho, Kariya, Aichi, 448-8505, Japan
| | - Mikiko Nakanishi-Imai
- Department of Radiology, Japanese Red Cross Aichi Medical Center Nagoya Daini Hospital, 2-9 Myoken-Cho, Showa-Ku, Nagoya, Aichi, 466-8650, Japan
| | - Shiho Ayakawa
- Department of Radiology, Japan Community Health Care Organization Chukyo Hospital, 1-1-10 Sanjo, Minami-Ku, Nagoya, Aichi, 457-8510, Japan
| | - Masato Iida
- Department of Radiation Oncology, Suzuka General Hospital, 1275-53 Yamanoue, Yasuzuka-Cho, Suzuka, Mie, 513-0818, Japan
| | - Yusuke Tsuzuki
- Department of Radiation Oncology, Nagoya Proton Therapy Center, Nagoya City West Medical Center, 1-1-1 Hirate-Cho, Kita-Ku, Nagoya, Aichi, 462-8508, Japan
| | - Shinya Otsuka
- Department of Radiology, Okazaki City Hospital, 3-1 Goshoai, Koryuji-Cho, Okazaki, Aichi, 444-8553, Japan
| | - Yoshihiko Manabe
- Department of Radiation Oncology, Nanbu Tokushukai General Hospital, 171-1 Hokama, Yaese-Cho, Shimajiri, Okinawa, 901-0493, Japan
| | - Kento Nomura
- Department of Radiotherapy, Nagoya City West Medical Center, 1-1-1 Hirate-Cho, Kita-Ku, Nagoya, Aichi, 462-8508, Japan
| | - Yasutaka Ogawa
- Department of Radiation Oncology, Kasugai Municipal Hospital, 1-1-1 Takaki-Cho, Kasugai, Aichi, 486-8510, Japan
| | - Akifumi Miyakawa
- Department of Radiation Oncology, National Hospital Organization Nagoya Medical Center, 4-1-1, Sannomaru, Naka-Ku, Nagoya, Aichi, 460-0001, Japan
| | - Akihiko Miyamoto
- Department of Radiation Oncology, Hokuto Hospital, 7-5 Kisen, Inada-Cho, Obihiro, Hokkaido, 080-0833, Japan
| | - Shinya Takemoto
- Department of Radiation Oncology, Fujieda Heisei Memorial Hospital, 123-1 Mizukami-Cho, Fujieda, Shizuoka, 426-8662, Japan
| | - Takahiro Yasui
- Department of Urology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
| | - Akio Hiwatashi
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-Cho, Mizuho-Ku, Nagoya, Aichi, 467-8601, Japan
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10
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Lin MH, Olsen L, Kavanaugh JA, Jacqmin D, Lobb E, Yoo S, Berry SL, Pichardo JC, Cardenas CE, Roper J, Kirk M, Cheung JP, Solberg TD, Moore KL, Kim M. Beyond Acceptable: The Vital Role of Medical Physicists in Ensuring High-Quality Treatment Plans. Pract Radiat Oncol 2024; 14:6-9. [PMID: 38182304 DOI: 10.1016/j.prro.2023.08.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/09/2023] [Accepted: 08/18/2023] [Indexed: 01/07/2024]
Affiliation(s)
- Mu-Han Lin
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, Texas.
| | - Lindsey Olsen
- Department of Radiation Oncology, Memorial Hospital, Colorado Springs, Colorado
| | - James A Kavanaugh
- Department of Radiation Oncology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota
| | - Dustin Jacqmin
- Department of Human Oncology, University of Wisconsin, Madison, Wisconsin
| | - Eric Lobb
- Department of Radiation Oncology, Ascension NE Wisconsin-St. Elizabeth Hospital, Appleton, Wisconsin
| | - Sua Yoo
- Radiation Oncology, Duke University Medical Center, Durham, North Carolina
| | - Sean L Berry
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | - Carlos E Cardenas
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Justin Roper
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Maura Kirk
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Joey P Cheung
- Radiation Oncology, Sutter Health Mills-Peninsula Medical Center, San Mateo, California
| | - Timothy D Solberg
- Department of Radiation Oncology, University of Washington, Seattle, Washington
| | - Kevin L Moore
- Department of Radiation Oncology, UC San Diego, La Jolla, California
| | - Minsun Kim
- Department of Radiation Oncology, University of Washington, Seattle, Washington
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11
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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] [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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12
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Schuring D, Westendorp H, van der Bijl E, Bol GH, Crijns W, Delor A, Jourani Y, Ong CL, Penninkhof J, Kierkels R, Verbakel W, van de Water T, van de Kamer JB. The NCS code of practice for the quality assurance of treatment planning systems (NCS-35). Phys Med Biol 2023; 68:205017. [PMID: 37748504 DOI: 10.1088/1361-6560/acfd06] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/25/2023] [Indexed: 09/27/2023]
Abstract
A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinviset al2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS. New sections about education, automated planning, information technology (IT) and updates are therefore added. Although the scope is photon therapy, large parts of this report will also apply to all other treatment modalities. This paper is a condensed version of these guidelines; the full version of the report in English is freely available from the NCS website (http://radiationdosimetry.org/ncs/publications). The paper starts with the scope of this report in relation to earlier reports on this subject. Next, general aspects of the commissioning process are addressed, like e.g. project management, education, and safety. It then focusses more on technical aspects such as beam commissioning and patient modeling, dose representation, dose calculation and (automated) plan optimisation. The final chapters deal with IT-related subjects and scripting, and the process of updating or upgrading the TPS.
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Affiliation(s)
- D Schuring
- Radiotherapiegroep, Radiation Oncology department, Arnhem/Deventer, The Netherlands
| | - H Westendorp
- Isala Hospital, Oncology department, Zwolle, The Netherlands
| | - E van der Bijl
- Radboud University Medical Center, Radiation Oncology department, Nijmegen, The Netherlands
| | - G H Bol
- University Medical Center Utrecht, Radiotherapy department, Utrecht, The Netherlands
| | - W Crijns
- KU Leuven-UZ Leuven, Oncology department, Radiation Oncology, Leuven, Belgium
| | - A Delor
- Institut Roi Albert II, Cliniques universitaires Saint-Luc, Radiation Oncology department, Brussels, Belgium
| | - Y Jourani
- Institut Jules Bordet-Université Libre de Bruxelles, Medical Physics department, Brussels, Belgium
| | - C Loon Ong
- Haga Hospital, Radiation Oncology department, The Hague, The Netherlands
| | - J Penninkhof
- Erasmus MC Cancer Institute-University Medical Center Rotterdam, Radiation Oncology department, Rotterdam, The Netherlands
| | - R Kierkels
- Radiotherapiegroep, Radiation Oncology department, Arnhem/Deventer, The Netherlands
| | - W Verbakel
- Amsterdam University Medical Centers-location VUmc, Radiation Oncology Department, Amsterdam, The Netherlands
| | - T van de Water
- Radiotherapeutic Institute Friesland, Leeuwarden, The Netherlands
| | - J B van de Kamer
- The Netherlands Cancer Institute, Department of Radiation Oncology, Amsterdam, The Netherlands
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13
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Osman AFI, Tamam NM, Yousif YAM. A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck. J Appl Clin Med Phys 2023; 24:e14015. [PMID: 37138549 PMCID: PMC10476994 DOI: 10.1002/acm2.14015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/05/2023] Open
Abstract
PURPOSE In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics. METHODS A dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices. RESULTS The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U-Net, 0.94 Gy (p = 0.40) for Res U-Net, 2.94 Gy (p = 0.09) for attention U-Net, and 3.51 Gy (p = 0.08) for U-Net. For the OARs, the values for theD m a x ${D_{max}}$ andD m e a n ${D_{mean}}$ indices were 2.72 Gy (p < 0.01) for attention Res U-Net, 2.94 Gy (p < 0.01) for Res U-Net, 1.10 Gy (p < 0.01) for attention U-Net, 0.84 Gy (p < 0.29) for U-Net. CONCLUSION All models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.
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Affiliation(s)
| | - Nissren M. Tamam
- Department of PhysicsCollege of SciencePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia
| | - Yousif A. M. Yousif
- Department of Radiation OncologyNorth West Cancer Centre – Tamworth HospitalTamworthAustralia
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14
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Flower E, Sykes J, Sullivan E, Busuttil G, Thiruthaneeswaran N, Cosgriff E, Chard J, Salkeld A, Thwaites D. Improving plan quality in cervical brachytherapy using a simple knowledge-based prediction tool for OAR dose (D2cm 3). Brachytherapy 2023; 22:623-629. [PMID: 37296007 DOI: 10.1016/j.brachy.2023.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 04/28/2023] [Accepted: 05/06/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE Toxicity from cervical brachytherapy has been demonstrated to correlate with the D2cm3 of the bladder, rectum, and bowel. This suggests a simplified version of knowledge-based planning investigating the relationship of the overlap distance for 2cm3 and the D2cm3 from planning may be possible. This work demonstrates the feasibility of simple knowledge-based planning to predict the D2cm3, detect suboptimal plans, and improve plan quality. METHODS AND MATERIALS The overlap volume histogram (OVH) method was used to determine the distance for 2cm3 of overlap between the OAR and CTV_HR. Linear plots modeled the OAR D2cm3 and 2cm3 overlap distance. Two datasets of 20 patients (plans from 43 insertions in each dataset) were used to create two independent models, and the performance of each model was compared using cross-validation. Doses were scaled to ensure consistent CTV_HR D90 values. The predicted D2cm3 is entered as the maximum constraint in the inverse planning algorithm. RESULTS Mean bladder D2cm3 decreased by 2.9% for the models from each dataset, mean rectal D2cm3 decreased 14.9% for the model from dataset 1 and 6.0% for the model from dataset 2, mean sigmoid D2cm3 decreased 10.7% for the model from dataset 1 and 6.1% for the model from dataset 2, mean bowel D2cm3 decreased 4.1% for the model from dataset 1 but no statistically significant difference was observed for the model from dataset 2. CONCLUSIONS A simplified knowledge-based planning method was used to predict D2cm3 and was able to automate optimization of brachytherapy plans for locally advanced cervical cancer.
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Affiliation(s)
- Emily Flower
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia; School of Physics, University of Sydney, Sydney, Australia.
| | - Jonathan Sykes
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia; School of Physics, University of Sydney, Sydney, Australia
| | - Emma Sullivan
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia
| | - Gemma Busuttil
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia
| | | | - Eireann Cosgriff
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia
| | - Jennifer Chard
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia
| | - Alison Salkeld
- Sydney West Radiation Oncology Network, Westmead, New South Wales, Australia; School of Medicine, University of Sydney, Sydney, Australia
| | - David Thwaites
- School of Physics, University of Sydney, Sydney, Australia
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15
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Yadav P, Pankuch M, McCorkindale J, Mitra RK, Rouse L, Khelashvili G, Mittal BB, Das IJ. Dosimetric evaluation of high-Z inhomogeneity with modern algorithms: A collaborative study. Phys Med 2023; 112:102649. [PMID: 37544030 DOI: 10.1016/j.ejmp.2023.102649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/12/2023] [Accepted: 07/31/2023] [Indexed: 08/08/2023] Open
Abstract
PURPOSE To evaluate modern dose calculation algorithms with high-Z prosthetic devices used in radiation treatment. METHODS A bilateral hip prosthetic patient was selected to see the effect of modern algorithms from the commercial system for plan comparisons. The CT data with dose constraints were sent to various institutions for dose calculations. The dosimetric parameters, D98%, D90%, D50% and D2% were compared. A water phantom with an actual prosthetic device was used to measure the dose using a parallel plate ionization chamber. RESULTS Dosimetric variability in PTV coverage was significant (>10%) among various treatment planning algorithms. The comparison of PTV dosimetric parameters, D98%, D90%, D50% and D2% as well as organs at risk (OAR) have large discrepancies compared to our previous publication with older algorithms (https://doi.org/10.1016/j.ejmp.2022.02.007) but provides realistic dose distribution with better homogeneity index (HI). Backscatter and forward scatter attenuation of the prosthesis was measured showing differences <15.7% at the interface among various algorithms. CONCLUSIONS Modern algorithms dose distributions have improved greatly compared to older generation algorithms. However, there is still significant differences at high-Z-tissue interfaces compared to the measurements. To ensure accuracy, it's important to take precautions avoiding placing any prosthesis in the beam direction and using type C algorithms.
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Affiliation(s)
- Poonam Yadav
- Department of Radiation Oncology, Northwest Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Mark Pankuch
- Northwestern Medicine Chicago Proton Center, 4455 Weaver Parkway, Warrenville, IL 60555, USA
| | - John McCorkindale
- Department of Radiation and Cellular Oncology, Northwestern Medicine 1000 N Westmoreland Rd, Lake Forest, IL 60045, USA
| | - Raj K Mitra
- Department of Radiation Oncology, Ochsner Health System, New Orleans, LA 7012, USA
| | - Luther Rouse
- Philips Healthcare, 100 Park Ave, Beachwood, OH 44122, USA
| | - Gocha Khelashvili
- Department of Radiation Oncology, Northwest Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Bharat B Mittal
- Department of Radiation Oncology, Northwest Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Indra J Das
- Department of Radiation Oncology, Northwest Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.
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Olch AJ, Gopalakrishnan M, Murphy ES, MacDonald SM, Hua CH. Toward Systematic Assessment and Improvement of Radiation Therapy Plan Quality of Cooperative Group Trial Submissions: A Report From the Children's Oncology Group. Pract Radiat Oncol 2023; 13:e374-e382. [PMID: 37037758 PMCID: PMC11163894 DOI: 10.1016/j.prro.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 04/12/2023]
Abstract
PURPOSE This study evaluates the quality of plans used for the treatment of patients in the Children's Oncology Group study ACNS1123. Plan quality is quantified based on a scoring system specific to the protocol. In this way, the distribution of plan quality scores is determined that can be used to identify plan quality issues for this study and for future plan quality improvement. METHODS AND MATERIALS ACNS1123 stratum 1 patients (70) were evaluated. This included 50 photon and 20 proton plans. Digital Imaging and Communications in Medicine (DICOM) structure and dose data were obtained from the Children's Oncology Group. A commercially available plan quality scoring algorithm was used to create a scoring system we designed using the protocol dosimetric requirements. The whole ventricle and boost planning target volumes (PTVs) could earn a maximum of 70 points, whereas the organs at risk could earn 30 points (total maximum score of 100 points). The scoring algorithm adjusted scores based on the difficulty in achieving the structure dose requirements, which depended on the proximity of the PTVs and the dose gradients achieved relative to the organs at risk. The distribution of plan scores was used to determine the mean, median, and range of scores. RESULTS The median adjusted plan quality scores for the 20 proton and 50 photon plans were 83.3 and 86.9, respectively. The range of adjusted scores (maximum to minimum) was 50 points. The average score adjustment was 7.4 points. Photon and proton plans performed almost equally. Average plan quality by individual structure revealed that the brain stem, PTV boost, and cochlea lost the most points. CONCLUSIONS This report is the first to systematically analyze overall radiation therapy plan quality scores for an entire cohort of patients treated in a cooperative group clinical trial. The methodology demonstrated a large variation in plan quality in this trial. Future clinical trials could potentially use this method to reduce plan quality variability, which may improve outcomes.
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Affiliation(s)
- Arthur J Olch
- Department of Radiation Oncology, University of Southern California and Children's Hospital Los Angeles, Los Angeles, California.
| | | | - Erin S Murphy
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Shannon M MacDonald
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Chia-Ho Hua
- Department of Radiation Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
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17
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Das IJ, Yadav P, Andersen AD, Chen ZJ, Huang L, Langer MP, Lee C, Li L, Popple RA, Rice RK, Schiff PB, Zhu TC, Abazeed ME. Dose prescription and reporting in stereotactic body radiotherapy: A multi-institutional study. Radiother Oncol 2023; 182:109571. [PMID: 36822361 PMCID: PMC10121952 DOI: 10.1016/j.radonc.2023.109571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND AND PURPOSE Radiation dose prescriptions are foundational for optimizing treatment efficacy and limiting treatment-related toxicity. We sought to assess the lack of standardization of SBRT dose prescriptions across institutions. MATERIALS & METHODS Dosimetric data from 1298 patients from 9 academic institutions treated with IMRT and VMAT were collected. Dose parameters D100, D98, D95, D50, and D2 were used to assess dosimetric variability. RESULTS Disease sites included lung (48.3 %) followed by liver (29.7 %), prostate (7.5 %), spine (6.8 %), brain (4.1 %), and pancreas (2.5 %). The PTV volume in lung varied widely with bimodality into two main groups (22.0-28.7 cm3) and (48.0-67.1 cm3). A hot spot ranging from 120-150 % was noted in nearly half of the patients, with significant variation across institutions. A D50 ≥ 110 % was found in nearly half of the institutions. There was significant dosimetric variation across institutions. CONCLUSIONS The SBRT prescriptions in the literature or in treatment guidelines currently lack nuance and hence there is significant variation in dose prescriptions across academic institutions. These findings add greater importance to the identification of dose parameters associated with improved clinical outcome comparisons as we move towards more hypofractionated treatments. There is a need for standardized reporting to help institutions in adapting treatment protocols based on the outcome of clinical trials. Dosimetric parameters are subsequently needed for uniformity and thereby standardizing planning guidelines to maximize efficacy, mitigate toxicity, and reduce treatment disparities are urgently needed.
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Affiliation(s)
- Indra J Das
- Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Poonam Yadav
- Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Aaron D Andersen
- Department of Radiation Oncology, Renown Medical Center, Reno, NV, USA
| | - Zhe Jay Chen
- Department of Therapeutic Radiology, Yale University, New haven, CT, USA
| | - Long Huang
- Department of Radiation Oncology, University of Utah, Salt Lake City, UT, USA
| | - Mark P Langer
- Department of Radiation Oncology, Indiana University Health, Indianapolis, IN, USA
| | - Choonik Lee
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Lin Li
- Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Richard A Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Roger K Rice
- Department of Radiation Medicine and Applied Science, University of California, San Diego, CA, USA
| | - Peter B Schiff
- Department of Radiation Oncology, New York University Grossman School of Medicine, New York, NY, USA
| | - Timothy C Zhu
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamed E Abazeed
- Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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18
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Kallis K, Moore LC, Cortes KG, Brown D, Mayadev J, Moore KL, Meyers SM. Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions. Phys Med Biol 2023; 68:10.1088/1361-6560/acc37c. [PMID: 36898161 PMCID: PMC10101723 DOI: 10.1088/1361-6560/acc37c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/10/2023] [Indexed: 03/12/2023]
Abstract
Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs).Approach. A dose rate kernelḋ(r,θ,φ)was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (Dcalc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error betweenDcalcand reference doseDref, computed using voxels withDref80%-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans whenDref= clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O usingDref= dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1N∑n=1Nabsxn-xn') over all voxels (xn= Dose,N= #voxels) and DTs (xn= DT,N= #dwell positions), mean differences (MD) in organD2ccand high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours.Main results. Validation plans agreed well with clinical plans (MADdose= 1.1%, MADDT= 4 s or 0.8% of total plan time,D2ccMD = -0.2% to 0.2% and D90 MD = -0.6%, DSC = 0.99). For automated plans, MADdose= 6.5% and MADDT= 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D2ccMD = -3.8% to 1.3% and D90 MD = -5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91).Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.
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Affiliation(s)
- Karoline Kallis
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Lance C Moore
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Katherina G Cortes
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Derek Brown
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Jyoti Mayadev
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Kevin L Moore
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
| | - Sandra M Meyers
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States of America
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19
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Romano C, Viola P, Craus M, Macchia G, Ferro M, Bonome P, Pierro A, Buwenge M, Arcelli A, Morganti AG, Deodato F, Cilla S. Feasibility-guided automated planning for stereotactic treatments of prostate cancer. Med Dosim 2023:S0958-3947(23)00020-1. [PMID: 36990847 DOI: 10.1016/j.meddos.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/09/2023] [Accepted: 02/23/2023] [Indexed: 03/29/2023]
Abstract
Significant improvements in plan quality using automated planning have been previously demonstrated. The aim of this study was to develop an optimal automated class solution for stereotactic radiotherapy (SBRT) planning of prostate cancer using the new Feasibility module implemented in the pinnacle evolution. Twelve patients were retrospectively enrolled in this planning study. Five plans were designed for each patient. Four plans were automatically generated using the 4 proposed templates for SBRT optimization implemented in the new pinnacle evolution treatment planning systems, differing for different settings of dose-fallout (low, medium, high and veryhigh). Based on the obtained results, the fifth plan (feas) was generated customizing the template with the optimal criteria obtained from the previous step and integrating in the template the "a-priori" knowledge of OARs sparing based on the Feasibility module, able to estimate the best possible dose-volume histograms of OARs before starting optimization. Prescribed dose was 35 Gy to the prostate in 5 fractions. All plans were generated with a full volumetric-modulated arc therapy arc and 6MV flattening filter-free beams, and optimized to ensure the same target coverage (95% of the prescription dose to 98% of the target). Plans were assessed according to dosimetric parameters and planning and delivery efficiency. Differences among the plans were evaluated using a Kruskal-Wallis 1-way analysis of variance. The requests for more aggressive objectives for dose falloff parameters (from low to veryhigh) translated in a statistically significant improvement of dose conformity, but at the expense of a dose homogeneity. The best automated plans in terms of best trade-off between target coverage and OARs sparing among the 4 plans automatically generated by the SBRT module were the high plans. The veryhigh plans reported a significant increase of high-doses to prostate, rectum, and bladder that was considered dosimetrically and clinically unacceptable. The feas plans were optimized on the basis on high plans, reporting significant reduction of rectum irradiation; Dmean, and V18 decreased by 19% to 23% (p = 0.031) and 4% to 7% (p = 0.059), respectively. No statistically significant differences were found in femoral heads and penile bulb irradiation for all dosimetric metrics. feas plans showed a significant increase of MU/Gy (mean: 368; p = 0.004), reflecting an increased level of fluence modulation. Thanks to the new efficient optimization engines implemented in pinnacle evolution (L-BFGS and layered graph), mean planning time was decreased to less than 10 minutes for all plans and all techniques. The integration of dose-volume histograms a-priori knowledge provided by the feasibility module in the automated planning process for SBRT planning has shown to significantly improve plan quality compared to generic protocol values as inputs.
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20
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Jayarathna S, Shen X, Chen RC, Li HH, Guida K. The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT. J Appl Clin Med Phys 2023:e13940. [PMID: 36827178 DOI: 10.1002/acm2.13940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/21/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.
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Affiliation(s)
- Sandun Jayarathna
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA.,Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinglei Shen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Ronald C Chen
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - H Harold Li
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
| | - Kenny Guida
- Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA
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21
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Baroudi H, Brock KK, Cao W, Chen X, Chung C, Court LE, El Basha MD, Farhat M, Gay S, Gronberg MP, Gupta AC, Hernandez S, Huang K, Jaffray DA, Lim R, Marquez B, Nealon K, Netherton TJ, Nguyen CM, Reber B, Rhee DJ, Salazar RM, Shanker MD, Sjogreen C, Woodland M, Yang J, Yu C, Zhao Y. Automated Contouring and Planning in Radiation Therapy: What Is 'Clinically Acceptable'? Diagnostics (Basel) 2023; 13:diagnostics13040667. [PMID: 36832155 PMCID: PMC9955359 DOI: 10.3390/diagnostics13040667] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/21/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.
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Affiliation(s)
- Hana Baroudi
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kristy K. Brock
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Wenhua Cao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xinru Chen
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Laurence E. Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Correspondence:
| | - Mohammad D. El Basha
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Maguy Farhat
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Skylar Gay
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Aashish Chandra Gupta
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Soleil Hernandez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kai Huang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - David A. Jaffray
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Rebecca Lim
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Barbara Marquez
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Kelly Nealon
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Callistus M. Nguyen
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Brandon Reber
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ramon M. Salazar
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mihir D. Shanker
- The University of Queensland, Saint Lucia 4072, Australia
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Carlos Sjogreen
- Department of Physics, University of Houston, Houston, TX 77004, USA
| | - McKell Woodland
- Department of Imaging Physics, Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Computer Science, Rice University, Houston, TX 77005, USA
| | - Jinzhong Yang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Cenji Yu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Yao Zhao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, USA
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22
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Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from a Phase III Lung Cancer Study. Cancers (Basel) 2023; 15:cancers15041014. [PMID: 36831358 PMCID: PMC9953775 DOI: 10.3390/cancers15041014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/09/2023] Open
Abstract
The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. In this study, we report on the use of these machine learning tools to guide the quality assurance of multicenter clinical trial plans. The data from 130 patients submitted to RTOG1308 were included in this study. Fifty patient cases were used to train separate photon and proton models on a commercially available platform based on principal component analysis. Models evaluated 80 patient cases. Statistical comparisons were made between the KBP plans and the original plans submitted for quality evaluation. Both photon and proton KBP plans demonstrate a statistically significant improvement of quality in terms of organ-at-risk (OAR) sparing. Proton KBP plans, a relatively emerging technique, show more improvements compared with photon plans. The KBP proton model is a useful tool for creating proton plans that adhere to protocol requirements. The KBP tool was also shown to be a useful tool for evaluating the quality of RT plans in the multicenter clinical trial setting.
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23
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Rhee DJ, Beddar S, Jaoude JA, Sawakuchi G, Martin R, Perles L, Yu C, He Y, Court LE, Ludmir EB, Koong AC, Das P, Koay EJ, Taniguichi C, Niedzielski JS. Dose Escalation for Pancreas SBRT: Potential and Limitations of using Daily Online Adaptive Radiation Therapy and an Iterative Isotoxicity Automated Planning Approach. Adv Radiat Oncol 2023; 8:101164. [PMID: 36798731 PMCID: PMC9926193 DOI: 10.1016/j.adro.2022.101164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 12/23/2022] [Indexed: 02/05/2023] Open
Abstract
Purpose To determine the dosimetric limitations of daily online adaptive pancreas stereotactic body radiation treatment by using an automated dose escalation approach. Methods and Materials We collected 108 planning and daily computed tomography (CT) scans from 18 patients (18 patients × 6 CT scans) who received 5-fraction pancreas stereotactic body radiation treatment at MD Anderson Cancer Center. Dose metrics from the original non-dose-escalated clinical plan (non-DE), the dose-escalated plan created on the original planning CT (DE-ORI), and the dose-escalated plan created on daily adaptive radiation therapy CT (DE-ART) were analyzed. We developed a dose-escalation planning algorithm within the radiation treatment planning system to automate the dose-escalation planning process for efficiency and consistency. In this algorithm, the prescription dose of the dose-escalation plan was escalated before violating any organ-at-risk (OAR) dose constraint. Dose metrics for 3 targets (gross target volume [GTV], tumor vessel interface [TVI], and dose-escalated planning target volume [DE-PTV]) and 9 OARs (duodenum, large bowel, small bowel, stomach, spinal cord, kidneys, liver, and skin) for the 3 plans were compared. Furthermore, we evaluated the effectiveness of the online adaptive dose-escalation planning process by quantifying the effect of the interfractional dose distribution variations among the DE-ART plans. Results The median D95% dose to the GTV/TVI/DE-PTV was 33.1/36.2/32.4 Gy, 48.5/50.9/40.4 Gy, and 53.7/58.2/44.8 Gy for non-DE, DE-ORI, and DE-ART, respectively. Most OAR dose constraints were not violated for the non-DE and DE-ART plans, while OAR constraints were violated for the majority of the DE-ORI patients due to interfractional motion and lack of adaptation. The maximum difference per fraction in D95%, due to interfractional motion, was 2.5 ± 2.7 Gy, 3.0 ± 2.9 Gy, and 2.0 ± 1.8 Gy for the TVI, GTV, and DE-PTV, respectively. Conclusions Most patients require daily adaptation of the radiation planning process to maximally escalate delivered dose to the pancreatic tumor without exceeding OAR constraints. Using our automated approach, patients can receive higher target dose than standard of care without violating OAR constraints.
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Affiliation(s)
- Dong Joo Rhee
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sam Beddar
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joseph Abi Jaoude
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Gabriel Sawakuchi
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rachael Martin
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Luis Perles
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cenji Yu
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA
| | - Yulun He
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA
| | - Laurence E. Court
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ethan B. Ludmir
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Albert C. Koong
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prajnan Das
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Cullen Taniguichi
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joshua S. Niedzielski
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Corresponding author: Joshua S. Niedzielski, PhD
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Jhanwar G, Dahiya N, Ghahremani P, Zarepisheh M, Nadeem S. Domain knowledge driven 3D dose prediction using moment-based loss function. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac8d45] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/26/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. To propose a novel moment-based loss function for predicting 3D dose distribution for the challenging conventional lung intensity modulated radiation therapy plans. The moment-based loss function is convex and differentiable and can easily incorporate clinical dose volume histogram (DVH) domain knowledge in any deep learning (DL) framework without computational overhead. Approach. We used a large dataset of 360 (240 for training, 50 for validation and 70 for testing) conventional lung patients with 2 Gy × 30 fractions to train the DL model using clinically treated plans at our institution. We trained a UNet like convolutional neural network architecture using computed tomography, planning target volume and organ-at-risk contours as input to infer corresponding voxel-wise 3D dose distribution. We evaluated three different loss functions: (1) the popular mean absolute error (MAE) loss, (2) the recently developed MAE + DVH loss, and (3) the proposed MAE + moments loss. The quality of the predictions was compared using different DVH metrics as well as dose-score and DVH-score, recently introduced by the AAPM knowledge-based planning grand challenge. Main results. Model with (MAE + moment) loss function outperformed the model with MAE loss by significantly improving the DVH-score (11%, p < 0.01) while having similar computational cost. It also outperformed the model trained with (MAE + DVH) by significantly improving the computational cost (48%) and the DVH-score (8%, p < 0.01). Significance. DVH metrics are widely accepted evaluation criteria in the clinic. However, incorporating them into the 3D dose prediction model is challenging due to their non-convexity and non-differentiability. Moments provide a mathematically rigorous and computationally efficient way to incorporate DVH information in any DL architecture. The code, pretrained models, docker container, and Google Colab project along with a sample dataset are available on our DoseRTX GitHub (https://github.com/nadeemlab/DoseRTX)
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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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26
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Tudda A, Castriconi R, Benecchi G, Cagni E, Cicchetti A, Dusi F, Esposito PG, Guernieri M, Ianiro A, Landoni V, Mazzilli A, Moretti E, Oliviero C, Placidi L, Rambaldi Guidasci G, Rancati T, Scaggion A, Trojani V, Fiorino C. Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. Radiother Oncol 2022; 175:10-16. [PMID: 35868603 DOI: 10.1016/j.radonc.2022.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI). MATERIALS AND METHODS Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SDint of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model's geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes. RESULTS The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R2 = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible. CONCLUSIONS Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.
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Affiliation(s)
- Alessia Tudda
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy; Università Statale di Milano, Milano, Italy
| | | | | | - Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Francesca Dusi
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | | | - Marika Guernieri
- Department of Medical Physics, University Hospital, Udine, Italy
| | - Anna Ianiro
- Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | | | - Aldo Mazzilli
- Medical Physics Dept, University Hospital of Parma AOUP, Italy
| | - Eugenia Moretti
- Department of Medical Physics, University Hospital, Udine, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giulia Rambaldi Guidasci
- Amethyst Radioterapia Italia, Medical Physics Department, San Giovanni Calibita Fatebenefratelli Hospital, Rome, Italy
| | - Tiziana Rancati
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Valeria Trojani
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Claudio Fiorino
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy
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Douglas RJ, Olanrewaju A, Zhang L, Beadle BM, Court LE. Assessing the practicality of using a single knowledge‐based planning model for multiple linac vendors. J Appl Clin Med Phys 2022; 23:e13704. [PMID: 35791594 PMCID: PMC9359004 DOI: 10.1002/acm2.13704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 05/18/2022] [Accepted: 05/31/2022] [Indexed: 12/03/2022] Open
Abstract
Purpose Knowledge‐based planning (KBP) has been shown to be an effective tool in quality control for intensity‐modulated radiation therapy treatment planning and generating high‐quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. Materials and methods This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric‐modulated arc therapy plans for a cohort of 50 patients treated for head‐neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically‐acceptable baseline plans by a margin of 1.8 Gy or 3% dose‐volume. The quality of these plans were also compared through the use of common clinical dose‐volume histogram criteria. Results The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. Conclusions These results support the use of a head‐neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine‐tuning for targets may be necessary.
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Affiliation(s)
- Raphael J. Douglas
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Adenike Olanrewaju
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Lifei Zhang
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
| | - Beth M. Beadle
- Department of Radiation Oncology Stanford University Palo Alto California USA
| | - Laurence E. Court
- Department of Radiation Physics The University of Texas MD Anderson Cancer Center Houston Texas USA
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28
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Knowledge-based three-dimensional dose prediction for tandem-and-ovoid brachytherapy. Brachytherapy 2022; 21:532-542. [PMID: 35562285 DOI: 10.1016/j.brachy.2022.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 02/28/2022] [Accepted: 03/12/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator. METHODS A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean(SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram(DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%, and bladder and/or rectum and/or sigmoid D2cc) with ΔDx=Dx,actual-Dx,predicted Pearson correlation coefficient, standard deviation, and mean further quantifying model performance. RESULTS Ranges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯90±σΔD=-0.19 ± 0.55Gy(-0.09 ± 0.67 Gy), bladder ΔD¯2cc±σΔD= -0.06 ± 0.54Gy(-0.17 ± 0.67 Gy), rectum ΔD¯2cc±σΔD= -0.03 ± 0.36Gy(-0.04 ± 0.46 Gy), and sigmoid ΔD¯2cc±σΔD= -0.01 ± 0.34Gy(0.00 ± 0.44 Gy). CONCLUSIONS A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.
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Kaderka R, Liu KC, Liu L, VanderStraeten R, Liu TL, Lee KM, Tu YCE, MacEwan I, Simpson D, Urbanic J, Chang C. Toward automatic beam angle selection for pencil-beam scanning proton liver Treatments: A deep learning-based approach. Med Phys 2022; 49:4293-4304. [PMID: 35488864 DOI: 10.1002/mp.15676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Dose deposition characteristics of proton radiation can be advantageous over photons. Proton treatment planning however poses additional challenges for the planners. Proton therapy is usually delivered with only a small number of beam angles, and the quality of a proton treatment plan is largely determined by the beam angles employed. Finding the optimal beam angles for a proton treatment plan requires time and experience, motivating the investigation of automatic beam angle selection methods. PURPOSE A deep learning-based approach to automatic beam angle selection is proposed for proton pencil-beam scanning treatment planning of liver lesions. METHODS We cast beam-angle selection as a multi-label classification problem. To account for angular boundary discontinuity, the underlying convolution neural network is trained with the proposed Circular Earth Mover's Distance based regularization and multi-label circular-smooth label technique. Furthermore, an analytical algorithm emulating proton treatment planners' clinical practice is employed in post-processing to improve the output of the model. Forty-nine patients that received proton liver treatments between 2017 and 2020 were randomly divided into training (n = 31), validation (n = 7), and test sets (n = 11). AI-selected beam angles were compared with those angles selected by human planners, and the dosimetric outcome was investigated by creating plans using knowledge-based treatment planning. RESULTS For 7 of the 11 cases in the test set, AI-selected beam angles agreed with those chosen by human planners to within 20 degrees (median angle difference = 10°; mean = 18.6°). Moreover, out of the total 22 beam angles predicted by the model, 15 (68%) were within 10 degrees of the human-selected angles. The high correlation in beam angles resulted in comparable dosimetric statistics between proton treatment plans generated using AI- and human-selected angles. For the cases with beam angle differences exceeding 20°, the dosimetric analysis showed similar plan quality although with different emphases on organ-at-risk sparing. CONCLUSIONS This pilot study demonstrated the feasibility of a novel deep learning-based beam angle selection technique. Testing on liver cancer patients showed that the resulting plans were clinically viable with comparable dosimetric quality to those using human-selected beam angles. In tandem with auto-contouring and knowledge-based treatment planning tools, the proposed model could represent a pathway for nearly fully automated treatment planning in proton therapy. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Robert Kaderka
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,Department of Radiation Oncology, University of Miami, Miami, FL, 33136
| | | | - Lawrence Liu
- California Protons Cancer Therapy Center, San Diego, CA, 92121
| | | | | | | | | | - Iain MacEwan
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
| | - Daniel Simpson
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121
| | - James Urbanic
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
| | - Chang Chang
- Department of Radiation Medicine and Applied Sciences, University of California at San Diego, La Jolla, CA, 92121.,California Protons Cancer Therapy Center, San Diego, CA, 92121
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30
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van Gysen K, Kneebone A, Le A, Wu K, Haworth A, Bromley R, Hruby G, O'Toole J, Booth J, Brown C, Pearse M, Sidhom M, Wiltshire K, Tang C, Eade T. Evaluating the utility of knowledge-based planning for clinical trials using the TROG 08.03 post prostatectomy radiation therapy planning data. Phys Imaging Radiat Oncol 2022; 22:91-97. [PMID: 35602546 PMCID: PMC9117914 DOI: 10.1016/j.phro.2022.05.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/05/2022] [Accepted: 05/05/2022] [Indexed: 10/27/2022] Open
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31
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Böhmer D, Siegmann A, Scharl S, Ruf C, Wiegel T, Krafcsik M, Thamm R. Impact of Dose Escalation on the Efficacy of Salvage Radiotherapy for Recurrent Prostate Cancer-A Risk-Adjusted, Matched-Pair Analysis. Cancers (Basel) 2022; 14:cancers14051320. [PMID: 35267629 PMCID: PMC8909709 DOI: 10.3390/cancers14051320] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 02/28/2022] [Accepted: 03/02/2022] [Indexed: 12/10/2022] Open
Abstract
Previous randomized trials have not provided conclusive evidence about dose escalations and associated toxicities for salvage radiotherapy (SRT) in prostate cancer. Here, we retrospectively analyzed whether dose escalations influenced progression-free survival in 554 patients that received salvage radiotherapy for relapses or persistently elevated prostate cancer antigen (PSA) after a radical prostatectomy. Patients received SRT between 1997 and 2017 at two University Hospitals in Germany. We compared patient groups that received radiation doses <7000 cGy (n = 225) or ≥7000 cGy (n = 329) to analyze the influence of radiation dose on progression-free survival. In a second matched-pair analysis of 216 pairs, we evaluated prognostic factors (pT2 vs. pT3−4, Gleason score [GS] ≤ 7 vs. GS ≥ 8, R0 vs. R1, and pre-SRT PSA <0.5 vs. ≥0.5 ng/mL). After a median follow-up of 6.8 (4.2−9.2) years, we found that escalated doses significantly improved progression-free survival (p = 0.0042). A multivariate analysis indicated that an escalated dose, lower tumor stages (pT2 vs. pT3/4), and lower GSs (≤7 vs. 8−10) were associated with improved progression-free survival. There was no significant effect on overall survival. Our data suggested that escalating the radiation dose to ≥7000 cGy for SRT after a prostatectomy significantly improved progression-free survival. Longer follow-ups are needed for a comprehensive recommendation.
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Affiliation(s)
- Dirk Böhmer
- Department of Radiation Oncology, Charité University Medicine, Campus Benjamin Franklin, 12203 Berlin, Germany;
- Correspondence: ; Tel.: +49-30-450-627601
| | - Alessandra Siegmann
- Department of Radiation Oncology, Charité University Medicine, Campus Benjamin Franklin, 12203 Berlin, Germany;
| | - Sophia Scharl
- Department of Radiation Oncology, University Hospital Ulm, 89081 Ulm, Germany; (S.S.); (T.W.); (M.K.); (R.T.)
| | - Christian Ruf
- Department of Urology, Bundeswehrkrankenhaus Ulm, 89081 Ulm, Germany;
| | - Thomas Wiegel
- Department of Radiation Oncology, University Hospital Ulm, 89081 Ulm, Germany; (S.S.); (T.W.); (M.K.); (R.T.)
| | - Manuel Krafcsik
- Department of Radiation Oncology, University Hospital Ulm, 89081 Ulm, Germany; (S.S.); (T.W.); (M.K.); (R.T.)
| | - Reinhard Thamm
- Department of Radiation Oncology, University Hospital Ulm, 89081 Ulm, Germany; (S.S.); (T.W.); (M.K.); (R.T.)
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32
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Thomas C, Dregely I, Oksuz I, Urbano TG, Greener T, King AP, Barrington SF. Neural-network dose-prediction for rectal spacer stratification in dose-escalated prostate radiotherapy. Med Phys 2022; 49:2172-2182. [PMID: 35218024 PMCID: PMC9311720 DOI: 10.1002/mp.15575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/22/2022] [Accepted: 02/14/2022] [Indexed: 12/03/2022] Open
Abstract
Purpose To develop a knowledge‐based decision‐support system capable of stratifying patients for rectal spacer (RS) insertion based on neural network predicted rectal dose, reducing the need for time‐ and resource‐intensive radiotherapy (RT) planning. Methods Forty‐four patients treated for prostate cancer were enrolled into a clinical trial (NCT03238170). Dose‐escalated prostate RT plans were manually created for 30 patients with simulated boost volumes using a conventional treatment planning system (TPS) and used to train a hierarchically dense 3D convolutional neural network to rapidly predict RT dose distributions. The network was used to predict rectal doses for 14 unseen test patients, with associated toxicity risks calculated according to published data. All metrics obtained using the network were compared to conventionally planned values. Results The neural network stratified patients with an accuracy of 100% based on optimal rectal dose–volume histogram constraints and 78.6% based on mandatory constraints. The network predicted dose‐derived grade 2 rectal bleeding risk within 95% confidence limits of ‐1.9% to +1.7% of conventional risk estimates (risk range 3.5%–9.9%) and late grade 2 fecal incontinence risk within ‐0.8% to +1.5% (risk range 2.3%–5.7%). Prediction of high‐resolution 3D dose distributions took 0.7 s. Conclusions The feasibility of using a neural network to provide rapid decision support for RS insertion prior to RT has been demonstrated, and the potential for time and resource savings highlighted. Directly after target and healthy tissue delineation, the network is able to (i) risk stratify most patients with a high degree of accuracy to prioritize which patients would likely derive greatest benefit from RS insertion and (ii) identify patients close to the stratification threshold who would require conventional planning.
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Affiliation(s)
- Christopher Thomas
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,Medical Physics Department, Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Isabel Dregely
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,Computer Science, UAS Technikum Wien, Vienna, Austria
| | - Ilkay Oksuz
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | | | - Tony Greener
- Medical Physics Department, Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Andrew P King
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sally F Barrington
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.,King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
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33
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Adherence to contouring and treatment planning requirements within a multicentric trial -results of the quality assurance of the SAKK 09/10 trial. Int J Radiat Oncol Biol Phys 2022; 113:80-91. [PMID: 34990777 DOI: 10.1016/j.ijrobp.2021.12.174] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE To evaluate the results of the radiation therapy (RT) quality assurance (QA) program of the phase III randomized "XXXX-Anonymized for Review" trial in biochemically recurrent prostate cancer (PC) patients after prostatectomy. METHODS AND MATERIALS Within the "XXXX-Anonymized for Review" trial testing 64Gy versus 70Gy salvage RT, a central collection of treatment plans were performed, which were thoroughly reviewed by a dedicated medical physicist and radiation oncologist. Adherence to the treatment protocol and specifically to the European Organization for the Research and Treatment of Cancer (EORTC) guidelines for target volume definition (classified as deviation observed yes vs. no) and its potential correlation with acute and late toxicity (Common Terminology Criteria for Adverse Events (CTCAE) v4.0) and freedom from biochemical progression (FFBP) were investigated. RESULTS The treatment plans of 344 patients treated between February 2011 and April 2014 depicted important deviations to the EORTC guidelines and to the recommendations per trial protocol. For example, in up to half of the cases, the delineated structures deviated from the protocol (e.g., prostate bed (PB) in 48.8%, rectal wall (RW) in 41%). In addition, variations in clinical (CTV) - and planning target volume (PTV) occurred frequently (e.g., CTV and PTV deviations in up to 42.4% and 25.9%, respectively). The detected deviations showed a significant association with a lower risk of grade ≥ 2 gastrointestinal (GI) acute toxicity when CTV not overlapped RW vs. CTV overlapping RW, (OR 0.43; CI [0.22, 0.85]; p= 0.014), and a higher rate of grade ≥ 2 late genitourinary (GU) toxicity in case of the CTV overlapped with RW, (OR 2.58; CI [1.17, 5.72]; p= 0.019). A marginally significant lower risk of grade ≥ 2 late GU toxicity in patients when PB not overlapping RW versus overlapping RW was observed (OR 0.51; CI [0.25, 1.03]; p= 0.06). In addition, a marginally significant decrease of FFBP in patients with PTV not including surgical clips as potential markers of the limits of the prostate bed, (HR 1.44; CI [0.96, 2.17]; p= 0.07) was observed. CONCLUSIONS Despite a thorough QA program, the central review of a phase-III trial showed limited adherence to treatment protocol recommendations which was associated with a higher risk of toxicity by means of acute or late GI or GU toxicity and showed a trend towards worse FFBP. Data from this QA review may help refine future QA programs and prostate bed delineation guidelines.
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Hedrick SG, Petro S, Ward A, Morris B. Validation of automated complex head and neck treatment planning with pencil beam scanning proton therapy. J Appl Clin Med Phys 2021; 23:e13510. [PMID: 34936205 PMCID: PMC8833278 DOI: 10.1002/acm2.13510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 11/30/2022] Open
Abstract
Background Pencil beam scanning (PBS) proton therapy offers dosimetric advantages for several treatment sites, including head and neck (H&N). However, to achieve the optimal target coverage and robustness, these plans can be complex and time consuming to develop and optimize. Automating the treatment planning process can ensure a high‐quality and standardized plan, reduce burden to the planner, and decrease time‐to‐treatment. We utilized in‐house scripting to automate a four‐field multi‐field optimization (MFO) H&N planning technique. Methods and materials Ten bilateral H&N patients were planned in RayStation v6 with a four‐field modified‐X beam configuration using MFO planning. Automation included creation of avoidance structures to control spot placement and development of standardized beams, PBS spot settings, robust optimization objectives, and patient‐specific predicted planning constraints. Each patient was planned both with and without automation to evaluate differences in planning time, perceived effort and plan quality, plan robustness, and OAR sparing. Results On average, scripted plans required 3.2 h, compared to 4.3 h without the script. There was no difference in target coverage or plan robustness with or without automation. Automation significantly reduced mean dose to the oral cavity, parotids, esophagus, trachea, and larynx. Perceived effort was scaled from 1 (minimum effort) to 100 (maximum effort), and automation reduced perceived effort by 42% (p < 0.05). Two non‐scripted plans required re‐planning due to errors. Conclusions Automation of this multi‐beam, the MFO proton planning process reduced planning time and improved OAR sparing compared to the same planning process without scripting. Scripting generation of complex structures and planning objectives reduced burden on the planner. With most current treatment planning software, this automation is simple to implement and can standardize quality of care across all treatment planners.
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Affiliation(s)
| | - Scott Petro
- Provision CARES Proton Therapy Center, Knoxville, Tennessee, USA
| | - Alex Ward
- Provision CARES Proton Therapy Center, Knoxville, Tennessee, USA
| | - Bart Morris
- Provision CARES Proton Therapy Center, Knoxville, Tennessee, USA
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Dose Prediction Models Based on Geometric and Plan Optimization Parameter for Adjuvant Radiotherapy Planning Design in Cervical Cancer Radiotherapy. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:7026098. [PMID: 34804459 PMCID: PMC8604605 DOI: 10.1155/2021/7026098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/16/2021] [Indexed: 11/18/2022]
Abstract
The prediction of an additional space for the dose sparing of organs at risk (OAR) in radiotherapy is still difficult. In this pursuit, the present study was envisaged to find out the factors affecting the bladder and rectum dosimetry of cervical cancer. Additionally, the relationship between the dose-volume histogram (DVH) parameters and the geometry and plan dose-volume optimization parameters of the bladder/rectum was established to develop the dose prediction models and guide the planning design for lower OARs dose coverage directly. Thirty volume modulated radiation therapy (VMAT) plans from cervical cancer patients were randomly chosen to build the dose prediction models. The target dose coverage was evaluated. Dose prediction models were established by univariate and multiple linear regression among the dosimetric parameters of the bladder/rectum, the geometry parameters (planning target volume (PTV), volume of bladder/rectum, overlap volume of bladder/rectum (OV), and overlapped volume as a percentage of bladder/rectum volume (OP)), and corresponding plan dose-volume optimization parameters of the nonoverlapping structures (the structure of bladder/rectum outside the PTV (NOS)). Finally, the accuracy of the prediction models was evaluated by tracking d = (predicted dose-actual dose)/actual in additional ten VMAT plans. V 30, V 35, and V 40 of the bladder and rectum were found to be multiple linearly correlated with the relevant OP and corresponding dose-volume optimization parameters of NOS (regression R 2 > 0.99, P < 0.001). The variations of these models were less than 0.5% for bladder and rectum. Percentage of bladder and rectum within the PTV and the dose-volume optimization parameters of NOS could be used to predict the dose quantitatively. The parameters of NOS as a limited condition could be used in the plan optimization instead of limiting the dose and volume of the entire OAR traditionally, which made the plan optimization more unified and convenient and strengthened the plan quality and consistency.
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Kallis K, Mayadev J, Kisling K, Brown D, Scanderbeg D, Ray X, Cortes K, Simon A, Yashar CM, Einck JP, Mell LK, Moore KL, Meyers SM. Knowledge-based dose prediction models to inform gynecologic brachytherapy needle supplementation for locally advanced cervical cancer. Brachytherapy 2021; 20:1187-1199. [PMID: 34393059 DOI: 10.1016/j.brachy.2021.07.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/16/2021] [Accepted: 07/01/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The use of interstitial needles, combined with intracavitary applicators, enables customized dose distributions and is beneficial for complex cases, but increases procedure time. Overall, applicator selection is not standardized and depends on physician expertise and preference. The purpose of this study is to determine whether dose prediction models can guide needle supplementation decision-making for cervical cancer. MATERIALS AND METHODS Intracavitary knowledge-based models for organ-at-risk (OAR) dose estimation were trained and validated for tandem-and-ring/ovoids (T&R/T&O) implants. Models were applied to hybrid cases with 1-3 implanted needles to predict OAR dose without needles. As a reference, 70/67 hybrid T&R/T&O cases were replanned without needles, following a standardized procedure guided by dose predictions. If a replanned dose exceeded the dose objective, the case was categorized as requiring needles. Receiver operating characteristic (ROC) curves of needle classification accuracy were generated. Optimal classification thresholds were determined from the Youden Index. RESULTS Needle supplementation reduced dose to OARs. However, 67%/39% of replans for T&R/T&O met all dose constraints without needles. The ROC for T&R/T&O models had an area-under-curve of 0.89/0.86, proving high classification accuracy. The optimal threshold of 99%/101% of the dose limit for T&R/T&O resulted in classification sensitivity and specificity of 78%/86% and 85%/78%. CONCLUSIONS Needle supplementation reduced OAR dose for most cases but was not always required to meet standard dose objectives, particularly for T&R cases. Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection.
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Affiliation(s)
- Karoline Kallis
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Jyoti Mayadev
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Kelly Kisling
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Derek Brown
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Daniel Scanderbeg
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Xenia Ray
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Katherina Cortes
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Aaron Simon
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Catheryn M Yashar
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - John P Einck
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Loren K Mell
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Kevin L Moore
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA
| | - Sandra M Meyers
- Department of Radiation Medicine & Applied Sciences, UC San Diego Health, San Diego, CA.
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Yusufaly TI, Meyers SM, Mell LK, Moore KL. Knowledge-Based Planning for Intact Cervical Cancer. Semin Radiat Oncol 2021; 30:328-339. [PMID: 32828388 DOI: 10.1016/j.semradonc.2020.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cervical cancer radiotherapy is often complicated by significant variability in the quality and consistency of treatment plans. Knowledge-based planning (KBP), which utilizes prior patient data to correlated achievable optimal dosimetry with patient-specific anatomy, has demonstrated promise as a quality control tool for controlling this variability, with consequences for patient outcomes, as well as for the reliability of data from multi-institutional clinical trials. In this article we highlight the application of KBP-based quality control to cervical cancer radiotherapy. We discuss the potential impact of KBP on multi-institutional clinical trials to standardize cervical cancer treatment planning across diverse clinics, and discuss challenges and progress in the implementation of KBP for brachytherapy treatment planning. Additionally, we briefly discuss secondary applications of KBP for cervical cancer. The emerging picture from these studies indicates several exciting opportunities for increasing the utilization of KBP in day-to-day cervical cancer radiotherapy.
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Affiliation(s)
- Tahir I Yusufaly
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA
| | - Sandra M Meyers
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA
| | - Loren K Mell
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA
| | - Kevin L Moore
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA.
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Geng H, Giaddui T, Cheng C, Zhong H, Ryu S, Liao Z, Yin FF, Gillin M, Mohan R, Xiao Y. A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials. J Appl Clin Med Phys 2021; 22:329-337. [PMID: 34432946 PMCID: PMC8504592 DOI: 10.1002/acm2.13401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 06/28/2021] [Accepted: 08/04/2021] [Indexed: 12/25/2022] Open
Abstract
Background and purpose The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient‐specific anatomy. This study examined these two techniques for dose‐volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge‐based planning (KBP) technique and another first principle (FP) technique. Methods This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi‐institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. Results For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re‐optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ‐at‐risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. Conclusions Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.
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Affiliation(s)
- Huaizhi Geng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Tawfik Giaddui
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chingyun Cheng
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Haoyu Zhong
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samuel Ryu
- Stony Brook University Medical Center, Stony Brook, New York, USA
| | | | - Fang-Fang Yin
- Duke University Medical Center, Durham, North Carolina, USA
| | | | - Radhe Mohan
- MD Anderson Cancer Center, Houston, Texas, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Covele BM, Puri KS, Kallis K, Murphy JD, Moore KL. ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning. JCO Clin Cancer Inform 2021; 5:134-142. [PMID: 33513032 DOI: 10.1200/cci.20.00093] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Access to knowledge-based treatment plan quality control has been hindered by the complexity of developing models and integration with different treatment planning systems (TPS). Online Real-time Benchmarking Information Technology for RadioTherapy (ORBIT-RT) provides a free, web-based platform for knowledge-based dose estimation that can be used by clinicians worldwide to benchmark the quality of their radiotherapy plans. MATERIALS AND METHODS The ORBIT-RT platform was developed to satisfy four primary design criteria: web-based access, TPS independence, Health Insurance Portability and Accountability Act compliance, and autonomous operation. ORBIT-RT uses a cloud-based server to automatically anonymize a user's Digital Imaging and Communications in Medicine for RadioTherapy (DICOM-RT) file before upload and processing of the case. From there, ORBIT-RT uses established knowledge-based dose-volume histogram (DVH) estimation methods to autonomously create DVH estimations for the uploaded DICOM-RT. ORBIT-RT performance was evaluated with an independent validation set of 45 volumetric modulated arc therapy prostate plans with two key metrics: (i) accuracy of the DVH estimations, as quantified by their error, DVHclinical - DVHprediction and (ii) time to process and display the DVH estimations on the ORBIT-RT platform. RESULTS ORBIT-RT organ DVH predictions show < 1% bias and 3% error uncertainty at doses > 80% of prescription for the prostate validation set. The ORBIT-RT extensions require 3.0 seconds per organ to analyze. The DICOM upload, data transfer, and DVH output display extend the entire system workflow to 2.5-3 minutes. CONCLUSION ORBIT-RT demonstrated fast and fully autonomous knowledge-based feedback on a web-based platform that takes only anonymized DICOM-RT as input. The ORBIT-RT system can be used for real-time quality control feedback that provides users with objective comparisons for final plan DVHs.
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Hardcastle N, Cook O, Ray X, Moore A, Moore KL, Pryor D, Rossi A, Foroudi F, Kron T, Siva S. Personalising treatment plan quality review with knowledge-based planning in the TROG 15.03 trial for stereotactic ablative body radiotherapy in primary kidney cancer. Radiat Oncol 2021; 16:142. [PMID: 34344402 PMCID: PMC8330099 DOI: 10.1186/s13014-021-01820-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/12/2021] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Quality assurance (QA) of treatment plans in clinical trials improves protocol compliance and patient outcomes. Retrospective use of knowledge-based-planning (KBP) in clinical trials has demonstrated improved treatment plan quality and consistency. We report the results of prospective use of KBP for real-time QA of treatment plan quality in the TROG 15.03 FASTRACK II trial, which evaluates efficacy of stereotactic ablative body radiotherapy (SABR) for kidney cancer. METHODS A KBP model was generated based on single institution data. For each patient in the KBP phase (open to the last 31 patients in the trial), the treating centre submitted treatment plans 7 days prior to treatment. A treatment plan was created by using the KBP model, which was compared with the submitted plan for each organ-at-risk (OAR) dose constraint. A report comparing each plan for each OAR constraint was provided to the submitting centre within 24 h of receiving the plan. The centre could then modify the plan based on the KBP report, or continue with the existing plan. RESULTS Real-time feedback using KBP was provided in 24/31 cases. Consistent plan quality was in general achieved between KBP and the submitted plan. KBP review resulted in replan and improvement of OAR dosimetry in two patients. All centres indicated that the feedback was a useful QA check of their treatment plan. CONCLUSION KBP for real-time treatment plan review was feasible for 24/31 cases, and demonstrated ability to improve treatment plan quality in two cases. Challenges include integration of KBP feedback into clinical timelines, interpretation of KBP results with respect to clinical trade-offs, and determination of appropriate plan quality improvement criteria.
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Affiliation(s)
- Nicholas Hardcastle
- Physical Sciences, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia. .,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia. .,Department of Oncology, Sir Peter MacCallum, University of Melbourne, Parkville, Australia.
| | - Olivia Cook
- Trans Tasman Radiation Oncology Group, Newcastle, Australia
| | - Xenia Ray
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, USA
| | - Alisha Moore
- Trans Tasman Radiation Oncology Group, Newcastle, Australia
| | - Kevin L Moore
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, USA
| | - David Pryor
- Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia
| | - Alana Rossi
- Trans Tasman Radiation Oncology Group, Newcastle, Australia
| | - Farshad Foroudi
- Olivia Newton, John Cancer Centre at Austin Health, Heidelberg, Australia
| | - Tomas Kron
- Physical Sciences, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia.,Department of Oncology, Sir Peter MacCallum, University of Melbourne, Parkville, Australia
| | - Shankar Siva
- Department of Oncology, Sir Peter MacCallum, University of Melbourne, Parkville, Australia.,Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
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Kaderka R, Hild SJ, Bry VN, Cornell M, Ray XJ, Murphy JD, Atwood TF, Moore KL. Wide-Scale Clinical Implementation of Knowledge-Based Planning: An Investigation of Workforce Efficiency, Need for Post-automation Refinement, and Data-Driven Model Maintenance. Int J Radiat Oncol Biol Phys 2021; 111:705-715. [PMID: 34217788 DOI: 10.1016/j.ijrobp.2021.06.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/05/2021] [Accepted: 06/17/2021] [Indexed: 11/19/2022]
Abstract
PURPOSE Our purpose was to investigate the effect of automated knowledge-based planning (KBP) on real-world clinical workflow efficiency, assess whether manual refinement of KBP plans improves plan quality across multiple disease sites, and develop a data-driven method to periodically improve KBP automated planning routines. METHODS AND MATERIALS Using clinical knowledge-based automated planning routines for prostate, prostatic fossa, head and neck, and hypofractionated lung disease sites in a commercial KBP solution, workflow efficiency was compared in terms of planning time in a pre-KBP (n = 145 plans) and post-KBP (n = 503) patient cohort. Post-KBP, planning was initialized with KBP (KBP-only) and subsequently manually refined (KBP + human). Differences in planning time were tested for significance using a 2-tailed Mann-Whitney U test (P < .05, null hypothesis: planning time unchanged). Post-refinement plan quality was assessed using site-specific dosimetric parameters of the original KBP-only plan versus KBP + human; 2-tailed paired t test quantified statistical significance (Bonferroni-corrected P < .05, null hypothesis: no dosimetric difference after refinement). If KBP + human significantly improved plans across the cohort, optimization objectives were changed to create an updated KBP routine (KBP'). Patients were replanned with KBP' and plan quality was compared with KBP + human as described previously. RESULTS KBP significantly reduced planning time in all disease sites: prostate (median: 7.6 hrs → 2.1 hrs; P < .001), prostatic fossa (11.1 hrs → 3.7 hrs; P = .001), lung (9.9 hrs → 2.0 hrs; P < .001), and head and neck (12.9 hrs → 3.5 hrs; P <.001). In prostate, prostatic fossa, and lung disease sites, organ-at-risk dose changes in KBP + human versus KBP-only were minimal (<1% prescription dose). In head and neck, KBP + human did achieve clinically relevant dose reductions in some parameters. The head and neck routine was updated (KBP'HN) to incorporate dose improvements from manual refinement. The only significant dosimetric differences to KBP + human after replanning with KBP'HN were in favor of the new routine. CONCLUSIONS KBP increased clinical efficiency by significantly reducing planning time. On average, human refinement offered minimal dose improvements over KBP-only plans. In the single disease site where KBP + human was superior to KBP-only, differences were eliminated by adjusting optimization parameters in a revised KBP routine.
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Affiliation(s)
- Robert Kaderka
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Sebastian J Hild
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Victoria N Bry
- Department of Radiation Oncology, School of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Mariel Cornell
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Xenia J Ray
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - James D Murphy
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Todd F Atwood
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Kevin L Moore
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
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Tambas M, van der Laan HP, Rutgers W, van den Hoek JG, Oldehinkel E, Meijer TW, van der Schaaf A, Scandurra D, Free J, Both S, Steenbakkers RJ, Langendijk JA. Development of advanced preselection tools to reduce redundant plan comparisons in model-based selection of head and neck cancer patients for proton therapy. Radiother Oncol 2021; 160:61-68. [DOI: 10.1016/j.radonc.2021.04.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 04/06/2021] [Accepted: 04/09/2021] [Indexed: 12/27/2022]
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Cilla S, Romano C, Morabito VE, Macchia G, Buwenge M, Dinapoli N, Indovina L, Strigari L, Morganti AG, Valentini V, Deodato F. Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study. Front Oncol 2021; 11:636529. [PMID: 34141608 PMCID: PMC8204695 DOI: 10.3389/fonc.2021.636529] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/24/2021] [Indexed: 01/08/2023] Open
Abstract
Background In radiation oncology, automation of treatment planning has reported the potential to improve plan quality and increase planning efficiency. We performed a comprehensive dosimetric evaluation of the new Personalized algorithm implemented in Pinnacle3 for full planning automation of VMAT prostate cancer treatments. Material and Methods Thirteen low-risk prostate (without lymph-nodes irradiation) and 13 high-risk prostate (with lymph-nodes irradiation) treatments were retrospectively taken from our clinical database and re-optimized using two different automated engines implemented in the Pinnacle treatment system. These two automated engines, the currently used Autoplanning and the new Personalized are both template-based algorithms that use a wish-list to formulate the planning goals and an iterative approach able to mimic the planning procedure usually adopted by experienced planners. In addition, the new Personalized module integrates a new engine, the Feasibility module, able to generate an “a priori” DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually generated (MP) and automated plans generated with both Autoplanning (AP) and Personalized engines (Pers) were performed using dose-volume histogram metrics and conformity indexes. Three different normal tissue complication probabilities (NTCPs) models were used for rectal toxicity evaluation. The planning efficiency and the accuracy of dose delivery were assessed for all plans. Results For similar targets coverage, Pers plans reported a significant increase of dose conformity and less irradiation of healthy tissue, with significant dose reduction for rectum, bladder, and femurs. On average, Pers plans decreased rectal mean dose by 11.3 and 8.3 Gy for low-risk and high-risk cohorts, respectively. Similarly, the Pers plans decreased the bladder mean doses by 7.3 and 7.6 Gy for low-risk and high-risk cohorts, respectively. The integral dose was reduced by 11–16% with respect to MP plans. Overall planning times were dramatically reduced to about 7 and 15 min for Pers plans. Despite the increased complexity, all plans passed the 3%/2 mm γ-analysis for dose verification. Conclusions The Personalized engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues for prostate cancer patients. The Feasibility “a priori” DVH prediction module provided OARs dose sparing well beyond the clinical objectives. The new Pinnacle Personalized algorithms outperformed the currently used Autoplanning ones as solution for treatment planning automation.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Vittoria E Morabito
- Medical Physics Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,DIMES, Alma Mater Studiorum Bologna University, Bologna, Italy
| | - Nicola Dinapoli
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Indovina
- Medical Physics Unit, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lidia Strigari
- Medical Physics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - Alessio G Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.,DIMES, Alma Mater Studiorum Bologna University, Bologna, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli-Università Cattolica del Sacro Cuore, Rome, Italy.,Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital-Università Cattolica del Sacro Cuore, Campobasso, Italy.,Istituto di Radiologia, Università Cattolica del Sacro Cuore, Rome, Italy
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Costa E, Richir T, Robilliard M, Bragard C, Logerot C, Kirova Y, Fourquet A, De Marzi L. Assessment of a conventional volumetric-modulated arc therapy knowledge-based planning model applied to the new Halcyon© O-ring linac in locoregional breast cancer radiotherapy. Phys Med 2021; 86:32-43. [PMID: 34051551 DOI: 10.1016/j.ejmp.2021.05.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 03/31/2021] [Accepted: 05/13/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION The aim of this study was to evaluate the performance of a knowledge-based planning (KBP) model for breast cancer trained on plans performed on a conventional linac with 6 MV FF (flattening filter) beams and volumetric-modulated arc therapy (VMAT) for plans performed on the new jawless Halcyon© system with 6 MV FFF (flattening filter-free) beams. MATERIALS AND METHODS Based on the RapidPlan© (RP) KBP optimization engine, a DVH Estimation Model was first trained using 56 VMAT left-sided breast cancer treatment plans performed on a conventional linac, and validated on another 20 similar cases (without manual intervention). To determine the capacity of the model for Halcyon©, an additional cohort of 20 left-sided breast cancer plans was generated with RP and analyzed for both TrueBeam© and Halcyon© machines. Plan qualities between manual vs RP (followed by manual intervention) Halcyon© plans set were compared qualitatively by blinded review by radiation oncologists for 10 new independent plans. RESULTS Halcyon© plans generated with the VMAT model trained with conventional linac plans showed comparable target dose distribution compared to TrueBeam© plans. Organ sparingwas comparable between the 2 devices with a slight decrease in heart dose for Halcyon© plans. Nine out of ten automatically generated Halcyon© plans were preferentially chosen by the radiation oncologists over the manually generated Halcyon© plans. CONCLUSION A VMAT KBP model driven by plans performed on a conventional linac with 6 MV FF beams provides high quality plans performed with 6 MV FFF beams on the new Halcyon© linac.
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Affiliation(s)
- Emilie Costa
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France.
| | - Thomas Richir
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Magalie Robilliard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christel Bragard
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Christelle Logerot
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Youlia Kirova
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Alain Fourquet
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France
| | - Ludovic De Marzi
- Institut Curie, Radiation Oncology Department, 26 rue d'Ulm, Paris 75005, France; Institut Curie, University Paris Saclay, PSL Research University, Inserm LITO, Orsay, France
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Shao Y, Zhang X, Wu G, Gu Q, Wang J, Ying Y, Feng A, Xie G, Kong Q, Xu Z. Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients With Asymmetric Network. IEEE J Biomed Health Inform 2021; 25:1120-1127. [PMID: 32966222 DOI: 10.1109/jbhi.2020.3025712] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric, able to preserve input information and to adapt the limitation of GPU memory. Squeeze and excitation (SE) units are used to improve the data-fitting ability. A loss function involving both the dose distribution and prescription dose as ground truth are designed. In the experiment, A-Net is separately trained and tested in the 50Gy and 60Gy dataset and most of the metrics A-Net achieve similar performance as HD-Unet and 3D-Unet, and some metrics slightly better. In the 50Gy-and-60Gy-combined dataset, most of the A-Net's metrics perform better than the other two. In conclusion, A-Net can accurately predict the IMRT dose distribution in the three datasets of 50Gy and 50Gy-and-60Gy-combined dataset.
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Wall PDH, Fontenot JD. Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning. Phys Med 2021; 87:136-143. [PMID: 33775567 DOI: 10.1016/j.ejmp.2021.03.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 03/01/2021] [Accepted: 03/09/2021] [Indexed: 10/21/2022] Open
Abstract
INTRODUCTION Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique. MATERIALS AND METHODS A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed. RESULTS Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum). CONCLUSION This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.
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Affiliation(s)
- Phillip D H Wall
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA.
| | - Jonas D Fontenot
- Department of Physics and Astronomy, Louisiana State University and Agricultural and Mechanical College, 202 Tower Drive, Baton Rouge, LA 70803-4001, USA; Department of Physics, Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA 70809, USA
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Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance. Phys Med 2021; 83:52-63. [DOI: 10.1016/j.ejmp.2021.02.026] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 02/15/2021] [Accepted: 02/24/2021] [Indexed: 12/15/2022] Open
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Covele BM, Carroll CJ, Moore KL. A practical method to quantify knowledge-based DVH prediction accuracy and uncertainty with reference cohorts. J Appl Clin Med Phys 2021; 22:279-284. [PMID: 33634947 PMCID: PMC7984487 DOI: 10.1002/acm2.13199] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/13/2021] [Accepted: 01/21/2021] [Indexed: 11/05/2022] Open
Abstract
The adoption of knowledge-based dose-volume histogram (DVH) prediction models for assessing organ-at-risk (OAR) sparing in radiotherapy necessitates quantification of prediction accuracy and uncertainty. Moreover, DVH prediction error bands should be readily interpretable as confidence intervals in which to find a percentage of clinically acceptable DVHs. In the event such DVH error bands are not available, we present an independent error quantification methodology using a local reference cohort of high-quality treatment plans, and apply it to two DVH prediction models, ORBIT-RT and RapidPlan, trained on the same set of 90 volumetric modulated arc therapy (VMAT) plans. Organ-at-risk DVH predictions from each model were then generated for a separate set of 45 prostate VMAT plans. Dose-volume histogram predictions were then compared to their analogous clinical DVHs to define prediction errorsV c l i n , i - V p r e d , i (ith plan), from which prediction bias μ, prediction error variation σ, and root-mean-square error R M S E pred ≡ 1 N ∑ i V c l i n , i - V p r e d , i 2 ≅ σ 2 + μ 2 could be calculated for the cohort. The empirical R M S E pred was then contrasted to the model-provided DVH error estimates. For all prostate OARs, above 50% Rx dose, ORBIT-RT μ and σ were comparable to or less than those of RapidPlan. Above 80% Rx dose, μ < 1% and σ < 3-4% for both models. As a result, above 50% Rx dose, ORBIT-RT R M S E pred was below that of RapidPlan, indicating slightly improved accuracy in this cohort. Because μ ≈ 0, R M S E pred is readily interpretable as a canonical standard deviation σ, whose error band is expected to correctly predict 68% of normally distributed clinical DVHs. By contrast, RapidPlan's provided error band, although described in literature as a standard deviation range, was slightly less predictive than R M S E pred (55-70% success), while the provided ORBIT-RT error band was confirmed to resemble an interquartile range (40-65% success) as described. Clinicians can apply this methodology using their own institutions' reference cohorts to (a) independently assess a knowledge-based model's predictive accuracy of local treatment plans, and (b) interpret from any error band whether further OAR dose sparing is likely attainable.
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Affiliation(s)
- Brent M. Covele
- Radiation Medicine and Applied SciencesUniversity of California – San DiegoLa JollaCAUSA
| | - Cody J. Carroll
- Department of StatisticsUniversity of California – DavisDavisCAUSA
| | - Kevin L. Moore
- Radiation Medicine and Applied SciencesUniversity of California – San DiegoLa JollaCAUSA
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Cilla S, Deodato F, Romano C, Ianiro A, Macchia G, Re A, Buwenge M, Boldrini L, Indovina L, Valentini V, Morganti AG. Personalized automation of treatment planning in head-neck cancer: A step forward for quality in radiation therapy? Phys Med 2021; 82:7-16. [PMID: 33508633 DOI: 10.1016/j.ejmp.2020.12.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/04/2020] [Accepted: 12/19/2020] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To perform a comprehensive dosimetric and clinical evaluation of the new Pinnacle Personalized automated planning system for complex head-and-neck treatments. METHODS Fifteen consecutive head-neck patients were enrolled. Radiotherapy was prescribed using VMAT with simultaneous integrated boost strategy. Personalized planning integrates the Feasibility engine able to supply an "a priori" DVH prediction of the achievability of planning goals. Comparison between clinically accepted manually-generated (MP) and automated (AP) plans was performed using dose-volume histograms and a blinded clinical evaluation by two radiation oncologists. Planning time between MP and AP was compared. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. RESULTS For similar targets coverage, AP plans reported less irradiation of healthy tissue, with significant dose reduction for spinal cord, brainstem and parotids. On average, the mean dose to parotids and maximal doses to spinal cord and brainstem were reduced by 13-15% (p < 0.001), 9% (p < 0.001) and 16% (p < 0.001), respectively. The integral dose was reduced by 16% (p < 0.001). The dose conformity for the three PTVs was significantly higher with AP plans (p < 0.001). The two oncologists chose AP plans in more than 80% of cases. Overall planning times were reduced to <30 min for automated optimization. All AP plans passed the 3%/2 mm γ-analysis by more than 95%. CONCLUSION Complex head-neck plans created using Personalized automated engine provided an overall increase of plan quality, in terms of dose conformity and sparing of normal tissues. The Feasibility module allowed OARs dose sparing well beyond the clinical objectives.
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Affiliation(s)
- Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy.
| | - Francesco Deodato
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Carmela Romano
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Anna Ianiro
- Medical Physics Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Gabriella Macchia
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Alessia Re
- Radiation Oncology Unit, Gemelli Molise Hospital - Università Cattolica del Sacro Cuore, Campobasso, Italy
| | - Milly Buwenge
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
| | - Luca Boldrini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Luca Indovina
- Medical Physics Unit, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Vincenzo Valentini
- Radiation Oncology Department, Fondazione Policlinico Universitario A. Gemelli - Università Cattolica del Sacro Cuore, Roma, Italy
| | - Alessio G Morganti
- Radiation Oncology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Italy; DIMES, Alma Mater Studiorum Bologna University, Italy
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Rago M, Placidi L, Polsoni M, Rambaldi G, Cusumano D, Greco F, Indovina L, Menna S, Placidi E, Stimato G, Teodoli S, Mattiucci GC, Chiesa S, Marazzi F, Masiello V, Valentini V, De Spirito M, Azario L. Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions. PLoS One 2021; 16:e0245305. [PMID: 33449952 PMCID: PMC7810311 DOI: 10.1371/journal.pone.0245305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 12/24/2020] [Indexed: 11/29/2022] Open
Abstract
PURPOSE To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions. METHODS AND MATERIALS Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures. RESULTS All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average. CONCLUSIONS All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.
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Affiliation(s)
- Maria Rago
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Lorenzo Placidi
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Mattia Polsoni
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Giulia Rambaldi
- Fatebenefratelli Isola Tiberina, Ospedale San Giovanni Calibita, Rome, Italy
- Amethyst Radioterapia Italia, Isola Tiberina, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Francesca Greco
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Sebastiano Menna
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Elisa Placidi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Stefania Teodoli
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Silvia Chiesa
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Fabio Marazzi
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valeria Masiello
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Marco De Spirito
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Luigi Azario
- Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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