دورية أكاديمية
OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.
| العنوان: | OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines. |
|---|---|
| المؤلفون: | Babier A; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada., Mahmood R; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada., Zhang B; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada., Alves VGL; Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America., Barragán-Montero AM; Department of Molecular Imaging Radiation Oncology, UCLouvain, Louvain-la-Neuve, Belgium., Beaudry J; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America., Cardenas CE; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL, United States of America., Chang Y; Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, People's Republic of China., Chen Z; Shenying Medical Technology Co., Ltd., Shenzhen, Guangdong, People's Republic of China., Chun J; Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, Republic of Korea., Diaz K; Department of Physics, National University of Colombia, Medellín, Colombia., David Eraso H; Department of Physics, National University of Colombia, Medellín, Colombia., Faustmann E; Atominstitut, Vienna University of Technology, Vienna, Austria., Gaj S; Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States of America., Gay S; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Gronberg M; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Guo B; Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, United States of America., He J; Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China., Heilemann G; Department of Radiation Oncology, Medical University of Vienna, Vienna, Austria., Hira S; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America., Huang Y; Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, People's Republic of China., Ji F; Department of Electrical Engineering and Automation, Anhui University, Hefei, People's Republic of China., Jiang D; Department of Electrical Engineering and Automation, Anhui University, Hefei, People's Republic of China., Carlo Jimenez Giraldo J; Department of Physics, National University of Colombia, Medellín, Colombia., Lee H; Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, United States of America., Lian J; Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America., Liu S; Department of Electrical Engineering and Automation, Anhui University, Hefei, People's Republic of China., Liu KC; Department of Medical Imaging, Taiwan AI Labs, Taipei, Taiwan., Marrugo J; Department of Physics, National University of Colombia, Medellín, Colombia., Miki K; Department Of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan., Nakamura K; Department of Biomedical Engineering, Cleveland Clinic, Cleveland, OH, United States of America., Netherton T; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., Nguyen D; Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, United States of America., Nourzadeh H; Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, United States of America., Osman AFI; Department of Medical Physics, Al-Neelain University, Khartoum, Sudan., Peng Z; Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, People's Republic of China., Darío Quinto Muñoz J; Department of Physics, National University of Colombia, Medellín, Colombia., Ramsl C; Atominstitut, Vienna University of Technology, Vienna, Austria., Joo Rhee D; Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America., David Rodriguez J; Department of Physics, National University of Colombia, Medellín, Colombia., Shan H; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, People's Republic of China., Siebers JV; Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America., Soomro MH; Department of Radiation Oncology, University of Virginia Health System, Charlottesville, VA, United States of America., Sun K; Studio Vodels, Atlanta, GA, United States of America., Usuga Hoyos A; Department of Physics, National University of Colombia, Medellín, Colombia., Valderrama C; Department of Physics, National University of Colombia, Medellín, Colombia., Verbeek R; Department Computer Science, Aalto University, Espoo, Finland., Wang E; Shenying Medical Technology Co., Ltd., Shenzhen, Guangdong, People's Republic of China., Willems S; Department of Electrical Engineering, KULeuven, Leuven, Belgium., Wu Q; Department of Electrical Engineering and Automation, Anhui University, Hefei, People's Republic of China., Xu X; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States of America., Yang S; Tencent AI Lab, Shenzhen, Guangdong, People's Republic of China., Yuan L; Department of Radiation Oncology, Virginia Commonwealth University Medical Center, Richmond, VA, United States of America., Zhu S; Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States of America., Zimmermann L; Faculty of Health, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria.; Competence Center for Preclinical Imaging and Biomedical Engineering, University of Applied Sciences Wiener Neustadt, Wiener Neustadt, Austria., Moore KL; Department of Radiation Oncology, University of California, San Diego, La Jolla, CA, United States of America., Purdie TG; Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, ON, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.; Techna Institute for the Advancement of Technology for Health, Toronto, ON, Canada.; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada., McNiven AL; Radiation Medicine Program, UHN Princess Margaret Cancer Centre, Toronto, ON, Canada.; Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada., Chan TCY; Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.; Vector Institute, Toronto, ON, Canada.; Techna Institute for the Advancement of Technology for Health, Toronto, ON, Canada. |
| المصدر: | Physics in medicine and biology [Phys Med Biol] 2022 Sep 12; Vol. 67 (18). Date of Electronic Publication: 2022 Sep 12. |
| نوع المنشور: | Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: | English |
| بيانات الدورية: | Publisher: IOP Publishing Country of Publication: England NLM ID: 0401220 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6560 (Electronic) Linking ISSN: 00319155 NLM ISO Abbreviation: Phys Med Biol Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bristol : IOP Publishing |
| مواضيع طبية MeSH: | Radiotherapy Planning, Computer-Assisted*/methods , Radiotherapy, Intensity-Modulated*/methods, Humans ; Knowledge Bases ; Radiotherapy Dosage ; Reproducibility of Results |
| مستخلص: | Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better ( P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available. (Creative Commons Attribution license.) |
| References: | Int J Radiat Oncol Biol Phys. 2007 Apr 1;67(5):1596-605. (PMID: 17394954) Med Phys. 2021 Sep;48(9):4730-4742. (PMID: 34265105) Int J Radiat Oncol Biol Phys. 2020 Feb 1;106(2):430-439. (PMID: 31678227) Med Phys. 2019 Jan;46(1):370-381. (PMID: 30383300) Front Oncol. 2020 Oct 23;10:580919. (PMID: 33194711) Med Phys. 2003 May;30(5):979-85. (PMID: 12773007) J Appl Clin Med Phys. 2021 Aug;22(8):16-44. (PMID: 34231970) Phys Med Biol. 2022 Feb 04;67(4):. (PMID: 35061602) Med Phys. 2020 Sep;47(9):3898-3912. (PMID: 32621789) Gigascience. 2014 Dec 12;3(1):37. (PMID: 25678961) Phys Med Biol. 2019 Mar 18;64(6):065020. (PMID: 30703760) Phys Med Biol. 2018 May 10;63(10):105004. (PMID: 29633957) Semin Radiat Oncol. 2019 Jul;29(3):209-218. (PMID: 31027638) Med Phys. 2022 Jun;49(6):3564-3573. (PMID: 35305023) Med Phys. 2021 Sep;48(9):5567-5573. (PMID: 34157138) PeerJ. 2019 Oct 4;7:e7702. (PMID: 31592346) Phys Med Biol. 2018 Dec 04;63(23):235022. (PMID: 30511663) Phys Med Biol. 2017 Jul 06;62(15):5926-5944. (PMID: 28486217) Med Phys. 2016 Mar;43(3):1212-21. (PMID: 26936706) Data Brief. 2017 Apr 01;12:143-149. (PMID: 28417100) Med Phys. 2021 Sep;48(9):5562-5566. (PMID: 34156727) Med Phys. 2021 Sep;48(9):5574-5582. (PMID: 34101852) Radiat Oncol. 2020 Aug 3;15(1):188. (PMID: 32746873) Phys Med Biol. 2021 Feb 24;66(5):054002. (PMID: 33503599) Nat Methods. 2021 Oct;18(10):1132-1135. (PMID: 34462593) Int J Radiat Oncol Biol Phys. 2019 Jan 1;103(1):251-258. (PMID: 30145392) Med Phys. 2021 Sep;48(9):5549-5561. (PMID: 34156719) Nat Med. 2021 Jun;27(6):999-1005. (PMID: 34083812) Int J Radiat Oncol Biol Phys. 2021 Nov 1;111(3):705-715. (PMID: 34217788) Br J Radiol. 2018 Dec;91(1092):20180270. (PMID: 30074813) Phys Imaging Radiat Oncol. 2021 Jul 13;19:60-65. (PMID: 34307920) Phys Med. 2020 Apr;72:73-79. (PMID: 32222642) Med Phys. 2019 Jun;46(6):2760-2775. (PMID: 30963580) |
| معلومات مُعتمدة: | P30 CA008748 United States CA NCI NIH HHS; R01 CA222216 United States CA NCI NIH HHS |
| فهرسة مساهمة: | Keywords: automated planning; inverse optimization; inverse problem; knowledge-based planning; open data; optimization; radiotherapy |
| تواريخ الأحداث: | Date Created: 20220912 Date Completed: 20220913 Latest Revision: 20231229 |
| رمز التحديث: | 20231229 |
| مُعرف محوري في PubMed: | PMC10696540 |
| DOI: | 10.1088/1361-6560/ac8044 |
| PMID: | 36093921 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1361-6560 |
|---|---|
| DOI: | 10.1088/1361-6560/ac8044 |