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Lagedamon V, Leni PE, Gschwind R. Deep learning applied to dose prediction in external radiation therapy: A narrative review. Cancer Radiother 2024; 28:402-414. [PMID: 39138047 DOI: 10.1016/j.canrad.2024.03.005] [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: 02/14/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 08/15/2024]
Abstract
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.
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Affiliation(s)
- V Lagedamon
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France
| | - P-E Leni
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France.
| | - R Gschwind
- Laboratoire chronoenvironnement, UMR 6249, université de Franche-Comté, CNRS, 4, place Tharradin, 25200 Montbéliard, France
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Gambetta V, Fredriksson A, Menkel S, Richter C, Stützer K. The partial adaptation strategy for online-adaptive proton therapy: A proof of concept study in head and neck cancer patients. Med Phys 2024; 51:5572-5581. [PMID: 38837396 DOI: 10.1002/mp.17178] [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: 11/22/2023] [Revised: 03/06/2024] [Accepted: 04/08/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The accuracy of intensity-modulated proton therapy (IMPT) is greatly affected by anatomy variations that might occur during the treatment course. Online plan adaptations have been proposed as a solution to intervene promptly during a treatment session once the anatomy changes are detected. The implementation of online-adaptive proton therapy (OAPT) is still hindered by time-consuming tasks in the workflow. PURPOSE The study introduces the novel concept of partial adaptation and aims at investigating its feasibility as a potential solution to parallelize tasks during an OAPT workflow for saving valuable in-room time. METHODS The proof-of-principle simulation study includes datasets from six head and neck cancer (HNC) patients, each consisting of one planning CT (pCT) and three contoured control CTs (cCTs). Robust 3-field normo-fractionated initial IMPT plans were generated on the pCTs with a standardized field configuration, delivering 66 Gy and 54 Gy to the high-risk and low-risk clinical target volume (CTVHigh and CTVLow), respectively. For each cCT, a dose-mimicking-based partial adaptation was applied: two fields were adapted on the current anatomy taking into account the background dose of the first non-adapted field supposedly delivered in the meantime. Fraction doses on the cCTs resulting from partially adapted plans with different first (non-adapted) field assignments were compared against those from non-adapted and fully adapted plans regarding target coverage and organs at risk (OARs) sparing. The robustness of partially adapted plans was also evaluated. RESULTS Partially adapted plans showed comparable results to fully adapted plans and were superior to non-adapted plans for both target coverage and OAR sparing. Target coverage degradation in the non-adapted plans (median D98%: 95.9% and 97.5% for CTVLow and CTVHigh, respectively) was recovered by both partial (98.0% and 98.5%) and full adaptation (98.2% and 98.7%) in comparison to the initial plans (98.7% and 98.8%). The initial hotspot dose for the CTVHigh (median D2%: 101.8%) increased in the non-adapted plans (102.9%) and was recovered by the adaptive strategies (partial: 102.5%, full: 101.9%). The near-maximum dose (D0.01cc) to brainstem and spinal cord was within clinical constraints for all investigated dose distributions, but clearly increased for no adaptation and improved in the (both partially and fully) adapted plans with respect to the non-adapted ones. The parotids' median doses (D50) were mainly patient-specific depending on the proximity to the target region, but anyway lower for the partially and fully adapted plans compared to the non-adapted ones. OAR sparing was furthermore improved for the partially adapted plans in comparison to full adaptation. Robustness of the target dose metrics was preserved in all evaluated scenarios. CONCLUSIONS For OAPT of HNC patients, partial adaptation is able to generate plans of superior conformity to non-adapted plans and of comparable conformity as fully adapted plans, while having the potential to speed up the online-adaptive workflows. Thus, partial adaptation represents an intermediate approach until fast online adaptation workflows become available. Furthermore, it can be applied in workflows where online treatment verification stops the delivery and triggers an online adaptation for the remaining fraction.
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Affiliation(s)
- Virginia Gambetta
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
| | | | - Stefan Menkel
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Christian Richter
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Kristin Stützer
- OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany
- Helmholtz-Zentrum Dresden - Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany
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Wu Z, Jia X, Lu L, Xu C, Pang Y, Peng S, Liu M, Wu Y. Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy. Clin Oncol (R Coll Radiol) 2024; 36:e209-e223. [PMID: 38631974 DOI: 10.1016/j.clon.2024.03.022] [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: 11/01/2023] [Revised: 02/22/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
AIMS Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and then explore the feasibility of the DL algorithm to endometrial cancer VMAT with different prescriptions. MATERIALS AND METHODS We proposed the AtTranNet algorithm for three-dimensional dose prediction. A total of 367 cervical patients were enrolled in this study. Three hundred twenty-two cervical patients from 3 centers were randomly divided into 70%, 10%, and 20% as training, validation, and testing sets, respectively. Forty-five cervical patients from another center were selected for external testing. Moreover, 70 patients of endometrial cancer with different prescriptions were further selected to test the model. Prediction precision was evaluated by dosimetric difference, dose map, and dose-volume histogram metrics. RESULTS The prediction results were all clinically acceptable. The mean absolute error within the body in internal testing was 0.66 ± 0.63%. The maximum |δD| for planning target volume was observed in D98, which is 1.24 ± 2.73 Gy. The maximum |δD| for organs at risk was observed in Dmean of bladder, which is 4.79 ± 3.14 Gy. The maximum |δV| were observed in V40 of pelvic bones, which is 4.77 ± 4.48%. CONCLUSION AtTranNet showed the feasibility and reasonable accuracy in the dose prediction for cervical cancer in multiple centers. The model can also be generalized for endometrial cancer with different prescriptions without any transfer learning.
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Affiliation(s)
- Z Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China
| | - X Jia
- Department of Radiotherapy, The Ninth People's Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - L Lu
- Department of Radiotherapy, Tongling People's Hospital, Anhui, PR China
| | - C Xu
- Department of Radiotherapy, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, PR China
| | - Y Pang
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - S Peng
- Department of Radiotherapy, Zigong First People's Hospital, Sichuan, PR China
| | - M Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China.
| | - Y Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing, PR China; Yu-Yue Pathology Research Center, Jinfeng Laboratory, Chongqing, PR China.
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Chen M, Pang B, Zeng Y, Xu C, Chen J, Yang K, Chang Y, Yang Z. Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy. Phys Med Biol 2024; 69:115056. [PMID: 38718814 DOI: 10.1088/1361-6560/ad48f6] [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: 12/08/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024]
Abstract
Objective.To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Approach.Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.Main results.Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.Significance.Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.
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Affiliation(s)
- Mei Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Bo Pang
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Yiling Zeng
- Department of Medical Physics, School of Physics and Technology, Wuhan University, Wuhan 430072, People's Republic of China
| | - Cheng Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 20025, People's Republic of China
| | - Kunyu Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
- Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People's Republic of China
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Hurkmans C, Bibault JE, Clementel E, Dhont J, van Elmpt W, Kantidakis G, Andratschke N. Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example. Radiother Oncol 2024; 194:110196. [PMID: 38432311 DOI: 10.1016/j.radonc.2024.110196] [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: 11/27/2023] [Revised: 01/29/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND AND PURPOSE Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.
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Affiliation(s)
- Coen Hurkmans
- Dept. of Radiation Oncology, Catharina Hospital Eindhoven, the Netherlands; Dept. of Electrical Engineering, Technical University Eindhoven, the Netherlands.
| | - Jean-Emmanuel Bibault
- Dept. of Radiation Oncology, Hôpital Européen Georges Pompidou, Université Paris Cité, Paris, France
| | - Enrico Clementel
- European Organisation for the Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Jennifer Dhont
- Université libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (H.U.B), Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium; Université Libre De Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, the Netherlands
| | - Georgios Kantidakis
- European Organisation for the Research and Treatment of Cancer (EORTC), Brussels, Belgium
| | - Nicolaus Andratschke
- Dept. of Radiation Oncology, University Hospital of Zurich, The University of Zurich, Zurich, Switzerland
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Zhang L, Holmes JM, Liu Z, Vora SA, Sio TT, Vargas CE, Yu NY, Keole SR, Schild SE, Bues M, Li S, Liu T, Shen J, Wong WW, Liu W. Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy. Med Phys 2024; 51:1484-1498. [PMID: 37748037 DOI: 10.1002/mp.16758] [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: 06/18/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s. CONCLUSIONS An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.
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Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Jason M Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Zhengliang Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Carlos E Vargas
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sameer R Keole
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sheng Li
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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Zou Z, Gong C, Zeng L, Guan Y, Huang B, Yu X, Liu Q, Zhang M. Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:60-71. [PMID: 38343215 DOI: 10.1007/s10278-023-00930-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 03/02/2024]
Abstract
Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.
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Affiliation(s)
- Zhongsheng Zou
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Changfei Gong
- Department of Radiation Oncology, 1st Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lingpeng Zeng
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Yu Guan
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Bin Huang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Xiuwen Yu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
| | - Minghui Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
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Wang H, Chen X, He L. A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges. Pediatr Radiol 2023; 53:2742-2755. [PMID: 37945937 DOI: 10.1007/s00247-023-05792-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various imaging modalities, including computed tomography, magnetic resonance imaging, and positron emission tomography, are utilized to assess neuroblastoma. Nevertheless, these conventional imaging modalities have limitations in providing quantitative information for accurate diagnosis and prognosis. Radiomics, an emerging technique, can extract intricate medical imaging information that is imperceptible to the human eye and transform it into quantitative data. In conjunction with deep learning algorithms, radiomics holds great promise in complementing existing imaging modalities. The aim of this review is to showcase the potential of radiomics and deep learning advancements to enhance the diagnostic capabilities of current imaging modalities for neuroblastoma.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
- Chongqing Key Laboratory of Pediatrics, Chongqing, China.
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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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Huet-Dastarac M, Michiels S, Rivas ST, Ozan H, Sterpin E, Lee JA, Barragan-Montero A. Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer. Med Phys 2023; 50:6201-6214. [PMID: 37140481 DOI: 10.1002/mp.16431] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/07/2023] [Accepted: 04/01/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND In cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails comparing manually generated treatment plans, which requires time and expertise. PURPOSE We developed an automatic and fast tool, AI-PROTIPP (Artificial Intelligence Predictive Radiation Oncology Treatment Indication to Photons/Protons), that assesses quantitatively the benefits of each therapeutic option. Our method uses deep learning (DL) models to directly predict the dose distributions for a given patient for both XT and PT. By using models that estimate the Normal Tissue Complication Probability (NTCP), namely the likelihood of side effects to occur for a specific patient, AI-PROTIPP can propose a treatment selection quickly and automatically. METHODS A database of 60 patients presenting oropharyngeal cancer, obtained from the Cliniques Universitaires Saint Luc in Belgium, was used in this study. For every patient, a PT plan and an XT plan were generated. The dose distributions were used to train the two dose DL prediction models (one for each modality). The model is based on U-Net architecture, a type of convolutional neural network currently considered as the state of the art for dose prediction models. A NTCP protocol used in the Dutch model-based approach, including grades II and III xerostomia and grades II and III dysphagia, was later applied in order to perform automatic treatment selection for each patient. The networks were trained using a nested cross-validation approach with 11-folds. We set aside three patients in an outer set and each fold consists of 47 patients in training, five in validation and five for testing. This method allowed us to assess our method on 55 patients (five patients per test times the number of folds). RESULTS The treatment selection based on the DL-predicted doses reached an accuracy of 87.4% for the threshold parameters set by the Health Council of the Netherlands. The selected treatment is directly linked with these threshold parameters as they express the minimal gain brought by the PT treatment for a patient to be indicated to PT. To validate the performance of AI-PROTIPP in other conditions, we modulated these thresholds, and the accuracy was above 81% for all the considered cases. The difference in average cumulative NTCP per patient of predicted and clinical dose distributions is very similar (less than 1% difference). CONCLUSIONS AI-PROTIPP shows that using DL dose prediction in combination with NTCP models to select PT for patients is feasible and can help to save time by avoiding the generation of treatment plans only used for the comparison. Moreover, DL models are transferable, allowing, in the future, experience to be shared with centers that would not have PT planning expertise.
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Affiliation(s)
| | - Steven Michiels
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
| | - Sara Teruel Rivas
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
| | - Hamdiye Ozan
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Leuven, Belgium
| | - John A Lee
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
| | - Ana Barragan-Montero
- Molecular Imaging, Radiotherapy and Oncology (MIRO), IREC, UCLouvain, Brussels, Belgium
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11
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Li Z, Yang Z, Lu J, Zhu Q, Wang Y, Zhao M, Li Z, Fu J. Deep learning-based dose map prediction for high-dose-rate brachytherapy. Phys Med Biol 2023; 68:175015. [PMID: 37589292 DOI: 10.1088/1361-6560/acecd2] [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: 03/27/2023] [Accepted: 08/02/2023] [Indexed: 08/18/2023]
Abstract
Background. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction techniques have been reported as promising solutions with high efficiency and accuracy. However, current dose prediction studies mainly target EBRT which are inappropriate for brachytherapy, the model designed specifically for brachytherapy has not yet well-established.Purpose. To predict dose distribution in brachytherapy using a novel Squeeze and Excitation Attention Net (SE_AN) model.Method. We hypothesized the tracks of192Ir inside applicators are essential for brachytherapy dose prediction. To emphasize the applicator contribution, a novel SE module was integrated into a Cascaded UNet to recalibrate informative features and suppress less useful ones. The Cascaded UNet consists of two stacked UNets, with the first designed to predict coarse dose distribution and the second added for fine-tuning 250 cases including all typical clinical applicators were studied, including vaginal, tandem and ovoid, multi-channel, and free needle applicators. The developed SE_AN was subsequently compared to the classic UNet and classic Cascaded UNet (without SE module) models. The model performance was evaluated by comparing the predicted dose against the clinically approved plans using mean absolute error (MAE) of DVH metrics, includingD2ccandD90%.Results. The MAEs of DVH metrics demonstrated that SE_AN accurately predicted the dose with 0.37 ± 0.25 difference for HRCTVD90%, 0.23 ± 0.14 difference for bladderD2cc, and 0.28 ± 0.20 difference for rectumD2cc. In comparison studies, UNet achieved 0.34 ± 0.24 for HRCTV, 0.25 ± 0.20 for bladder, 0.25 ± 0.21 for rectum, and Cascaded UNet achieved 0.42 ± 0.31 for HRCTV, 0.24 ± 0.19 for bladder, 0.23 ± 0.19 for rectum.Conclusion. We successfully developed a method specifically for 3D brachytherapy dose prediction. Our model demonstrated comparable performance to clinical plans generated by experienced dosimetrists. The developed technique is expected to improve the standardization and quality control of brachytherapy treatment planning.
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Affiliation(s)
- Zhen Li
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
| | - Zhenyu Yang
- Duke University, Durham, NC, United States of America
| | - Jiayu Lu
- Boston University, Boston, MA, United States of America
| | - Qingyuan Zhu
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
| | - Yanxiao Wang
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
| | - Mengli Zhao
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
| | - Zhaobin Li
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
| | - Jie Fu
- Shanghai Sixth People's Hospital, Shanghai, People's Republic of China
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12
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Automation of pencil beam scanning proton treatment planning for intracranial tumours. Phys Med 2023; 105:102503. [PMID: 36529006 DOI: 10.1016/j.ejmp.2022.11.007] [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: 04/26/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To evaluate the feasibility of comprehensive automation of an intra-cranial proton treatment planning. MATERIALS AND METHODS Class solution (CS) beam configuration selection allows the user to identify predefined beam configuration based on target localization; automatic CS (aCS) will then explore all the possible CS beam geometries. Ten patients, already used for the evaluation of the automatic selection of the beam configuration, have been also employed to training an algorithm based on the computation of a benchmark dose exploit automatic general planning solution (GPS) optimization with a wish list approach for the planning optimization. An independent cohort of ten patients has been then used for the evaluation step between the clinical and the GPS plan in terms of dosimetric quality of plans and the time needed to generate a plan. RESULTS The definition of a beam configuration requires on average 22 min (range 9-29 min). The average time for GPS plan generation is 18 min (range 7-26 min). Median dose differences (GPS-Manual) for each OAR constraints are: brainstem -1.60 Gy, left cochlea -1.22 Gy, right cochlea -1.42 Gy, left eye 0.55 Gy, right eye -2.33 Gy, optic chiasm -1.87 Gy, left optic nerve -4.45 Gy, right optic nerve -2.48 Gy and optic tract -0.31 Gy. Dosimetric CS and aCS plan evaluation shows a slightly worsening of the OARs values except for the optic tract and optic chiasm for both CS and aCS, where better results have been observed. CONCLUSION This study has shown the feasibility and implementation of the automatic planning system for intracranial tumors. The method developed in this work is ready to be implemented in a clinical workflow.
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13
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Pirlepesov F, Wilson L, Moskvin VP, Breuer A, Parkins F, Lucas JT, Merchant TE, Faught AM. Three-dimensional dose and LET D prediction in proton therapy using artificial neural networks. Med Phys 2022; 49:7417-7427. [PMID: 36227617 PMCID: PMC9872814 DOI: 10.1002/mp.16043] [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: 04/26/2022] [Revised: 08/30/2022] [Accepted: 09/21/2022] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Challenges in proton therapy include identifying patients most likely to benefit; ensuring consistent, high-quality plans as its adoption becomes more widespread; and recognizing biological uncertainties that may be related to increased relative biologic effectiveness driven by linear energy transfer (LET). Knowledge-based planning (KBP) is a domain that may help to address all three. METHODS Artificial neural networks were trained using 117 unique treatment plans and associated dose and dose-weighted LET (LETD ) distributions. The data set was split into training (n = 82), validation (n = 17), and test (n = 18) sets. Model performance was evaluated on the test set using dose- and LETD -volume metrics in the clinical target volume (CTV) and nearby organs at risk and Dice similarity coefficients (DSC) comparing predicted and planned isodose lines at 50%, 75%, and 95% of the prescription dose. RESULTS Dose-volume metrics significantly differed (α = 0.05) between predicted and planned dose distributions in only one dose-volume metric, D2% to the CTV. The maximum observed root mean square (RMS) difference between corresponding metrics was 4.3 GyRBE (8% of prescription) for D1cc to optic chiasm. DSC were 0.90, 0.93, and 0.88 for the 50%, 75%, and 95% isodose lines, respectively. LETD -volume metrics significantly differed in all but one metric, L0.1cc of the brainstem. The maximum observed difference in RMS differences for LETD metrics was 1.0 keV/μm for L0.1cc to brainstem. CONCLUSIONS We have devised the first three-dimensional dose and LETD -prediction model for cranial proton radiation therapy has been developed. Dose accuracy compared favorably with that of previously published models in other treatment sites. The agreement in LETD supports future investigations with biological doses in mind to enable the full potential of KBP in proton therapy.
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Affiliation(s)
- Fakhriddin Pirlepesov
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - Lydia Wilson
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - Vadim P. Moskvin
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - Alex Breuer
- Department of Pathology, St. Jude Children’s
Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678
| | - Franz Parkins
- Department of Information Services, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - John T. Lucas
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - Thomas E. Merchant
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
| | - Austin M. Faught
- Department of Radiation Oncology, St. Jude
Children’s Research Hospital, 262 Danny Thomas Place, Memphis, TN,
38105-3678
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Leary D, Basran PS. The role of artificial intelligence in veterinary radiation oncology. Vet Radiol Ultrasound 2022; 63 Suppl 1:903-912. [PMID: 36514233 DOI: 10.1111/vru.13162] [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: 08/20/2021] [Revised: 01/21/2022] [Accepted: 04/12/2022] [Indexed: 12/15/2022] Open
Abstract
Veterinary radiation oncology regularly deploys sophisticated contouring, image registration, and treatment planning optimization software for patient care. Over the past decade, advances in computing power and the rapid development of neural networks, open-source software packages, and data science have been realized and resulted in new research and clinical applications of artificial intelligent (AI) systems in radiation oncology. These technologies differ from conventional software in their level of complexity and ability to learn from representative and local data. We provide clinical and research application examples of AI in human radiation oncology and their potential applications in veterinary medicine throughout the patient's care-path: from treatment simulation, deformable registration, auto-segmentation, automated treatment planning and plan selection, quality assurance, adaptive radiotherapy, and outcomes modeling. These technologies have the potential to offer significant time and cost savings in the veterinary setting; however, since the range of usefulness of these technologies have not been well studied nor understood, care must be taken if adopting AI technologies in clinical practice. Over the next several years, some practical and realizable applications of AI in veterinary radiation oncology include automated segmentation of normal tissues and tumor volumes, deformable registration, multi-criteria plan optimization, and adaptive radiotherapy. Keys in achieving success in adopting AI in veterinary radiation oncology include: establishing "truth-data"; data harmonization; multi-institutional data and collaborations; standardized dose reporting and taxonomy; adopting an open access philosophy, data collection and curation; open-source algorithm development; and transparent and platform-independent code development.
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Affiliation(s)
- Del Leary
- Department of Environment and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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15
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Zhang G, Jiang Z, Zhu J, Wang L. Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results. Radiat Oncol 2022; 17:179. [PMID: 36372897 PMCID: PMC9655866 DOI: 10.1186/s13014-022-02155-7] [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: 09/12/2022] [Accepted: 11/04/2022] [Indexed: 11/15/2022] Open
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Draguet C, Barragán-Montero AM, Vera MC, Thomas M, Populaire P, Defraene G, Haustermans K, Lee JA, Sterpin E. Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer. Radiother Oncol 2022; 176:101-107. [PMID: 36167194 DOI: 10.1016/j.radonc.2022.08.031] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/03/2022] [Accepted: 08/28/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE This study aims to investigate how accurate our deep learning (DL) dose prediction models for intensity modulated radiotherapy (IMRT) and pencil beam scanning (PBS) treatments, when chained with normal tissue complication probability (NTCP) models, are at identifying esophageal cancer patients who are at high risk of toxicity and should be switched to proton therapy (PT). MATERIALS AND METHODS Two U-Net were created, for photon (XT) and proton (PT) plans, respectively. To estimate the dose distribution for each patient, they were trained on a database of 40 uniformly planned patients using cross validation and a circulating test set. These models were combined with a NTCP model for postoperative pulmonary complications. The NTCP model used the mean lung dose, age, histology type, and body mass index as predicting variables. The treatment choice is then done by using a ΔNTCP threshold between XT and PT plans. Patients with ΔNTCP ≥ 10% were referred to PT. RESULTS Our DL models succeed in predicting dose distributions with a mean error on the mean dose to the lungs (MLD) of 1.14 ± 0.93% for XT and 0.66 ± 0.48% for PT. The complete automated workflow (DL chained with NTCP) achieved 100% accuracy in patient referral. The average residual (ΔNTCP ground truth - ΔNTCP predicted) is 1.43 ± 1.49%. CONCLUSION This study evaluates our DL dose prediction models in a broader patient referral context and demonstrates their ability to support clinical decisions.
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Affiliation(s)
- Camille Draguet
- UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium.
| | - Ana M Barragán-Montero
- UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Macarena Chocan Vera
- UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Melissa Thomas
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; University Hospitals Leuven, Department of Radiation Oncology, 3000 Leuven, Belgium
| | - Pieter Populaire
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; University Hospitals Leuven, Department of Radiation Oncology, 3000 Leuven, Belgium
| | - Gilles Defraene
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
| | - Karin Haustermans
- KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium; University Hospitals Leuven, Department of Radiation Oncology, 3000 Leuven, Belgium
| | - John A Lee
- UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium
| | - Edmond Sterpin
- UCLouvain, Institut de Recherche Expérimentale et Clinique, Molecular Imaging Radiotherapy and Oncology (MIRO), Brussels, Belgium; KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium
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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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A Survey on Deep Learning for Precision Oncology. Diagnostics (Basel) 2022; 12:diagnostics12061489. [PMID: 35741298 PMCID: PMC9222056 DOI: 10.3390/diagnostics12061489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
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Barragán-Montero A, Bibal A, Dastarac MH, Draguet C, Valdés G, Nguyen D, Willems S, Vandewinckele L, Holmström M, Löfman F, Souris K, Sterpin E, Lee JA. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol 2022; 67:10.1088/1361-6560/ac678a. [PMID: 35421855 PMCID: PMC9870296 DOI: 10.1088/1361-6560/ac678a] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/14/2022] [Indexed: 01/26/2023]
Abstract
The interest in machine learning (ML) has grown tremendously in recent years, partly due to the performance leap that occurred with new techniques of deep learning, convolutional neural networks for images, increased computational power, and wider availability of large datasets. Most fields of medicine follow that popular trend and, notably, radiation oncology is one of those that are at the forefront, with already a long tradition in using digital images and fully computerized workflows. ML models are driven by data, and in contrast with many statistical or physical models, they can be very large and complex, with countless generic parameters. This inevitably raises two questions, namely, the tight dependence between the models and the datasets that feed them, and the interpretability of the models, which scales with its complexity. Any problems in the data used to train the model will be later reflected in their performance. This, together with the low interpretability of ML models, makes their implementation into the clinical workflow particularly difficult. Building tools for risk assessment and quality assurance of ML models must involve then two main points: interpretability and data-model dependency. After a joint introduction of both radiation oncology and ML, this paper reviews the main risks and current solutions when applying the latter to workflows in the former. Risks associated with data and models, as well as their interaction, are detailed. Next, the core concepts of interpretability, explainability, and data-model dependency are formally defined and illustrated with examples. Afterwards, a broad discussion goes through key applications of ML in workflows of radiation oncology as well as vendors' perspectives for the clinical implementation of ML.
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Affiliation(s)
- Ana Barragán-Montero
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Adrien Bibal
- PReCISE, NaDI Institute, Faculty of Computer Science, UNamur and CENTAL, ILC, UCLouvain, Belgium
| | - Margerie Huet Dastarac
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Camille Draguet
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - Gilmer Valdés
- Department of Radiation Oncology, Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, United States of America
| | - Siri Willems
- ESAT/PSI, KU Leuven Belgium & MIRC, UZ Leuven, Belgium
| | | | | | | | - Kevin Souris
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
| | - Edmond Sterpin
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
- Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven, Belgium
| | - John A Lee
- Molecular Imaging, Radiation and Oncology (MIRO) Laboratory, Institut de Recherche Expérimentale et Clinique (IREC), UCLouvain, Belgium
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Wang W, Chang Y, Liu Y, Liang Z, Liao Y, Qin B, Liu X, Yang Z. Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer. Med Phys 2022; 49:5451-5463. [PMID: 35543109 DOI: 10.1002/mp.15702] [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: 02/07/2022] [Revised: 04/20/2022] [Accepted: 04/28/2022] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT). METHODS A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a 5-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the Peak-MU model, this work develops the Multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and MC dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The Monte-Carlo dropout was also added to the network to quantify the uncertainty of the model. RESULTS Compared to the Peak-MU model, the Multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (CTV D98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the Multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the Multi-MU model is 0.46%, with a dropout rate of 10%. CONCLUSION This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the PB analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wei Wang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yu Chang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Yilin Liu
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030-3722, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Zhikai Liang
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yicheng Liao
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Bin Qin
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xu Liu
- State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Zhiyong Yang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
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21
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Pediatric radiotherapy for thoracic and abdominal targets: organ motion, reported margin sizes, and delineation variations – a systematic review. Radiother Oncol 2022; 173:134-145. [DOI: 10.1016/j.radonc.2022.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 05/09/2022] [Accepted: 05/26/2022] [Indexed: 11/21/2022]
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22
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Borderías-Villarroel E, Taasti V, Van Elmpt W, Teruel-Rivas S, Geets X, Sterpin E. Evaluation of the clinical value of automatic online dose restoration for adaptive proton therapy of head and neck cancer. Radiother Oncol 2022; 170:190-197. [PMID: 35346754 DOI: 10.1016/j.radonc.2022.03.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 03/21/2022] [Accepted: 03/21/2022] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Intensity modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. This study compares not-adapted (NA) robust plans to two adaptive IMPT methods - a fully-offline adaptive (FOA) and a simplified automatic online adaptive strategy (dose restoration (DR)) to determine the benefit of DR, in head and neck cancer (HNC). MATERIAL/METHODS Robustly optimized clinical IMPT doses in planning-CTs (pCTs) were available for a cohort of 10 HNC patients. During robust re-optimization, DR used isodose contours, generated from the clinical dose on pCTs, and patient specific objectives to reproduce the clinical dose in every repeated-CT(rCT). For each rCT(n=50), NA, DR and FOA plans were robustly evaluated. RESULTS An improvement in DVH-metrics and robustness was seen for DR and FOA plans compared to NA plans. For NA plans, 74%(37/50) of rCTs did not fulfill the CTV coverage criteria (D98%>95%Dprescription). DR improved target coverage, target homogeneity and variability on critical risk organs such as the spinal cord. After DR, 52%(26/50) of rCTs met all clinical goals. Because of large anatomical changes and/or inaccurate patient repositioning, 48%(24/50) of rCTs still needed full offline adaptation to ensure an optimal treatment since dose restoration was not able to re-establish the initial plan quality. CONCLUSION Robust optimization together with fully-automatized DR avoided offline adaptation in 52% of the cases. Implementation of dose restoration in clinical routine could ensure treatment plan optimality while saving valuable human and material resources to radiotherapy departments.
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Affiliation(s)
- Elena Borderías-Villarroel
- Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium. Avenue Hippocrate 54, Bte B1.54.07, 1200 Brussels, (Belgium).
| | - Vicki Taasti
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, (Netherlands).
| | - Wouter Van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology, Maastricht University Medical Centre+, Doctor Tanslaan 12, 6229 ET Maastricht, (Netherlands).
| | - S Teruel-Rivas
- Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium. Avenue Hippocrate 54, Bte B1.54.07, 1200 Brussels, (Belgium)
| | - X Geets
- Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium. Avenue Hippocrate 54, Bte B1.54.07, 1200 Brussels, (Belgium); Department of Radiation Oncology, Cliniques Universitaires Saint-Luc, Brussels, Belgium. Avenue Hippocrate 10, 1200 Brussels, (Belgium).
| | - E Sterpin
- Molecular Imaging, Radiotherapy and Oncology (MIRO), UCLouvain, Brussels, Belgium. Avenue Hippocrate 54, Bte B1.54.07, 1200 Brussels, (Belgium); Department of Oncology, Laboratory of Experimental Radiotherapy, KULeuven, Herestraat 49, 3000 Leuven, (Belgium).
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23
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Cui Bono, Proton Radiotherapy? Clin Oncol (R Coll Radiol) 2022; 34:258-260. [DOI: 10.1016/j.clon.2022.01.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/07/2022] [Accepted: 01/14/2022] [Indexed: 11/19/2022]
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24
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Sun Z, Xia X, Fan J, Zhao J, Zhang K, Wang J, Hu W. A hybrid optimization strategy for deliverable Intensity-modulated radiotherapy plan generation using deep learning-based dose prediction. Med Phys 2022; 49:1344-1356. [PMID: 35043971 DOI: 10.1002/mp.15462] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/08/2021] [Accepted: 12/22/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy (IMRT) including dose prediction via a deep learning and voxel-based optimization strategy. MATERIALS AND METHODS The dose distribution of patients was predicted using a U-Net-based deep learning network based on the patient's anatomy information. One hundred seventeen patients with nasopharyngeal cancer (NPC) and 200 patients with rectal cancer were enrolled in this study. For NPC cases, 94 cases were included in the training dataset, 13 in the validation dataset and 10 in the testing dataset. For rectal cancer cases, 172 cases were included in the training set, 18 in the validation set and 10 in the testing set. A voxel-based optimization strategy, "Voxel", was proposed to achieve treatment planning optimization by dividing body voxels into two parts: inside planning target volumes (PTVs) and outside PTVs. Fixed dose-volume objectives were attached to the total objective function to realize individualized planning intended as the "hybrid" optimizing strategy. Automatically generated plans were compared with clinically approved plans to evaluate clinical gains, according to dosimetric indices and dose-volume histograms (DVHs). RESULTS Similarities were found between the DVH of the predicted dose and clinical plan, although significant differences were found in some organs at risk (OARs). Better organ sparing and suboptimal PTV coverage were shown using the voxel strategy; however, the deviation in homogeneity indices (HI) and conformity indices (CI) of the PTV between automatically generated plans and manual plans were reduced by the hybrid strategy ((Manual plans)/(Voxel plans)/(Hybrid plans): HI of PTV70 (1.06/1.12/1.02), CI of PTV70 (0.79/0.58/0.76)). The optimization time for each patient was within 1 minute and included fluence map optimization, leaf sequencing and control point optimization. All the generated plans (voxel & hybrid strategy) could be delivered on uRT-linac 506c (United Imaging Health care, Shanghai, China). CONCLUSION Deliverable plans can be generated by incorporating a voxel-based optimization strategy into a commercial TPS. The hybrid optimization method shows the benefit and clinical feasibility in generating clinically acceptable plans. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zihan Sun
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Xiang Xia
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Jun Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Kang Zhang
- United Imaging Healthcare, Shanghai, 201807, China
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai, 200032, China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai, 200032, China
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Liu Y, Chen Z, Wang J, Wang X, Qu B, Ma L, Zhao W, Zhang G, Xu S. Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy. Front Oncol 2021; 11:752007. [PMID: 34858825 PMCID: PMC8631763 DOI: 10.3389/fonc.2021.752007] [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: 08/02/2021] [Accepted: 10/21/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose This study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs). Methods A convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the "3D Dense-U-Net", which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis. Results We found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one. Conclusions It is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.
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Affiliation(s)
- Yaoying Liu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China.,School of Physics, Beihang University, Beijing, China
| | | | - Jinyuan Wang
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Xiaoshen Wang
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Baolin Qu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
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Jampa-Ngern S, Kobashi K, Shimizu S, Takao S, Nakazato K, Shirato H. Prediction of liver Dmean for proton beam therapy using deep learning and contour-based data augmentation. JOURNAL OF RADIATION RESEARCH 2021:rrab095. [PMID: 34617104 DOI: 10.1093/jrr/rrab095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/13/2021] [Indexed: 06/13/2023]
Abstract
The prediction of liver Dmean with 3-dimensional radiation treatment planning (3DRTP) is time consuming in the selection of proton beam therapy (PBT), and deep learning prediction generally requires large and tumor-specific databases. We developed a simple dose prediction tool (SDP) using deep learning and a novel contour-based data augmentation (CDA) approach and assessed its usability. We trained the SDP to predict the liver Dmean immediately. Five and two computed tomography (CT) data sets of actual patients with liver cancer were used for the training and validation. Data augmentation was performed by artificially embedding 199 contours of virtual clinical target volume (CTV) into CT images for each patient. The data sets of the CTVs and OARs are labeled with liver Dmean for six different treatment plans using two-dimensional calculations assuming all tissue densities as 1.0. The test of the validated model was performed using 10 unlabeled CT data sets of actual patients. Contouring only of the liver and CTV was required as input. The mean relative error (MRE), the mean percentage error (MPE) and regression coefficient between the planned and predicted Dmean was 0.1637, 6.6%, and 0.9455, respectively. The mean time required for the inference of liver Dmean of the six different treatment plans for a patient was 4.47±0.13 seconds. We conclude that the SDP is cost-effective and usable for gross estimation of liver Dmean in the clinic although the accuracy should be improved further if we need the accuracy of liver Dmean to be compatible with 3DRTP.
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Affiliation(s)
- Sira Jampa-Ngern
- Graduate School of Biomedical Science and Engineering, Hokkaido University, Sapporo, 0608638, Japan
| | - Keiji Kobashi
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, 0608638, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
| | - Shinichi Shimizu
- Graduate School of Biomedical Science and Engineering, Hokkaido University, Sapporo, 0608638, Japan
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, 0608638, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
| | - Seishin Takao
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
- Faculty of Engineering, Hokkaido University, Sapporo, 0608628, Japan
| | - Keiji Nakazato
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, 0608638, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
| | - Hiroki Shirato
- Graduate School of Biomedical Science and Engineering, Hokkaido University, Sapporo, 0608638, Japan
- Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, 0608638, Japan
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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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