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Zhang H, Yu Y, Zhang F. Prediction of dose distributions for non-small cell lung cancer patients using MHA-ResUNet. Med Phys 2024. [PMID: 39024495 DOI: 10.1002/mp.17308] [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: 12/20/2023] [Revised: 06/08/2024] [Accepted: 06/29/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND The current level of automation in the production of radiotherapy plans for lung cancer patients is relatively low. With the development of artificial intelligence, it has become a reality to use neural networks to predict dose distributions and provide assistance for radiation therapy planning. However, due to the significant individual variability in the distribution of non-small cell lung cancer (NSCLC) planning target volume (PTV) and the complex spatial relationships between the PTV and organs at risk (OARs), there is still a lack of a high-precision dose prediction network tailored to the characteristics of NSCLC. PURPOSE To assist in the development of volumetric modulated arc therapy (VMAT) plans for non-small cell lung cancer patients, a deep neural network is proposed to predict high-precision dose distribution. METHODS This study has developed a network called MHA-ResUNet, which combines a large-kernel dilated convolution module and multi-head attention (MHA) modules. The network was trained based on 80 VMAT plans of NSCLC patients. CT images, PTV, and OARs were fed into the independent input channel. The dose distribution was taken as the output to train the model. The performance of this network was compared with that of several commonly used networks, and the networks' performance was evaluated based on the voxel-level mean absolute error (MAE) within the PTV and OARs, as well as the error in clinical dose-volume metrics. RESULTS The MAE between the predicted dose distribution and the manually planned dose distribution within the PTV is 1.43 Gy, and the D95 error is less than 1 Gy. Compared with the other three commonly used networks, the dose error of the MHA-ResUNet is the smallest in PTV and OARs. CONCLUSIONS The proposed MHA-ResUNet network improves the receptive field and filters the shallow features to learn the relative spatial relation between the PTV and the OARs, enabling accurate prediction of dose distributions in NSCLC patients undergoing VMAT radiotherapy.
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Affiliation(s)
- Haifeng Zhang
- Radiation Oncology Department, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Yanjun Yu
- Radiation Oncology Department, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fuli Zhang
- Radiation Oncology Department, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China
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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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Luan S, Ding Y, Wei C, Huang Y, Yuan Z, Quan H, Ma C, Zhu B, Xue X, Wei W, Wang X. PRT-Net: a progressive refinement transformer for dose prediction to guide ovarian transposition. Front Oncol 2024; 14:1372424. [PMID: 38884079 PMCID: PMC11177340 DOI: 10.3389/fonc.2024.1372424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/29/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction Young cervical cancer patients who require ovarian transposition usually have their ovaries moved away from the pelvic radiotherapy (RT) field before radiotherapy. The dose of ovaries during radiotherapy is closely related to the location of the ovaries. To protect ovarian function and avoid ovarian dose exceeding the limits, a safe location of transposed ovary must be determined prior to surgery. Methods For this purpose, we input the patient's preoperative CT into a neural network model to predict the dose distribution. Surgeons were able to quickly locate low-dose regions based on the dose distribution before surgery, thus determining the safe location of the transposed ovary. In this work, we proposed a new progressive refinement transformer model PRT-Net that can generate dose prediction at multiple scale resolutions in one forward propagation, and refine the dose prediction using prediction details from low to high resolution based on a deep supervision strategy. A multi-loss function fusion algorithm was also built to fit the prediction results under different loss dimensions. The clinical feasibility of the method was verified through an actual cases. Results and discussion Therefore, using PRT-Net to predict the dose distribution by preoperative CT in cervical cancer patients can assist clinicians to perform ovarian transposition surgery and prevent patients' ovaries from exceeding the prescribed dose limit in postoperative radiotherapy.
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Affiliation(s)
- Shunyao Luan
- The Institute of School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Ding
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Changchao Wei
- Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, Center for Theoretical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Yi Huang
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zilong Yuan
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hong Quan
- Key Laboratory of Artificial Micro and Nano-structures of Ministry of Education, Center for Theoretical Physics, School of Physics and Technology, Wuhan University, Wuhan, China
| | - Chi Ma
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
| | - Benpeng Zhu
- The Institute of School of Integrated Circuits, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Xudong Xue
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Wei
- Department of Radiation Oncology, Hubei Cancer Hospital, TongJi Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiao Wang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States
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Shan G, Yu S, Lai Z, Xuan Z, Zhang J, Wang B, Ge Y. A Review of Artificial Intelligence Application for Radiotherapy. Dose Response 2024; 22:15593258241263687. [PMID: 38912333 PMCID: PMC11193352 DOI: 10.1177/15593258241263687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 05/03/2024] [Indexed: 06/25/2024] Open
Abstract
Background and Purpose Artificial intelligence (AI) is a technique which tries to think like humans and mimic human behaviors. It has been considered as an alternative in a lot of human-dependent steps in radiotherapy (RT), since the human participation is a principal uncertainty source in RT. The aim of this work is to provide a systematic summary of the current literature on AI application for RT, and to clarify its role for RT practice in terms of clinical views. Materials and Methods A systematic literature search of PubMed and Google Scholar was performed to identify original articles involving the AI applications in RT from the inception to 2022. Studies were included if they reported original data and explored the clinical applications of AI in RT. Results The selected studies were categorized into three aspects of RT: organ and lesion segmentation, treatment planning and quality assurance. For each aspect, this review discussed how these AI tools could be involved in the RT protocol. Conclusions Our study revealed that AI was a potential alternative for the human-dependent steps in the complex process of RT.
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Affiliation(s)
- Guoping Shan
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
- Zhejiang Cancer Hospital, Hangzhou, China
| | - Shunfei Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhongjun Lai
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Zhiqiang Xuan
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Jie Zhang
- Zhejiang Cancer Hospital, Hangzhou, China
| | | | - Yun Ge
- School of Electronic Science and Engineering, Nanjing University, Nanjing, China
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Cai W, Ding S, Li H, Zhou X, Dou W, Zhou L, Song T, Li Y. Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma. Radiat Oncol 2024; 19:39. [PMID: 38509540 PMCID: PMC10956235 DOI: 10.1186/s13014-024-02401-0] [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: 08/14/2023] [Accepted: 01/09/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND At present, the implementation of intensity-modulated radiation therapy (IMRT) treatment planning for geometrically complex nasopharyngeal carcinoma (NPC) through manual trial-and-error fashion presents challenges to the improvement of planning efficiency and the obtaining of high-consistency plan quality. This paper aims to propose an automatic IMRT plan generation method through fluence prediction and further plan fine-tuning for patients with NPC and evaluates the planning efficiency and plan quality. METHODS A total of 38 patients with NPC treated with nine-beam IMRT were enrolled in this study and automatically re-planned with the proposed method. A trained deep learning model was employed to generate static field fluence maps for each patient with 3D computed tomography images and structure contours as input. Automatic IMRT treatment planning was achieved by using its generated dose with slight tightening for further plan fine-tuning. Lastly, the plan quality was compared between automatic plans and clinical plans. RESULTS The average time for automatic plan generation was less than 4 min, including fluence maps prediction with a python script and automated plan tuning with a C# script. Compared with clinical plans, automatic plans showed better conformity and homogeneity for planning target volumes (PTVs) except for the conformity of PTV-1. Meanwhile, the dosimetric metrics for most organs at risk (OARs) were ameliorated in the automatic plan, especially Dmax of the brainstem and spinal cord, and Dmean of the left and right parotid glands significantly decreased (P < 0.05). CONCLUSION We have successfully implemented an automatic IMRT plan generation method for patients with NPC. This method shows high planning efficiency and comparable or superior plan quality than clinical plans. The qualitative results before and after the plan fine-tuning indicates that further optimization using dose objectives generated by predicted fluence maps is crucial to obtain high-quality automatic plans.
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Affiliation(s)
- Wenwen Cai
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Shouliang Ding
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China
| | - Huali Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Xuanru Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Wen Dou
- Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
| | - Ting Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - Yongbao Li
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, China.
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Wu Z, Liu M, Pang Y, Deng L, Yang Y, Wu Y. A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy. Technol Cancer Res Treat 2024; 23:15330338241242654. [PMID: 38584413 PMCID: PMC11005497 DOI: 10.1177/15330338241242654] [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: 09/20/2023] [Revised: 12/19/2023] [Accepted: 02/19/2024] [Indexed: 04/09/2024] Open
Abstract
Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients' plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.
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Affiliation(s)
- Zhe Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China
- Department of Radiation Oncology, Zigong Disease Prevention and Control Center Mental Health Center, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Mujun Liu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ya Pang
- Department of Radiation Oncology, Zigong Disease Prevention and Control Center Mental Health Center, Zigong First People's Hospital, Zigong, Sichuan, China
| | - Lihua Deng
- Department of Radiology, The First Affiliated Hospital of the Army Medical University, Chongqing, China
| | - Yi Yang
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China
| | - Yi Wu
- Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China
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Shen Y, Tang X, Lin S, Jin X, Ding J, Shao M. Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy. Med Phys 2024; 51:545-555. [PMID: 37748133 DOI: 10.1002/mp.16743] [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: 01/03/2023] [Revised: 07/21/2023] [Accepted: 08/26/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel-dose features has not been extensively studied. PURPOSE This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity-modulated radiation therapy (IMRT) treatment planning. METHODS A double-UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high-dose region-controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O-P)] and [body avoids OARs & PTV(B-OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two-step process. In the beginning, the plans of two extra cases with and without low-dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O-P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O-P FEs from first step were constrained again but weakly and the dose of the B-OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double-UNet dose prediction model trained by low and high region-controlled plans (method 2). RESULTS The mean differences in dose metrics between the UNet-predicted dose and the clinical plans were: 0.47 Gy for bladder D50% ; 0.62 Gy for rectum D50% ; 0% for small intestine V30Gy ; 1% for small intestine V40Gy ; 4% for left femoral head V30Gy ; and 6% for right femoral head V30Gy . The reductions in mean dose (p < 0.001) after FE-based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7 Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7 Gy), rectum (1.0 Gy), and small intestine (0.6 Gy), while maintaining PTV homogeneity and conformity. CONCLUSION FE-based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low-dose regions.
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Affiliation(s)
- Yichao Shen
- Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China
| | - Xingni Tang
- Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China
| | - Sara Lin
- Petrone associates, Staten Island, New York, USA
| | - Xiance Jin
- Radiotherapy Center Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China
| | - Jiapei Ding
- Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China
| | - Minghai Shao
- Department of Radiation Oncology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China
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Ma L, Yang Y, Ma J, Mao L, Li X, Feng L, Abulimiti M, Xiang X, Fu F, Tan Y, Zhang W, Li YX, Jin J, Li N. Correlation between AI-based CT organ features and normal lung dose in adjuvant radiotherapy following breast-conserving surgery: a multicenter prospective study. BMC Cancer 2023; 23:1085. [PMID: 37946125 PMCID: PMC10636953 DOI: 10.1186/s12885-023-11554-2] [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: 06/24/2023] [Accepted: 10/20/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Radiation pneumonitis (RP) is one of the common side effects after adjuvant radiotherapy in breast cancer. Irradiation dose to normal lung was related to RP. We aimed to propose an organ features based on deep learning (DL) model and to evaluate the correlation between normal lung dose and organ features. METHODS Patients with pathology-confirmed invasive breast cancer treated with adjuvant radiotherapy following breast-conserving surgery in four centers were included. From 2019 to 2020, a total of 230 patients from four nationwide centers in China were screened, of whom 208 were enrolled for DL modeling, and 22 patients from another three centers formed the external testing cohort. The subset of the internal testing cohort (n = 42) formed the internal correlation testing cohort for correlation analysis. The outline of the ipsilateral breast was marked with a lead wire before the scanning. Then, a DL model based on the High-Resolution Net was developed to detect the lead wire marker in each slice of the CT images automatically, and an in-house model was applied to segment the ipsilateral lung region. The mean and standard deviation of the distance error, the average precision, and average recall were used to measure the performance of the lead wire marker detection model. Based on these DL model results, we proposed an organ feature, and the Pearson correlation coefficient was calculated between the proposed organ feature and ipsilateral lung volume receiving 20 Gray (Gy) or more (V20). RESULTS For the lead wire marker detection model, the mean and standard deviation of the distance error, AP (5 mm) and AR (5 mm) reached 3.415 ± 4.529, 0.860, 0.883, and 4.189 ± 8.390, 0.848, 0.830 in the internal testing cohort and external testing cohort, respectively. The proposed organ feature calculated from the detected marker correlated with ipsilateral lung V20 (Pearson correlation coefficient, 0.542 with p < 0.001 in the internal correlation testing cohort and 0.554 with p = 0.008 in the external testing cohort). CONCLUSIONS The proposed artificial Intelligence-based CT organ feature was correlated with normal lung dose in adjuvant radiotherapy following breast-conserving surgery in patients with invasive breast cancer. TRIAL REGISTRATION NCT05609058 (08/11/2022).
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Affiliation(s)
- Li Ma
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Yongjing Yang
- Department of Radiation Oncology, Jilin Cancer Hospital, Changchun, Jilin, 130012, China
| | - Jiabao Ma
- Department of Radiation Oncology, Sichuan Cancer Hospital & Research Institute, No. 55, the 4Th Section, Renmin South Road, Chengdu, 610041, China
| | - Li Mao
- AI Lab, Deepwise Healthcare, Beijing, 100080, People's Republic of China
| | - Xiuli Li
- AI Lab, Deepwise Healthcare, Beijing, 100080, People's Republic of China
| | - Lingling Feng
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Muyasha Abulimiti
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Xiaoyong Xiang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Fangmeng Fu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Yutong Tan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Wenjue Zhang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Ye-Xiong Li
- Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jing Jin
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
- Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
| | - Ning Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
- Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
- Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Jinzhong, China.
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Cui J, Xiao J, Hou Y, Wu X, Zhou J, Peng X, Wang Y. Unsupervised Domain Adaptive Dose Prediction via Cross-Attention Transformer and Target-Specific Knowledge Preservation. Int J Neural Syst 2023; 33:2350057. [PMID: 37771298 DOI: 10.1142/s0129065723500570] [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] [Indexed: 09/30/2023]
Abstract
Radiotherapy is one of the leading treatments for cancer. To accelerate the implementation of radiotherapy in clinic, various deep learning-based methods have been developed for automatic dose prediction. However, the effectiveness of these methods heavily relies on the availability of a substantial amount of data with labels, i.e. the dose distribution maps, which cost dosimetrists considerable time and effort to acquire. For cancers of low-incidence, such as cervical cancer, it is often a luxury to collect an adequate amount of labeled data to train a well-performing deep learning (DL) model. To mitigate this problem, in this paper, we resort to the unsupervised domain adaptation (UDA) strategy to achieve accurate dose prediction for cervical cancer (target domain) by leveraging the well-labeled high-incidence rectal cancer (source domain). Specifically, we introduce the cross-attention mechanism to learn the domain-invariant features and develop a cross-attention transformer-based encoder to align the two different cancer domains. Meanwhile, to preserve the target-specific knowledge, we employ multiple domain classifiers to enforce the network to extract more discriminative target features. In addition, we employ two independent convolutional neural network (CNN) decoders to compensate for the lack of spatial inductive bias in the pure transformer and generate accurate dose maps for both domains. Furthermore, to enhance the performance, two additional losses, i.e. a knowledge distillation loss (KDL) and a domain classification loss (DCL), are incorporated to transfer the domain-invariant features while preserving domain-specific information. Experimental results on a rectal cancer dataset and a cervical cancer dataset have demonstrated that our method achieves the best quantitative results with [Formula: see text], [Formula: see text], and HI of 1.446, 1.231, and 0.082, respectively, and outperforms other methods in terms of qualitative assessment.
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Affiliation(s)
- Jiaqi Cui
- School of Computer Science, Sichuan University, Chengdu, P. R. China
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Yun Hou
- Agile and Intelligent Computing Key Laboratory, Southwest China Institute of Electronic Technology, Chengdu, P. R. China
| | - Xi Wu
- School of Computer Science, Chengdu University of Information Technology, P. R. China
| | - Jiliu Zhou
- School of Computer Science, Sichuan University, Chengdu, P. R. China
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, P. R. China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, P. R. China
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10
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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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Jiao Z, Peng X, Wang Y, Xiao J, Nie D, Wu X, Wang X, Zhou J, Shen D. TransDose: Transformer-based radiotherapy dose prediction from CT images guided by super-pixel-level GCN classification. Med Image Anal 2023; 89:102902. [PMID: 37482033 DOI: 10.1016/j.media.2023.102902] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/13/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
Radiotherapy is a mainstay treatment for cancer in clinic. An excellent radiotherapy treatment plan is always based on a high-quality dose distribution map which is produced by repeated manual trial-and-errors of experienced experts. To accelerate the radiotherapy planning process, many automatic dose distribution prediction methods have been proposed recently and achieved considerable fruits. Nevertheless, these methods require certain auxiliary inputs besides CT images, such as segmentation masks of the tumor and organs at risk (OARs), which limits their prediction efficiency and application potential. To address this issue, we design a novel approach named as TransDose for dose distribution prediction that treats CT images as the unique input in this paper. Specifically, instead of inputting the segmentation masks to provide the prior anatomical information, we utilize a super-pixel-based graph convolutional network (GCN) to extract category-specific features, thereby compensating the network for the necessary anatomical knowledge. Besides, considering the strong continuous dependency between adjacent CT slices as well as adjacent dose maps, we embed the Transformer into the backbone, and make use of its superior ability of long-range sequence modeling to endow input features with inter-slice continuity message. To our knowledge, this is the first network that specially designed for the task of dose prediction from only CT images without ignoring necessary anatomical structure. Finally, we evaluate our model on two real datasets, and extensive experiments demonstrate the generalizability and advantages of our method.
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Affiliation(s)
- Zhengyang Jiao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, China.
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dong Nie
- Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Xi Wu
- School of Computer Science, Chengdu University of Information Technology, China
| | | | - Jiliu Zhou
- School of Computer Science, Sichuan University, Chengdu, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
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13
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Li J, Song Y, Wu Y, Liang L, Li G, Bai S. Clinical evaluation on automatic segmentation results of convolutional neural networks in rectal cancer radiotherapy. Front Oncol 2023; 13:1158315. [PMID: 37731629 PMCID: PMC10508953 DOI: 10.3389/fonc.2023.1158315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 08/11/2023] [Indexed: 09/22/2023] Open
Abstract
Purpose Image segmentation can be time-consuming and lacks consistency between different oncologists, which is essential in conformal radiotherapy techniques. We aimed to evaluate automatic delineation results generated by convolutional neural networks (CNNs) from geometry and dosimetry perspectives and explore the reliability of these segmentation tools in rectal cancer. Methods Forty-seven rectal cancer cases treated from February 2018 to April 2019 were randomly collected retrospectively in our cancer center. The oncologists delineated regions of interest (ROIs) on planning CT images as the ground truth, including clinical target volume (CTV), bladder, small intestine, and femoral heads. The corresponding automatic segmentation results were generated by DeepLabv3+ and ResUNet, and we also used Atlas-Based Autosegmentation (ABAS) software for comparison. The geometry evaluation was carried out using the volumetric Dice similarity coefficient (DSC) and surface DSC, and critical dose parameters were assessed based on replanning optimized by clinically approved or automatically generated CTVs and organs at risk (OARs), i.e., the Planref and Plantest. Pearson test was used to explore the correlation between geometric metrics and dose parameters. Results In geometric evaluation, DeepLabv3+ performed better in DCS metrics for the CTV (volumetric DSC, mean = 0.96, P< 0.01; surface DSC, mean = 0.78, P< 0.01) and small intestine (volumetric DSC, mean = 0.91, P< 0.01; surface DSC, mean = 0.62, P< 0.01), ResUNet had advantages in volumetric DSC of the bladder (mean = 0.97, P< 0.05). For critical dose parameters analysis between Planref and Plantest, there was a significant difference for target volumes (P< 0.01), and no significant difference was found for the ResUNet-generated small intestine (P > 0.05). For the correlation test, a negative correlation was found between DSC metrics (volumetric, surface DSC) and dosimetric parameters (δD95, δD95, HI, CI) for target volumes (P< 0.05), and no significant correlation was found for most tests of OARs (P > 0.05). Conclusions CNNs show remarkable repeatability and time-saving in automatic segmentation, and their accuracy also has a certain potential in clinical practice. Meanwhile, clinical aspects, such as dose distribution, may need to be considered when comparing the performance of auto-segmentation methods.
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Affiliation(s)
- Jing Li
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ying Song
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Machine Intelligence Laboratory, College of Computer Science, Chengdu, China
| | - Yongchang Wu
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Lan Liang
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Guangjun Li
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Sen Bai
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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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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Jia Q, Zheng C, Li Y, Guo F, Zhou L, Song T. A predicted three-dimensional dose sequence based treatment planning optimization method for gynecologic IMRT. Med Eng Phys 2023; 118:104011. [PMID: 37536834 DOI: 10.1016/j.medengphy.2023.104011] [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: 12/06/2022] [Revised: 05/25/2023] [Accepted: 06/22/2023] [Indexed: 08/05/2023]
Abstract
In knowledge-based treatment planning (KBTP) for intensity-modulated radiation therapy (IMRT), the quality of the plan is dependent on the sophistication of the predicted dosimetric information and its application. In this paper, we propose a KBTP method that based on the effective and reasonable utilization of a three-dimensional (3D) dose prediction on planning optimization. We used an organs-at-risk (OARs) dose distribution prediction model to create a voxel-based dose sequence based optimization objective for OARs doses. This objective was used to reformulate a traditional fluence map optimization model, which involves a tolerable spatial re-assignment of the predicted dose distribution to the OAR voxels based on their current doses' positions at a sorted dose sequencing. The feasibility of this method was evaluated with ten gynecology (GYN) cancer IMRT cases by comparing its generated plan quality with the original clinical plan. Results showed feasible plan by proposed method, with comparable planning target volume (PTV) dose coverage and greater dose sparing of the OARs. Among ten GYN cases, the average V30 and V45 of rectum were decreased by 4%±4% (p = 0.02) and 4%±3% (p<0.01), respectively. V30 and V45 of bladder were decreased by 8%±2% (p<0.01) and 3%±2% (p<0.01), respectively. Our predicted dose sequence-based planning optimization method for GYN IMRT offered a flexible use of predicted 3D doses while ensuring the output plan consistency.
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Affiliation(s)
- Qiyuan Jia
- Department of Radiotherapy Technology, Ningbo No.2 Hospital, Ningbo, Zhejiang 315310, China; School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Chuancheng Zheng
- Department of Radiotherapy Technology, Ningbo No.2 Hospital, Ningbo, Zhejiang 315310, China
| | - Yongbao Li
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong 510060, China
| | - Futong Guo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
| | - Ting Song
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China.
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16
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Bohannon D, Janopaul-Naylor J, Rudra S, Yang X, Chang CW, Wang Y, Ma C, Patel SA, McDonald MW, Zhou J. Prediction of plan adaptation in head and neck cancer proton therapy using clinical, radiographic, and dosimetric features. Acta Oncol 2023:1-8. [PMID: 37335043 DOI: 10.1080/0284186x.2023.2224050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/01/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE Because proton head and neck (HN) treatments are sensitive to anatomical changes, plan adaptation (re-plan) during the treatment course is needed for a significant portion of patients. We aim to predict re-plan at plan review stage for HN proton therapy with a neural network (NN) model trained with patients' dosimetric and clinical features. The model can serve as a valuable tool for planners to assess the probability of needing to revise the current plan. METHODS AND MATERIALS Mean beam dose heterogeneity index (BHI), defined as the ratio of the maximum beam dose to the prescription dose, plan robustness features (clinical target volume (CTV), V100 changes, and V100 > 95% passing rates in 21 robust evaluation scenarios), as well as clinical features (e.g., age, tumor site, and surgery/chemotherapy status) were gathered from 171 patients treated at our proton center in 2020, with a median age of 64 and stages from I-IVc across 13 HN sites. Statistical analyses of dosimetric parameters and clinical features were conducted between re-plan and no-replan groups. A NN was trained and tested using these features. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of the prediction model. A sensitivity analysis was done to determine feature importance. RESULTS Mean BHI in the re-plan group was significantly higher than the no-replan group (p < .01). Tumor site (p < .01), chemotherapy status (p < .01), and surgery status (p < .01) were significantly correlated to re-plan. The model had sensitivities/specificities of 75.0%/77.4%, respectively, and an area under the ROC curve of .855. CONCLUSION There are several dosimetric and clinical features that correlate to re-plans, and NNs trained with these features can be used to predict HN re-plans, which can be used to reduce re-plan rate by improving plan quality.
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Affiliation(s)
- D Bohannon
- Department of Nuclear and Radiological Engineering, Georgia institute of Technology, Atlanta, GA, USA
| | - J Janopaul-Naylor
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S Rudra
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - X Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C W Chang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - Y Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - C Ma
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - S A Patel
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - M W McDonald
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
| | - J Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA, USA
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Li F, Niu S, Han Y, Zhang Y, Dong Z, Zhu J. Multi-stage framework with difficulty-aware learning for progressive dose prediction. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Oh K, Gronberg MP, Netherton TJ, Sengupta B, Cardenas CE, Court LE, Ford EC. A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system. Phys Imaging Radiat Oncol 2023; 26:100440. [PMID: 37342210 PMCID: PMC10277917 DOI: 10.1016/j.phro.2023.100440] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 04/15/2023] [Accepted: 04/17/2023] [Indexed: 06/22/2023] Open
Abstract
Background and purpose A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.
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Affiliation(s)
- Kyuhak Oh
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mary P. Gronberg
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tucker J. Netherton
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Bishwambhar Sengupta
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama, Birmingham, AL 35233, USA
| | - Laurence E. Court
- Department of Radiation Physics, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Eric C. Ford
- Department of Radiation Oncology, University of Washington Medical Center, Seattle, WA 98195, USA
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Chen X, Zhu J, Yang B, Chen D, Men K, Dai J. Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning. Front Oncol 2023; 13:1041769. [PMID: 36925918 PMCID: PMC10012276 DOI: 10.3389/fonc.2023.1041769] [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: 09/11/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
Purpose Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance. Materials and methods One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation. Results The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model. Conclusions The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.
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Affiliation(s)
- Xinyuan Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China
| | - Ji Zhu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bining Yang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Deqi Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kuo Men
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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20
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Cui J, Jiao Z, Wei Z, Hu X, Wang Y, Xiao J, Peng X. CT-Only Radiotherapy: An Exploratory Study for Automatic Dose Prediction on Rectal Cancer Patients Via Deep Adversarial Network. Front Oncol 2022; 12:875661. [PMID: 35924164 PMCID: PMC9341484 DOI: 10.3389/fonc.2022.875661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/14/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose Current deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically. Materials and Methods We developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients. Results Experimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas). Conclusion The proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT
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Affiliation(s)
- Jiaqi Cui
- School of Computer Science, Sichuan University, Chengdu, China
| | - Zhengyang Jiao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Zhigong Wei
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolin Hu
- West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Wang
- School of Computer Science, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yan Wang, ; Jianghong Xiao, ; Xingchen Peng,
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21
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VMAT dose prediction in radiotherapy by using progressive refinement UNet. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.11.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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22
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Explainable attention guided adversarial deep network for 3D radiotherapy dose distribution prediction. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108324] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Mask-Free Radiotherapy Dose Prediction via Multi-Task Learning. 2022 IEEE 19TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) 2022. [DOI: 10.1109/isbi52829.2022.9761505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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24
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Zeng J, Cao C, Peng X, Xiao J, Zu C, Wu X, Zhou J, Wang Y. Two-Phase Progressive Deep Transfer Learning for Cervical Cancer Dose Map Prediction. 2022 IEEE 19TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) 2022. [DOI: 10.1109/isbi52829.2022.9761628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Jie Zeng
- Sichuan University,School of Computer Science,China
| | | | - Xingchen Peng
- Sichuan University,Cancer Center, West China Hospital,Department of Biotherapy,China
| | - Jianghong Xiao
- Sichuan University,Cancer Center, West China Hospital,Department of Radiation Oncology,China
| | - Chen Zu
- JD.com,Department of Risk Controlling Research,China
| | - Xi Wu
- Chengdu University of Information Technology,School of Computer Science,China
| | - Jiliu Zhou
- Sichuan University,School of Computer Science,China
| | - Yan Wang
- Sichuan University,School of Computer Science,China
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25
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Dose prediction via distance-guided deep learning: initial development for nasopharyngeal carcinoma radiotherapy. Radiother Oncol 2022; 170:198-204. [DOI: 10.1016/j.radonc.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 03/21/2022] [Accepted: 03/23/2022] [Indexed: 11/20/2022]
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26
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Zhan B, Xiao J, Cao C, Peng X, Zu C, Zhou J, Wang Y. Multi-constraint generative adversarial network for dose prediction in radiotherapy. Med Image Anal 2021; 77:102339. [PMID: 34990905 DOI: 10.1016/j.media.2021.102339] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 02/05/2023]
Abstract
Radiation therapy (RT) is regarded as the primary treatment for cancer in the clinic, aiming to deliver an accurate dose to the planning target volume (PTV) while protecting the surrounding organs at risk (OARs). To improve the effectiveness of the treatment planning, deep learning methods are widely adopted to predict dose distribution maps for clinical treatment planning. In this paper, we present a novel multi-constraint dose prediction model based on generative adversarial network, named Mc-GAN, to automatically predict the dose distribution map from the computer tomography (CT) images and the masks of PTV and OARs. Specifically, the generator is an embedded UNet-like structure with dilated convolution to capture both the global and local information. During the feature extraction, a dual attention module (DAM) is embedded to force the generator to take more heed of internal semantic relevance. To improve the prediction accuracy, two additional losses, i.e., the locality-constrained loss (LCL) and the self-supervised perceptual loss (SPL), are introduced besides the conventional global pixel-level loss and adversarial loss. Concretely, the LCL tries to focus on the predictions of locally important areas while the SPL aims to prevent the predicted dose maps from the possible distortion at the feature level. Evaluated on two in-house datasets, our proposed Mc-GAN has been demonstrated to outperform other state-of-the-art methods in almost all PTV and OARs criteria.
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Affiliation(s)
- Bo Zhan
- School of Computer Science, Sichuan University, China
| | - Jianghong Xiao
- Department of Radiation Oncology, Cancer Center West China Hospital, Sichuan University, China
| | - Chongyang Cao
- School of Computer Science, Sichuan University, China
| | - Xingchen Peng
- Department of Biotherapy, Cancer Center West China Hospital, Sichuan University, China
| | - Chen Zu
- Department of Risk Controlling Research, JD.com, China
| | - Jiliu Zhou
- School of Computer Science, Sichuan University, China; School of Computer Science, Chengdu University of Information Technology, China
| | - Yan Wang
- School of Computer Science, Sichuan University, China.
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27
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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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28
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Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021; 22:16-44. [PMID: 34231970 PMCID: PMC8364264 DOI: 10.1002/acm2.13337] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/18/2022] Open
Abstract
This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.
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Affiliation(s)
- Shadab Momin
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yabo Fu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Jeffrey D. Bradley
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Walter J. Curran
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaGAUSA
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29
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The status of medical physics in radiotherapy in China. Phys Med 2021; 85:147-157. [PMID: 34010803 DOI: 10.1016/j.ejmp.2021.05.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 05/01/2021] [Accepted: 05/03/2021] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To present an overview of the status of medical physics in radiotherapy in China, including facilities and devices, occupation, education, research, etc. MATERIALS AND METHODS: The information about medical physics in clinics was obtained from the 9-th nationwide survey conducted by the China Society for Radiation Oncology in 2019. The data of medical physics in education and research was collected from the publications of the official and professional organizations. RESULTS By 2019, there were 1463 hospitals or institutes registered to practice radiotherapy and the number of accelerators per million population was 1.5. There were 4172 medical physicists working in clinics of radiation oncology. The ratio between the numbers of radiation oncologists and medical physicists is 3.51. Approximately, 95% of medical physicists have an undergraduate or graduate degrees in nuclear physics and biomedical engineering. 86% of medical physicists have certificates issued by the Chinese Society of Medical Physics. There has been a fast growth of publications by authors from mainland of China in the top international medical physics and radiotherapy journals since 2018. CONCLUSIONS Demand for medical physicists in radiotherapy increased quickly in the past decade. The distribution of radiotherapy facilities in China became more balanced. High quality continuing education and training programs for medical physicists are deficient in most areas. The role of medical physicists in the clinic has not been clearly defined and their contributions have not been fully recognized by the community.
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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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31
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Jiao SX, Wang ML, Chen LX, Liu XW. Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning. Sci Rep 2021; 11:3117. [PMID: 33542427 PMCID: PMC7862493 DOI: 10.1038/s41598-021-82749-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 01/25/2021] [Indexed: 12/13/2022] Open
Abstract
The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R2 and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D98, D95, D1 and uniformity index (UI). The OAR dose was compared using the maximum dose, V30 and V40. The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D98,D95,D1 and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R2, MAE, the maximum dose, V30 and V40 (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.
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Affiliation(s)
- Sheng Xiu Jiao
- School of Physics, Sun Yat-Sen University, 135 Xin Gang Road West, Guangzhou, 510275, China
| | - Ming Li Wang
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, 651 Dong Feng Road East, Guangzhou, 510060, China
| | - Li Xin Chen
- State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, 651 Dong Feng Road East, Guangzhou, 510060, China.
| | - Xiao-Wei Liu
- School of Physics, Sun Yat-Sen University, 135 Xin Gang Road West, Guangzhou, 510275, China.
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