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de Lima MC, de Castro CC, Aguiar KEC, Monte N, da Costa Nunes GG, da Costa ACA, Rodrigues JCG, Guerreiro JF, Ribeiro-dos-Santos Â, de Assumpção PP, Burbano RMR, Fernandes MR, dos Santos SEB, dos Santos NPC. Molecular Profile of Important Genes for Radiogenomics in the Amazon Indigenous Population. J Pers Med 2024; 14:484. [PMID: 38793065 PMCID: PMC11122349 DOI: 10.3390/jpm14050484] [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: 03/20/2024] [Revised: 04/13/2024] [Accepted: 04/18/2024] [Indexed: 05/26/2024] Open
Abstract
Radiotherapy is focused on the tumor but also reaches healthy tissues, causing toxicities that are possibly related to genomic factors. In this context, radiogenomics can help reduce the toxicity, increase the effectiveness of radiotherapy, and personalize treatment. It is important to consider the genomic profiles of populations not yet studied in radiogenomics, such as the indigenous Amazonian population. Thus, our objective was to analyze important genes for radiogenomics, such as ATM, TGFB1, RAD51, AREG, XRCC4, CDK1, MEG3, PRKCE, TANC1, and KDR, in indigenous people and draw a radiogenomic profile of this population. The NextSeq 500® platform was used for sequencing reactions; for differences in the allelic frequency between populations, Fisher's Exact Test was used. We identified 39 variants, 2 of which were high impact: 1 in KDR (rs41452948) and another in XRCC4 (rs1805377). We found four modifying variants not yet described in the literature in PRKCE. We did not find any variants in TANC1-an important gene for personalized medicine in radiotherapy-that were associated with toxicities in previous cohorts, configuring a protective factor for indigenous people. We identified four SNVs (rs664143, rs1801516, rs1870377, rs1800470) that were associated with toxicity in previous studies. Knowing the radiogenomic profile of indigenous people can help personalize their radiotherapy.
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Affiliation(s)
- Milena Cardoso de Lima
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Cinthia Costa de Castro
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Kaio Evandro Cardoso Aguiar
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Natasha Monte
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Giovanna Gilioli da Costa Nunes
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Ana Caroline Alves da Costa
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Juliana Carla Gomes Rodrigues
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - João Farias Guerreiro
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
- Laboratory of Human and Medical Genetics, Federal University of Pará, Belém 66075-110, PA, Brazil;
| | | | - Paulo Pimentel de Assumpção
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Rommel Mario Rodríguez Burbano
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Marianne Rodrigues Fernandes
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
| | - Sidney Emanuel Batista dos Santos
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
- Laboratory of Human and Medical Genetics, Federal University of Pará, Belém 66075-110, PA, Brazil;
| | - Ney Pereira Carneiro dos Santos
- Oncology Research Center, Federal University of Pará, Belém 66073-005, PA, Brazil; (M.C.d.L.); (C.C.d.C.); (K.E.C.A.); (N.M.); (G.G.d.C.N.); (A.C.A.d.C.); (J.C.G.R.); (J.F.G.); (P.P.d.A.); (R.M.R.B.); (M.R.F.)
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El Ouardy K, Zerfaoui M, Oulhouq Y, Bahhous K, Rrhioua A, Bakari D. A comparative study of boost dose delivery techniques in breast cancer radiotherapy optimising efficacy and minimising toxicity. RADIATION PROTECTION DOSIMETRY 2024; 200:459-466. [PMID: 38273648 DOI: 10.1093/rpd/ncad328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024]
Abstract
The present study aims to compare three techniques for delivering a boost absorbed dose: conventional reduced tangential (3D), volumetric modulated arc therapy (VMAT) and fields forward-planned technique boost (3DF). The study included 15 postoperative breast cancer patients who received a boost absorbed dose following breast-conserving surgery. The conformity index and homogeneity index were used to evaluate treatment outcomes, along with the average absorbed dose received by organs at risk (OAR). All the calculated dosimetric plans are carried out using Monaco Treatment Planning System (TPS). VMAT offers superior conformity, dose homogeneity and target coverage, it is associated with higher absorbed doses to OAR such as the heart and lung. In contrast, the 3D and 3DF techniques exhibit advantages in reducing absorbed doses to critical structures, potentially minimising the risk of cardiac and pulmonary complications. Each technique has its advantages and disadvantages. The choice of technique should be individualised, taking into account patient-specific factors and treatment goals and involves a multidisciplinary approach.
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Affiliation(s)
- Khalid El Ouardy
- Laboratory of Physics of Matter and Radiation, Faculty of Sciences, Mohammed First University, Oujda, 60000, Morocco
| | - Mustapha Zerfaoui
- Laboratory of Physics of Matter and Radiation, Faculty of Sciences, Mohammed First University, Oujda, 60000, Morocco
| | - Yassine Oulhouq
- Laboratory of Physics of Matter and Radiation, Faculty of Sciences, Mohammed First University, Oujda, 60000, Morocco
| | - Karim Bahhous
- Faculty of Science, University Mohammed V in Rabat, Rabat B.P. 1014, Morocco
| | - Abdeslem Rrhioua
- Laboratory of Physics of Matter and Radiation, Faculty of Sciences, Mohammed First University, Oujda, 60000, Morocco
| | - Dikra Bakari
- National School of Applied Sciences, Mohammed First University, Oujda 60000, Morocco
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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: 10] [Impact Index Per Article: 5.0] [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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Hansen CR, Hussein M, Bernchou U, Zukauskaite R, Thwaites D. Plan quality in radiotherapy treatment planning - Review of the factors and challenges. J Med Imaging Radiat Oncol 2022; 66:267-278. [PMID: 35243775 DOI: 10.1111/1754-9485.13374] [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: 09/02/2021] [Accepted: 12/14/2021] [Indexed: 12/25/2022]
Abstract
A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.
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Affiliation(s)
- Christian Rønn Hansen
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark.,Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia.,Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Mohammad Hussein
- Metrology for Medical Physics Centre, National Physical Laboratory, Teddington, UK
| | - Uffe Bernchou
- Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ruta Zukauskaite
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark.,Department of Oncology, Odense University Hospital, Odense, Denmark
| | - David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, NSW, Australia
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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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Sheng Y, Zhang J, Ge Y, Li X, Wang W, Stephens H, Yin FF, Wu Q, Wu QJ. Artificial intelligence applications in intensity modulated radiation treatment planning: an overview. Quant Imaging Med Surg 2021; 11:4859-4880. [PMID: 34888195 PMCID: PMC8611458 DOI: 10.21037/qims-21-208] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 07/02/2021] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.
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Affiliation(s)
- Yang Sheng
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Jiahan Zhang
- Department of Radiation Oncology, Emory University Hospital, Atlanta, GA, USA
| | - Yaorong Ge
- Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, USA
| | - Xinyi Li
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Wentao Wang
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Hunter Stephens
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Qiuwen Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
| | - Q. Jackie Wu
- Department of Radiation Oncology, Duke University Medical Center, Durham, NC, USA
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Bai X, Zhang J, Wang B, Wang S, Xiang Y, Hou Q. Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks. Biomed Eng Online 2021; 20:101. [PMID: 34627279 PMCID: PMC8501531 DOI: 10.1186/s12938-021-00937-w] [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: 06/02/2021] [Accepted: 09/22/2021] [Indexed: 11/21/2022] Open
Abstract
Background Neural-network methods have been widely used for the prediction of dose distributions in radiotherapy. However, the prediction accuracy of existing methods may be degraded by the problem of dose imbalance. In this work, a new loss function is proposed to alleviate the dose imbalance and achieve more accurate prediction results. The U-Net architecture was employed to build a prediction model. Our study involved a total of 110 patients with left-breast cancer, who were previously treated by volumetric-modulated arc radiotherapy. The patient dataset was divided into training and test subsets of 100 and 10 cases, respectively. We proposed a novel ‘sharp loss’ function, and a parameter γ was used to adjust the loss properties. The mean square error (MSE) loss and the sharp loss with different γ values were tested and compared using the Wilcoxon signed-rank test. Results The sharp loss achieved superior dose prediction results compared to those of the MSE loss. The best performance with the MSE loss and the sharp loss was obtained when the parameter γ was set to 100. Specifically, the mean absolute difference values for the planning target volume were 318.87 ± 30.23 for the MSE loss versus 144.15 ± 16.27 for the sharp loss with γ = 100 (p < 0.05). The corresponding values for the ipsilateral lung, the heart, the contralateral lung, and the spinal cord were 278.99 ± 51.68 versus 198.75 ± 61.38 (p < 0.05), 216.99 ± 44.13 versus 144.86 ± 43.98 (p < 0.05), 125.96 ± 66.76 versus 111.86 ± 47.19 (p > 0.05), and 194.30 ± 14.51 versus 168.58 ± 25.97 (p < 0.05), respectively. Conclusions The sharp loss function could significantly improve the accuracy of radiotherapy dose prediction.
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Affiliation(s)
- Xue Bai
- Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China. .,Key Laboratory of Radiation Physics and Technology, Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, 610064, China.
| | - Jie Zhang
- Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China
| | - Binbing Wang
- Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China
| | - Shengye Wang
- Department of Radiation Physics, Zhejiang Key Laboratory of radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China
| | - Yida Xiang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421000, China
| | - Qing Hou
- Key Laboratory of Radiation Physics and Technology, Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, Chengdu, 610064, China.
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Deep learning method for prediction of patient-specific dose distribution in breast cancer. Radiat Oncol 2021; 16:154. [PMID: 34404441 PMCID: PMC8369791 DOI: 10.1186/s13014-021-01864-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 07/19/2021] [Indexed: 11/10/2022] Open
Abstract
Background Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™. Methods Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction. Results The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D95%, Dmean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively. Conclusions In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.
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Bai X, Liu Z, Zhang J, Wang S, Hou Q, Shan G, Chen M, Wang B. Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer. Sci Prog 2021; 104:368504211038162. [PMID: 34519556 PMCID: PMC10466025 DOI: 10.1177/00368504211038162] [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] [Indexed: 11/17/2022]
Abstract
Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk (p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model (p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.
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Affiliation(s)
- Xue Bai
- Key Lab of Radiation Physics and
Technology, Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, China
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
| | - Ze Liu
- School of Electronic Information and
Electronical Engineering, Chengdu University, China
| | - Jie Zhang
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
| | - Shengye Wang
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
| | - Qing Hou
- Key Lab of Radiation Physics and
Technology, Ministry of Education, Institute of Nuclear Science and Technology, Sichuan University, China
| | - Guoping Shan
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
| | - Ming Chen
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
| | - Binbing Wang
- Department of Radiation Physics, Cancer Hospital of the University of
Chinese Academy of Sciences (Zhejiang Cancer Hospital), China
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