1
|
Jiang C, Ji T, Qiao Q. Application and progress of artificial intelligence in radiation therapy dose prediction. Clin Transl Radiat Oncol 2024; 47:100792. [PMID: 38779524 PMCID: PMC11109740 DOI: 10.1016/j.ctro.2024.100792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Radiation therapy (RT) nowadays is a main treatment modality of cancer. To ensure the therapeutic efficacy of patients, accurate dose distribution is often required, which is a time-consuming and labor-intensive process. In addition, due to the differences in knowledge and experience among participants and diverse institutions, the predicted dose are often inconsistent. In last several decades, artificial intelligence (AI) has been applied in various aspects of RT, several products have been implemented in clinical practice and confirmed superiority. In this paper, we will review the research of AI in dose prediction, focusing on the progress in deep learning (DL).
Collapse
Affiliation(s)
- Chen Jiang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Tianlong Ji
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Qiao Qiao
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Zou Z, Gong C, Zeng L, Guan Y, Huang B, Yu X, Liu Q, Zhang M. Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:60-71. [PMID: 38343215 DOI: 10.1007/s10278-023-00930-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 03/02/2024]
Abstract
Pretreatment patient-specific quality assurance (prePSQA) is conducted to confirm the accuracy of the radiotherapy dose delivered. However, the process of prePSQA measurement is time consuming and exacerbates the workload for medical physicists. The purpose of this work is to propose a novel deep learning (DL) network to improve the accuracy and efficiency of prePSQA. A modified invertible and variable augmented network was developed to predict the three-dimensional (3D) measurement-guided dose (MDose) distribution of 300 cancer patients who underwent volumetric modulated arc therapy (VMAT) between 2018 and 2021, in which 240 cases were randomly selected for training, and 60 for testing. For simplicity, the present approach was termed as "IVPSQA." The input data include CT images, radiotherapy dose exported from the treatment planning system, and MDose distribution extracted from the verification system. Adam algorithm was used for first-order gradient-based optimization of stochastic objective functions. The IVPSQA model obtained high-quality 3D prePSQA dose distribution maps in head and neck, chest, and abdomen cases, and outperformed the existing U-Net-based prediction approaches in terms of dose difference maps and horizontal profiles comparison. Moreover, quantitative evaluation metrics including SSIM, MSE, and MAE demonstrated that the proposed approach achieved a good agreement with ground truth and yield promising gains over other advanced methods. This study presented the first work on predicting 3D prePSQA dose distribution by using the IVPSQA model. The proposed method could be taken as a clinical guidance tool and help medical physicists to reduce the measurement work of prePSQA.
Collapse
Affiliation(s)
- Zhongsheng Zou
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Changfei Gong
- Department of Radiation Oncology, 1st Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lingpeng Zeng
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Yu Guan
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Bin Huang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Xiuwen Yu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China
| | - Qiegen Liu
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
| | - Minghui Zhang
- Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
| |
Collapse
|
4
|
Bitterman DS, Gensheimer MF, Jaffray D, Pryma DA, Jiang SB, Morin O, Ginart JB, Upadhaya T, Vallis KA, Buatti JM, Deasy J, Hsiao HT, Chung C, Fuller CD, Greenspan E, Cloyd-Warwick K, Courdy S, Mao A, Barnholtz-Sloan J, Topaloglu U, Hands I, Maurer I, Terry M, Curran WJ, Le QT, Nadaf S, Kibbe W. Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology. JCO Clin Cancer Inform 2023; 7:e2300136. [PMID: 38055914 PMCID: PMC10703125 DOI: 10.1200/cci.23.00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 10/16/2023] [Indexed: 12/08/2023] Open
Abstract
In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.
Collapse
Affiliation(s)
- Danielle S. Bitterman
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - Michael F. Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - David Jaffray
- Department of Radiation Physics, M.D. Anderson Cancer Center, Houston, TX
| | - Daniel A. Pryma
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Steve B. Jiang
- Medical Artificial Intelligence and Automation Laboratory and Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - Olivier Morin
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Jorge Barrios Ginart
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Taman Upadhaya
- Department of Radiation Oncology, MEDomics Laboratory, University of California San Francisco, San Francisco, CA
| | - Katherine A. Vallis
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA
| | - John M. Buatti
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Joseph Deasy
- Department of Radiation Oncology, University of Iowa Carver College of Medicine, Iowa City, IA
| | - H. Timothy Hsiao
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Caroline Chung
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Clifton D. Fuller
- Department of Scientific Affairs, American Society for Radiation Oncology, Arlington, VA
| | - Emily Greenspan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Kristy Cloyd-Warwick
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD
| | | | | | - Jill Barnholtz-Sloan
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
- Center for Informatics, Digital Vertical, City of Hope National Comprehensive Cancer Center, Los Angeles, CA
| | - Umit Topaloglu
- Department of Radiation Oncology, M.D. Anderson Cancer Center, Houston, TX
| | - Isaac Hands
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
- Cancer Research Informatics Shared Resource Facility, University of Kentucky Markey Cancer Center, Lexington, NY
| | | | | | | | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA
| | - Sorena Nadaf
- Department of Radiation Oncology, Emory University, Atlanta, GA
| | - Warren Kibbe
- Cancer Center Informatics Society, Los Angeles, CA
| |
Collapse
|
5
|
Huang C, Vasudevan V, Pastor-Serrano O, Islam MT, Nomura Y, Dubrowski P, Wang JY, Schulz JB, Yang Y, Xing L. Learning image representations for content-based image retrieval of radiotherapy treatment plans. Phys Med Biol 2023; 68:10.1088/1361-6560/accdb0. [PMID: 37068492 PMCID: PMC10259733 DOI: 10.1088/1361-6560/accdb0] [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: 06/14/2022] [Accepted: 04/17/2023] [Indexed: 04/19/2023]
Abstract
Objective.In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context.Approach.Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github.Main results.The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network.Significance.Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.
Collapse
Affiliation(s)
- Charles Huang
- Department of Bioengineering, Stanford University, Stanford, USA
| | - Varun Vasudevan
- Institute for Computational & Mathematical Engineering, Stanford University, Stanford, USA
| | - Oscar Pastor-Serrano
- Department of Radiation Oncology, Stanford University, Stanford, USA
- Department of Radiation Science and Technology, Delft University of Technology, the Netherlands
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Yusuke Nomura
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Piotr Dubrowski
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Jen-Yeu Wang
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Joseph B. Schulz
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Stanford, USA
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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
| |
Collapse
|
8
|
Niu T, Tsui T, Zhao W. AI-Augmented Images for X-Ray Guiding Radiation Therapy Delivery. Semin Radiat Oncol 2022; 32:365-376. [DOI: 10.1016/j.semradonc.2022.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
|
9
|
A Survey on Deep Learning for Precision Oncology. Diagnostics (Basel) 2022; 12:diagnostics12061489. [PMID: 35741298 PMCID: PMC9222056 DOI: 10.3390/diagnostics12061489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 12/27/2022] Open
Abstract
Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions.
Collapse
|
10
|
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]
|
11
|
Peng Y, Liu Y, Chen Z, Zhang G, Ma C, Xu S, Yin Y. Accuracy Improvement Method Based on Characteristic Database Classification for IMRT Dose Prediction in Cervical Cancer: Scientifically Training Data Selection. Front Oncol 2022; 12:808580. [PMID: 35311133 PMCID: PMC8927290 DOI: 10.3389/fonc.2022.808580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose Consistent training and testing datasets can lead to good performance for deep learning (DL) models. However, a large high-quality training dataset for unusual clinical scenarios is usually not easy to collect. The work aims to find optimal training data collection strategies for DL-based dose prediction models. Materials and Methods A total of 325 clinically approved cervical IMRT plans were utilized. We designed comparison experiments to investigate the impact of (1) beam angles, (2) the number of beams, and (3) patient position for DL dose prediction models. In addition, a novel geometry-based beam mask generation method was proposed to provide beam setting information in the model training process. What is more, we proposed a new training strategy named “full-database pre-trained strategy”. Results The model trained with a homogeneous dataset with the same beam settings achieved the best performance [mean prediction errors of planning target volume (PTV), bladder, and rectum: 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%] compared with that trained with large mixed beam setting plans (mean errors of PTV, bladder, and rectum: 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4%). A homogeneous dataset is more accessible to train an accurate dose prediction model (mean errors of PTV, bladder and rectum: 2.2 ± 0.15%, 5 ± 2.1%, and 3.23 ± 1.53%) than a non-homogeneous one (mean errors of PTV, bladder and rectum: 2.55 ± 0.12%, 6.33 ± 2.46%, and 4.76 ± 2.91%) without other processing approaches. The added beam mask can constantly improve the model performance, especially for datasets with different beam settings (mean errors of PTV, bladder, and rectum improved from 0.8 ± 0.14%, 5.03 ± 2.2%, and 4.45 ± 1.4% to 0.29 ± 0.15%, 3.1 ± 2.55%, and 3.15 ± 1.69%). Conclusions A consistent dataset is recommended to form a patient-specific IMRT dose prediction model. When a consistent dataset is not accessible to collect, a large dataset with different beam angles and a training model with beam information can also get a relatively good model. The full-database pre-trained strategies can rapidly form an accuracy model from a pre-trained model. The proposed beam mask can effectively improve the model performance. Our study may be helpful for further dose prediction studies in terms of training strategies or database establishment.
Collapse
Affiliation(s)
- Yiru Peng
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yaoying Liu
- School of Physics, Beihang University, Beijing, China
| | | | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, China
| | - Changsheng Ma
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shouping Xu
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Yin
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| |
Collapse
|
12
|
Qilin Z, Peng B, Ang Q, Weijuan J, Ping J, Hongqing Z, Bin D, Ruijie Y. The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer. J Appl Clin Med Phys 2022; 23:e13583. [PMID: 35262273 PMCID: PMC9195039 DOI: 10.1002/acm2.13583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 12/23/2021] [Accepted: 02/19/2022] [Indexed: 11/09/2022] Open
Abstract
Purpose To develop a 3D‐Unet dose prediction model to predict the three‐dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization. Methods One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D‐Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (Dmean), the received dose of 2 cm3 (D2cc), the percentage volume of receiving 40 Gy dose of organs‐at‐risk (V40), planning target volume (PTV) D98%, and homogeneity index (HI), (d) dose–volume histograms (DVHs). Results The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference. Conclusion A 3D‐Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.
Collapse
Affiliation(s)
- Zhang Qilin
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Bao Peng
- Center for Data ScienceAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
| | - Qu Ang
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Jiang Weijuan
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Jiang Ping
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Zhuang Hongqing
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| | - Dong Bin
- Beijing International Center for Mathematical ResearchPeking UniversityBeijingChina
| | - Yang Ruijie
- Department of Radiation OncologyPeking University Third HospitalBeijingChina
| |
Collapse
|
13
|
Ma M, Kidd E, Fahimian BP, Han B, Niedermayr TR, Hristov D, Xing L, Yang Y. Dose Prediction for Cervical Cancer Brachytherapy Using 3-D Deep Convolutional Neural Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022. [DOI: 10.1109/trpms.2021.3098507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
14
|
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.
Collapse
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.
| |
Collapse
|
15
|
Liu Y, Chen Z, Wang J, Wang X, Qu B, Ma L, Zhao W, Zhang G, Xu S. Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy. Front Oncol 2021; 11:752007. [PMID: 34858825 PMCID: PMC8631763 DOI: 10.3389/fonc.2021.752007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/21/2021] [Indexed: 01/14/2023] Open
Abstract
Purpose This study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs). Methods A convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the "3D Dense-U-Net", which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis. Results We found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one. Conclusions It is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.
Collapse
Affiliation(s)
- Yaoying Liu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China.,School of Physics, Beihang University, Beijing, China
| | | | - Jinyuan Wang
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Xiaoshen Wang
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Baolin Qu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Lin Ma
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| | - Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, the First Medical Center of the People's Liberation Army General Hospital, Beijing, China
| |
Collapse
|
16
|
Kalantar R, Lin G, Winfield JM, Messiou C, Lalondrelle S, Blackledge MD, Koh DM. Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges. Diagnostics (Basel) 2021; 11:1964. [PMID: 34829310 PMCID: PMC8625809 DOI: 10.3390/diagnostics11111964] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/14/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022] Open
Abstract
The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.
Collapse
Affiliation(s)
- Reza Kalantar
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Jessica M. Winfield
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Christina Messiou
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Susan Lalondrelle
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| | - Matthew D. Blackledge
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
| | - Dow-Mu Koh
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London SM2 5NG, UK; (R.K.); (J.M.W.); (C.M.); (S.L.); (D.-M.K.)
- Department of Radiology, The Royal Marsden Hospital, London SW3 6JJ, UK
| |
Collapse
|
17
|
On dose cube pixel spacing pre-processing for features extraction stability in dosiomic studies. Phys Med 2021; 90:108-114. [PMID: 34600351 DOI: 10.1016/j.ejmp.2021.09.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 09/06/2021] [Accepted: 09/17/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Dosiomics allows to parameterize regions of interest (ROIs) and to produce quantitative dose features encoding the spatial and statistical distribution of radiotherapy dose. The stability of dosiomics features extraction on dose cube pixel spacing variation has been investigated in this study. MATERIAL AND METHODS Based on 17 clinical delivered dose distributions (Pn), dataset has been generated considering all the possible combinations of four dose grid resolutions and two calculation algorithms. Each dose voxel cube has been post-processed considering 4 different dose cube pixel spacing values: 1x1x1, 2x2x2, 3x3x3 mm3 and the one equal to the planning CT. Dosiomics features extraction has been performed from four different ROIs. The stability of each extracted dosiomic feature has been analyzed in terms of coefficient of variation (CV) intraclass correlation coefficient (ICC). RESULTS The highest CV mean values were observed for PTV ROI and for the grey level size zone matrix features family. On the other hand, the lowest CV mean values have been found for RING ROI for the grey level co-occurrence matrix features family. P3 showed the highest percentage of CV >1 (1.14%) followed by P15 (0.41%), P1 (0.29%) and P13 (0.19%). ICC analysis leads to identify features with an ICC >0.95 that could be considered stable to use in dosiomic studies when different dose cube pixel spacing are considered, especially the features in common among the seventeen plans. CONCLUSION Considering the observed variability, dosiomic studies should always provide a report not only on grid resolution and algorithm dose calculation, but also on dose cube pixel spacing.
Collapse
|
18
|
Huang C, Yang Y, Panjwani N, Boyd S, Xing L. Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface. IEEE Trans Biomed Eng 2021; 68:2907-2917. [PMID: 33523802 PMCID: PMC8526351 DOI: 10.1109/tbme.2021.3055822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Radiation therapy treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) Pareto optimal and 2) clinically acceptable. Here, we propose the Pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the Pareto front. METHODS Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the Pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we construct a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM). RESULTS On a dataset of 21 prostate cases collected as part of clinical workflow, our proposed POPS algorithm produces Pareto optimal plans that are clinically acceptable in regards to dose conformity, dose homogeneity, and sparing of organs-at-risk. CONCLUSION Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners. SIGNIFICANCE Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow.
Collapse
|
19
|
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.
Collapse
|
20
|
Lempart M, Benedek H, Jamtheim Gustafsson C, Nilsson M, Eliasson N, Bäck S, Munck af Rosenschöld P, Olsson LE. Volumetric modulated arc therapy dose prediction and deliverable treatment plan generation for prostate cancer patients using a densely connected deep learning model. Phys Imaging Radiat Oncol 2021; 19:112-119. [PMID: 34401537 PMCID: PMC8353474 DOI: 10.1016/j.phro.2021.07.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/02/2021] [Accepted: 07/14/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Radiation therapy treatment planning is a manual, time-consuming task that might be accelerated using machine learning algorithms. In this study, we aimed to evaluate if a triplet-based deep learning model can predict volumetric modulated arc therapy (VMAT) dose distributions for prostate cancer patients. MATERIALS AND METHODS A modified U-Net was trained on triplets, a combination of three consecutive image slices and corresponding segmentations, from 160 patients, and compared to a baseline U-Net. Dose predictions from 17 test patients were transformed into deliverable treatment plans using a novel planning workflow. RESULTS The model achieved a mean absolute dose error of 1.3%, 1.9%, 1.0% and ≤ 2.6% for clinical target volume (CTV) CTV_D100%, planning target volume (PTV) PTV_D98%, PTV_D95% and organs at risk (OAR) respectively, when compared to the clinical ground truth (GT) dose distributions. All predicted distributions were successfully transformed into deliverable treatment plans and tested on a phantom, resulting in a passing rate of 100% (global gamma, 3%, 2 mm, 15% cutoff). The dose difference between deliverable treatment plans and GT dose distributions was within 4.4%. The difference between the baseline model and our improved model was statistically significant (p < 0.05) for CVT_D100%, PTV_D98% and PTV_D95%. CONCLUSION Triplet-based training improved VMAT dose distribution predictions when compared to 2D. Dose predictions were successfully transformed into deliverable treatment plans using our proposed treatment planning procedure. Our method may automate parts of the workflow for external beam prostate radiation therapy and improve the overall treatment speed and plan quality.
Collapse
Affiliation(s)
- Michael Lempart
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Hunor Benedek
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Christian Jamtheim Gustafsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| | - Mikael Nilsson
- Centre for Mathematical Sciences, Lund University, Lund, Sweden
| | - Niklas Eliasson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Sven Bäck
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
| | - Per Munck af Rosenschöld
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Lars E. Olsson
- Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden
- Department of Translational Sciences, Medical Radiation Physics, Lund University, Malmö, Sweden
| |
Collapse
|
21
|
Fan J, Xing L, Yang Y. Independent verification of brachytherapy treatment plan by using deep learning inference modeling. Phys Med Biol 2021; 66. [PMID: 34132651 DOI: 10.1088/1361-6560/ac067f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 05/28/2021] [Indexed: 11/12/2022]
Abstract
This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodose distribution. In our modeling, each dwell position is represented by a Gaussian heatmap located in the vicinity of the dwell positions. A deep inception network based architecture was established to learn the mapping between CT, dose distribution and the heatmap volume. The dwell position coordinates were obtained from the predicted heatmap volume by finding the location of the Gaussian peak using non-maximum suppression. An encoder network was employed to predict dwell time by using the same input. 110 HDR brachytherapy cervical patients were used to train the proposed network. Additional 10 patients were employed to evaluate the accuracy of the proposed method through comparing the dwell position coordinates and dwell times with the results from a treatment planning system. The proposed deep learning-based dwell positions and times verification method achieved excellent predictive performance. For the tested patients, the deviation of the deep learning predicted dwell position coordinates was around one pixel from the planned positions (on average, a pixel is ∼0.5 mm), and the relative deviations of the predicted dwell times were within 2%. A deep learning-based plan check and verification method was established for brachytherapy. Our study showed that the model is capable of predicting the dwell positions and times reliably and promises to provide an efficient and accurate tool for independent verification of HDR brachytherapy treatment plan.
Collapse
Affiliation(s)
- Jiawei Fan
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China.,Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, People's Republic of China.,Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| |
Collapse
|
22
|
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
|
23
|
DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects. BIOMED RESEARCH INTERNATIONAL 2021; 2021:2043830. [PMID: 33532489 PMCID: PMC7837766 DOI: 10.1155/2021/2043830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/28/2020] [Accepted: 01/04/2021] [Indexed: 12/01/2022]
Abstract
Purpose A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information generated by nonmodulated beams of different orientations, the GRU-RNN model was capable of accurate DVH prediction for nasopharyngeal carcinoma (NPC) treatment planning. On the basis of our previous work, we proposed an improved approach and aimed to further improve the DVH prediction accuracy as well as study the feasibility of applying the proposed method to relatively small-size patient data. Methods Eighty NPC volumetric modulated arc therapy (VMAT) plans with local IRB's approval in recent two years were retrospectively and randomly selected in this study. All these original plans were created using the Eclipse treatment planning system (V13.5, Varian Medical Systems, USA) with ≥95% of PGTVnx receiving the prescribed doses of 70 Gy, ≥95% of PGTVnd receiving 66 Gy, and ≥95% of PTV receiving 60 Gy. Among them, fifty plans were used to train the DVH prediction model, and the remaining were used for testing. On the basis of our previously published work, we simplified the 3-layer GRU-RNN model to a single-layer model and further trained every organ at risk (OAR) separately with an OAR-specific equivalent uniform dose- (EUD-) based loss function. Results The results of linear least squares regression obtained by the new proposed method showed the excellent agreements between the predictions and the original plans with the correlation coefficient r = 0.976 and 0.968 for EUD results and maximum dose results, respectively, and the coefficient r of our previously published method was 0.957 and 0.946, respectively. The Wilcoxon signed-rank test results between the proposed and the previous work showed that the proposed method could significantly improve the EUD prediction accuracy for the brainstem, spinal cord, and temporal lobes with a p value < 0.01. Conclusions The accuracy of DVH prediction achieved in different OARs showed the great improvements compared to the previous works, and more importantly, the effectiveness and robustness showed by the simplified GRU-RNN trained from relatively small-size DVH samples, fully demonstrated the feasibility of applying the proposed method to small-size patient data. Excellent agreements in both EUD results and maximum dose results between the predictions and original plans indicated the application prospect in a physically and biologically related (or a mixture of both) model for treatment planning.
Collapse
|
24
|
Abstract
Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy and efficiency, which may result in unsatisfactory dose accuracy or highly intensive computation time in many clinical situations. The purpose of this work is to develop a novel dose calculation algorithm based on the deep learning method for radiation therapy. In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two-dimensional (2D) fluence map was first converted into a three-dimensional (3D) volume using ray traversal algorithm. 3D U-Net like deep residual network was then established to learn a mapping between this converted 3D volume, CT and 3D dose distribution. Therefore an indirect relationship was built between a fluence map and its corresponding 3D dose distribution without using significantly complex neural networks. Two hundred patients, including nasopharyngeal, lung, rectum and breast cancer cases, were collected and applied to train the proposed network. Additional 47 patients were randomly selected to evaluate the accuracy of the proposed method through comparing dose distributions, dose volume histograms and clinical indices with the results from a treatment planning system (TPS), which was used as the ground truth in this study. The proposed deep learning based dose calculation algorithm achieved good predictive performance. For 47 tested patients, the average per-voxel bias of the deep learning calculated value and standard deviation (normalized to the prescription), relative to the TPS calculation, is 0.17%±2.28%. The average deep learning calculated values and standard deviations for relevant clinical indices were compared with the TPS calculated results and the t-test p-values demonstrated the consistency between them. In this study we developed a new deep learning based dose calculation method. This approach was evaluated by the clinical cases with different sites. Our results demonstrated its feasibility and reliability and indicated its great potential to improve the efficiency and accuracy of radiation dose calculation for different treatment modalities.
Collapse
Affiliation(s)
- Jiawei Fan
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China
- On leave from Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| | - Peng Dong
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| | - Jiazhou Wang
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China
| | - Weigang Hu
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China
| | - Yong Yang
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America
| |
Collapse
|
25
|
Incorporating historical sub-optimal deep neural networks for dose prediction in radiotherapy. Med Image Anal 2020; 67:101886. [PMID: 33166773 DOI: 10.1016/j.media.2020.101886] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/21/2020] [Accepted: 10/22/2020] [Indexed: 02/07/2023]
Abstract
As the main treatment for cancer patients, radiotherapy has achieved enormous advancement over recent decades. However, these achievements have come at the cost of increased treatment plan complexity, necessitating high levels of expertise experience and effort. The accurate prediction of dose distribution would alleviate the above issues. Deep convolutional neural networks are known to be effective models for such prediction tasks. Most studies on dose prediction have attempted to modify the network architecture to accommodate the requirement of different diseases. In this paper, we focus on the input and output of dose prediction model, rather than the network architecture. Regarding the input, the non-modulated dose distribution, which is the initial quantity in the inverse optimization of the treatment plan, is used to provide auxiliary information for the prediction task. Regarding the output, a historical sub-optimal ensemble (HSE) method is proposed, which leverages the sub-optimal models during the training phase to improve the prediction results. The proposed HSE is a general method that does not require any modification of the learning algorithm and does not incur additional computational cost during the training phase. Multiple experiments, including the dose prediction, segmentation, and classification tasks, demonstrate the effectiveness of the strategies applied to the input and output parts.
Collapse
|
26
|
Wang M, Zhang Q, Lam S, Cai J, Yang R. A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning. Front Oncol 2020; 10:580919. [PMID: 33194711 PMCID: PMC7645101 DOI: 10.3389/fonc.2020.580919] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 09/16/2020] [Indexed: 01/03/2023] Open
Abstract
Treatment planning plays an important role in the process of radiotherapy (RT). The quality of the treatment plan directly and significantly affects patient treatment outcomes. In the past decades, technological advances in computer and software have promoted the development of RT treatment planning systems with sophisticated dose calculation and optimization algorithms. Treatment planners now have greater flexibility in designing highly complex RT treatment plans in order to mitigate the damage to healthy tissues better while maximizing radiation dose to tumor targets. Nevertheless, treatment planning is still largely a time-inefficient and labor-intensive process in current clinical practice. Artificial intelligence, including machine learning (ML) and deep learning (DL), has been recently used to automate RT treatment planning and has gained enormous attention in the RT community due to its great promises in improving treatment planning quality and efficiency. In this article, we reviewed the historical advancement, strengths, and weaknesses of various DL-based automated RT treatment planning techniques. We have also discussed the challenges, issues, and potential research directions of DL-based automated RT treatment planning techniques.
Collapse
Affiliation(s)
- Mingqing Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Qilin Zhang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Saikit Lam
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ruijie Yang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| |
Collapse
|
27
|
Fan J, Xing L, Ma M, Hu W, Yang Y. Verification of the machine delivery parameters of a treatment plan via deep learning. Phys Med Biol 2020; 65:195007. [PMID: 32604082 DOI: 10.1088/1361-6560/aba165] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We developed a generative adversarial network (GAN)-based deep learning approach to estimate the multileaf collimator (MLC) aperture and corresponding monitor units (MUs) from a given 3D dose distribution. The proposed design of the adversarial network, which integrates a residual block into pix2pix framework, jointly trains a 'U-Net'-like architecture as the generator and a convolutional 'PatchGAN' classifier as the discriminator. 199 patients, including nasopharyngeal, lung and rectum, treated with intensity-modulated radiotherapy and volumetric-modulated arc therapy techniques were utilized to train the network. An additional 47 patients were used to test the prediction accuracy of the proposed deep learning model. The Dice similarity coefficient (DSC) was calculated to evaluate the similarity between the MLC aperture shapes obtained from the treatment planning system (TPS) and the deep learning prediction. The average and standard deviation of the bias between the TPS-generated MUs and predicted MUs was calculated to evaluate the MU prediction accuracy. In addition, the differences between TPS and deep learning-predicted MLC leaf positions were compared. The average and standard deviation of DSC was 0.94 ± 0.043 for 47 testing patients. The average deviation of predicted MUs from the planned MUs normalized to each beam or arc was within 2% for all the testing patients. The average deviation of the predicted MLC leaf positions was around one pixel for all the testing patients. Our results demonstrated the feasibility and reliability of the proposed approach. The proposed technique has strong potential to improve the efficiency and accuracy of the patient plan quality assurance process.
Collapse
Affiliation(s)
- Jiawei Fan
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Drive, Stanford, CA 94305-5847, United States of America. Department of Radiation Oncology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College Fudan University, Shanghai 200032, People's Republic of China
| | | | | | | | | |
Collapse
|
28
|
Cao W, Zhuang Y, Chen L, Liu X. Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning. Radiat Oncol 2020; 15:216. [PMID: 32933543 PMCID: PMC7653901 DOI: 10.1186/s13014-020-01623-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 07/17/2020] [Indexed: 11/29/2022] Open
Abstract
Purpose In this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning. Methods and materials One hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: > 98% of PGTVnx received the prescribed doses of 70 Gy, > 98% of PGTVnd received the prescribed doses of 66 Gy and > 98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs). Results For the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values < 0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by − 3.44 Gy, − 1.94 Gy, − 1.88 Gy, 0.44 Gy, 1.98 Gy, − 1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1 cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average. Conclusion The GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.
Collapse
Affiliation(s)
- Wufei Cao
- School of Physics, Sun Yat-sen University, Guangzhou, 510275, China.,State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China
| | - Yongdong Zhuang
- National Cancer Center, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Lixin Chen
- State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
| | - Xiaowei Liu
- School of Physics, Sun Yat-sen University, Guangzhou, 510275, China.
| |
Collapse
|
29
|
Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med 2020; 14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 02/14/2020] [Indexed: 12/19/2022]
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
Collapse
Affiliation(s)
- Ke Sheng
- Department of Radiation Oncology, University of California, Los Angeles, CA, 90095, USA.
| |
Collapse
|
30
|
Cozzi L. Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy. Phys Imaging Radiat Oncol 2019; 11:69-70. [PMID: 33458281 PMCID: PMC7807646 DOI: 10.1016/j.phro.2019.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Luca Cozzi
- Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
| |
Collapse
|