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Panettieri V, Gagliardi G. Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready. J Med Radiat Sci 2024; 71:174-176. [PMID: 38641984 PMCID: PMC11177026 DOI: 10.1002/jmrs.791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/04/2024] [Indexed: 04/21/2024] Open
Abstract
The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.
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Affiliation(s)
- Vanessa Panettieri
- Department of Physical SciencesPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of OncologyThe University of MelbourneMelbourneVictoriaAustralia
- Central Clinical SchoolMonash UniversityMelbourneVictoriaAustralia
- Department of Medical Imaging and Radiation SciencesMonash UniversityClaytonVictoriaAustralia
| | - Giovanna Gagliardi
- Medical Radiation Physics DepartmentKarolinska University HospitalStockholmSweden
- Department of Oncology‐PathologyKarolinska InstitutetStockholmSweden
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Jones S, Thompson K, Porter B, Shepherd M, Sapkaroski D, Grimshaw A, Hargrave C. Automation and artificial intelligence in radiation therapy treatment planning. J Med Radiat Sci 2024; 71:290-298. [PMID: 37794690 PMCID: PMC11177028 DOI: 10.1002/jmrs.729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/12/2023] [Indexed: 10/06/2023] Open
Abstract
Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI-based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher-quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety.
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Affiliation(s)
- Scott Jones
- Radiation Oncology Princess Alexandra Hospital Raymond TerraceBrisbaneQueenslandAustralia
| | - Kenton Thompson
- Department of Radiation Therapy ServicesPeter MacCullum Cancer Care CentreMelbourneVictoriaAustralia
| | - Brian Porter
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
| | - Meegan Shepherd
- Northern Sydney Cancer CentreRoyal North Shore HospitalSydneyNew South WalesAustralia
- Monash UniversityClaytonVictoriaAustralia
| | - Daniel Sapkaroski
- Department of Radiation Therapy ServicesPeter MacCullum Cancer Care CentreMelbourneVictoriaAustralia
- RMIT UniversityMelbourneVictoriaAustralia
| | | | - Catriona Hargrave
- Radiation Oncology Princess Alexandra Hospital Raymond TerraceBrisbaneQueenslandAustralia
- Queensland University of Technology, Faculty of Health, School of Clinical SciencesBrisbaneQueenslandAustralia
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Castriconi R, Tudda A, Placidi L, Benecchi G, Cagni E, Dusi F, Ianiro A, Landoni V, Malatesta T, Mazzilli A, Meffe G, Oliviero C, Rambaldi Guidasci G, Scaggion A, Trojani V, Del Vecchio A, Fiorino C. Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation. Phys Med 2024; 120:103331. [PMID: 38484461 DOI: 10.1016/j.ejmp.2024.103331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/05/2024] [Accepted: 03/08/2024] [Indexed: 04/19/2024] Open
Abstract
PURPOSE Within a multi-institutional project, we aimed to assess the transferability of knowledge-based (KB) plan prediction models in the case of whole breast irradiation (WBI) for left-side breast irradiation with tangential fields (TF). METHODS Eight institutions set KB models, following previously shared common criteria. Plan prediction performance was tested on 16 new patients (2 pts per centre) extracting dose-volume-histogram (DVH) prediction bands of heart, ipsilateral lung, contralateral lung and breast. The inter-institutional variability was quantified by the standard deviations (SDint) of predicted DVHs and mean-dose (Dmean). The transferability of models, for the heart and the ipsilateral lung, was evaluated by the range of geometric Principal Component (PC1) applicability of a model to test patients of the other 7 institutions. RESULTS SDint of the DVH was 1.8 % and 1.6 % for the ipsilateral lung and the heart, respectively (20 %-80 % dose range); concerning Dmean, SDint was 0.9 Gy and 0.6 Gy for the ipsilateral lung and the heart, respectively (<0.2 Gy for contralateral organs). Mean predicted doses ranged between 4.3 and 5.9 Gy for the ipsilateral lung and 1.1-2.3 Gy for the heart. PC1 analysis suggested no relevant differences among models, except for one centre showing a systematic larger sparing of the heart, concomitant to a worse PTV coverage, due to high priority in sparing the left anterior descending coronary artery. CONCLUSIONS Results showed high transferability among models and low inter-institutional variability of 2% for plan prediction. These findings encourage the building of benchmark models in the case of TF-WBI.
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Affiliation(s)
- Roberta Castriconi
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy.
| | - Alessia Tudda
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy; Università Statale di Milano, Milano, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giovanna Benecchi
- Medical Physics Dept, University Hospital of Parma AOUP, Parma, Italy
| | - Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Francesca Dusi
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Anna Ianiro
- IRCCS Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | - Valeria Landoni
- IRCCS Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | - Tiziana Malatesta
- UOC di Radioterapia Oncologica, Fatebenefratelli Isola Tiberina - Gemelli Isola, Roma, Italy
| | - Aldo Mazzilli
- Medical Physics Dept, University Hospital of Parma AOUP, Parma, Italy
| | - Guenda Meffe
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | | | | | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Valeria Trojani
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Claudio Fiorino
- Medical Physics Dept, IRCCS San Raffaele Scientific Institute, Milano, Italy
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Mallio CA, Bernetti C, Sertorio AC, Zobel BB. ChatGPT in radiology structured reporting: analysis of ChatGPT-3.5 Turbo and GPT-4 in reducing word count and recalling findings. Quant Imaging Med Surg 2024; 14:2096-2102. [PMID: 38415145 PMCID: PMC10895108 DOI: 10.21037/qims-23-1300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/20/2023] [Indexed: 02/29/2024]
Affiliation(s)
- Carlo A Mallio
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Caterina Bernetti
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Andrea C Sertorio
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Bruno Beomonte Zobel
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
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Mukherjee S, Vagha S, Gadkari P. Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer. Cureus 2024; 16:e54467. [PMID: 38510911 PMCID: PMC10953838 DOI: 10.7759/cureus.54467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024] Open
Abstract
This comprehensive review explores the transformative role of artificial intelligence (AI) in the realm of gastrointestinal cancer. Gastrointestinal cancers present unique challenges, necessitating precise diagnostic tools and personalized treatment strategies. Leveraging AI, particularly machine learning and deep learning algorithms, has demonstrated remarkable potential in revolutionizing early detection, treatment planning, prognosis, and drug development. The analysis of current research and technological advancements underscores the capacity of AI to unravel intricate patterns within extensive datasets, providing actionable insights that enhance diagnostic accuracy and treatment efficacy. The transformative impact of AI on the landscape of gastrointestinal cancer is emphasized, signaling a paradigm shift towards more precise and targeted cancer care. The conclusion emphasizes the need for sustained research efforts and collaborative initiatives among AI researchers, healthcare professionals, and policymakers. By fostering interdisciplinary collaboration, we can navigate the evolving field of gastrointestinal cancer care, embracing the potential of AI to improve patient outcomes and contribute to a more effective and personalized approach to cancer management.
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Affiliation(s)
- Sreetama Mukherjee
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sunita Vagha
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pravin Gadkari
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Li X, Ge Y, Wu Q, Wang C, Sheng Y, Wang W, Stephens H, Yin FF, Wu QJ. Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy. Phys Med Biol 2022; 67:215009. [PMID: 36206747 DOI: 10.1088/1361-6560/ac9882] [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: 06/14/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
Objective. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve the rigor of input feature design in a DL-FMP model by examining how different designs of input features influence model prediction performance.Approach. This study included 231 head-and-neck intensity-modulated radiation therapy patients. Three input feature designs were investigated. The first design (D1) assumed that information of all critical structures from all beam angles should be combined to predict fluence maps. The second design (D2) assumed that local anatomical information was sufficient for predicting radiation intensity of a beamlet at a respective beam angle. The third design (D3) assumed the need for both local anatomical information and inter-beam modulation to predict radiation intensity values of the beamlets that intersect at a voxel. For each input design, we tailored the DL model accordingly. All models were trained using the same set of ground truth plans (GT plans). The plans generated by DL models (DL plans) were analyzed using key dose-volume metrics. One-way ANOVA with multiple comparisons correction (Bonferroni method) was performed (significance level = 0.05).Main results. For PTV-related metrics, all DL plans had significantly higher maximum dose (p < 0.001), conformity index (p < 0.001), and heterogeneity index (p < 0.001) compared to GT plans, with D2 being the worst performer. Meanwhile, except for cord+5 mm (p < 0.001), DL plans of all designs resulted in OAR dose metrics that are comparable to those of GT plans.Significance. Local anatomical information contains most of the information that DL models need to predict fluence maps for clinically acceptable OAR sparing. Input features from beam angles are needed to achieve the best PTV coverage. These results provide valuable insights for further improvement of DL-FMP models and DL models in general.
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Affiliation(s)
- Xinyi Li
- Duke University Medical Center, United States of America
| | - Yaorong Ge
- University of North Carolina at Charlotte, United States of America
| | - Qiuwen Wu
- Duke University Medical Center, United States of America
| | - Chunhao Wang
- Duke University Medical Center, United States of America
| | - Yang Sheng
- Duke University Medical Center, United States of America
| | - Wentao Wang
- Duke University Medical Center, United States of America
| | | | - Fang-Fang Yin
- Duke University Medical Center, United States of America
| | - Q Jackie Wu
- Duke University Medical Center, United States of America
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Tudda A, Castriconi R, Benecchi G, Cagni E, Cicchetti A, Dusi F, Esposito PG, Guernieri M, Ianiro A, Landoni V, Mazzilli A, Moretti E, Oliviero C, Placidi L, Rambaldi Guidasci G, Rancati T, Scaggion A, Trojani V, Fiorino C. Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. Radiother Oncol 2022; 175:10-16. [PMID: 35868603 DOI: 10.1016/j.radonc.2022.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/07/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI). MATERIALS AND METHODS Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SDint of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model's geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes. RESULTS The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R2 = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible. CONCLUSIONS Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.
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Affiliation(s)
- Alessia Tudda
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy; Università Statale di Milano, Milano, Italy
| | | | | | - Elisabetta Cagni
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Francesca Dusi
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | | | - Marika Guernieri
- Department of Medical Physics, University Hospital, Udine, Italy
| | - Anna Ianiro
- Istituto Nazionale dei Tumori Regina Elena, Rome, Italy
| | | | - Aldo Mazzilli
- Medical Physics Dept, University Hospital of Parma AOUP, Italy
| | - Eugenia Moretti
- Department of Medical Physics, University Hospital, Udine, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Giulia Rambaldi Guidasci
- Amethyst Radioterapia Italia, Medical Physics Department, San Giovanni Calibita Fatebenefratelli Hospital, Rome, Italy
| | - Tiziana Rancati
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandro Scaggion
- Medical Physics Department, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
| | - Valeria Trojani
- Medical Physics Unit, Department of Advanced Technology, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Claudio Fiorino
- Medical Physics Dept, San Raffaele Scientific Institute, Milano, Italy
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Jiang S, Xue Y, Li M, Yang C, Zhang D, Wang Q, Wang J, Chen J, You J, Yuan Z, Wang X, Zhang X, Wang W. Artificial Intelligence-Based Automated Treatment Planning of Postmastectomy Volumetric Modulated Arc Radiotherapy. Front Oncol 2022; 12:871871. [PMID: 35547874 PMCID: PMC9084926 DOI: 10.3389/fonc.2022.871871] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 03/22/2022] [Indexed: 12/24/2022] Open
Abstract
As a useful tool, artificial intelligence has surpassed human beings in many fields. Artificial intelligence-based automated radiotherapy planning strategies have been proposed in lots of cancer sites and are the future of treatment planning. Postmastectomy radiotherapy (PMRT) decreases local recurrence probability and improves overall survival, and volumetric modulated arc therapy (VMAT) has gradually become the mainstream technique of radiotherapy. However, there are few customized effective automated treatment planning schemes for postmastectomy VMAT so far. This study investigated an artificial intelligence based automated planning using the MD Anderson Cancer Center AutoPlan (MDAP) system and Pinnacle treatment planning system (TPS), to effectively generate high-quality postmastectomy VMAT plans. In this study, 20 patients treated with PMRT were retrospectively investigated, including 10 left- and 10 right-sided postmastectomy patients. Chest wall and the supraclavicular, subclavicular, and internal mammary regions were delineated as target volume by radiation oncologists, and 50 Gy in 25 fractions was prescribed. Organs at risk including heart, spinal cord, left lung, right lung, and lungs were also contoured. All patients were planned with VMAT using 2 arcs. An optimization objective template was summarized based on the dose of clinical plans and requirements from oncologists. Several treatment planning parameters were investigated using an artificial intelligence algorithm, including collimation angle, jaw collimator mode, gantry spacing resolution (GSR), and number of start optimization times. The treatment planning parameters with the best performance or that were most preferred were applied to the automated treatment planning method. Dosimetric indexes of automated treatment plans (autoplans) and manual clinical plans were compared by the paired t-test. The jaw tracking mode, 2-degree GSR, and 3 rounds of optimization were selected in all the PMRT autoplans. Additionally, the 350- and 10-degree collimation angles were selected in the left- and right-sided PMRT autoplans, respectively. The uniformity index and conformity index of the planning target volume, mean heart dose, spinal cord D0.03cc, mean lung dose, and V5Gy and V20Gy of the lung of autoplans were significantly better compared with the manual clinical plans. An artificial intelligence-based automated treatment planning method for postmastectomy VMAT has been developed to ensure plan quality and improve clinical efficiency.
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Affiliation(s)
- Shengpeng Jiang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Yi Xue
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Ming Li
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Chengwen Yang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Daguang Zhang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Qingxin Wang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Jing Wang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Jie Chen
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Jinqiang You
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Zhiyong Yuan
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
| | - Xiaochun Wang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Xiaodong Zhang
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Wei Wang
- Department of Radiation Ocology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
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