1
|
Covington EL, Suresh K, Anderson BM, Barker M, Dess K, Price JG, Moncion A, Vaccarelli MJ, Santanam L, Xiao Y, Mayo C. Perceptions on and roadblocks to implementation of standardized nomenclature in radiation oncology: A survey from TG-263U1. J Appl Clin Med Phys 2024; 25:e14359. [PMID: 38689502 PMCID: PMC11163509 DOI: 10.1002/acm2.14359] [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: 12/04/2023] [Revised: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 05/02/2024] Open
Abstract
PURPOSE AAPM Task Group No. 263U1 (Update to Report No. 263 - Standardizing Nomenclatures in Radiation Oncology) disseminated a survey to receive feedback on utilization, gaps, and means to facilitate further adoption. METHODS The survey was created by TG-263U1 members to solicit feedback from physicists, dosimetrists, and physicians working in radiation oncology. Questions on the adoption of the TG-263 standard were coupled with demographic information, such as clinical role, place of primary employment (e.g., private hospital, academic center), and size of institution. The survey was emailed to all AAPM, AAMD, and ASTRO members. RESULTS The survey received 463 responses with 310 completed survey responses used for analysis, of whom most had the clinical role of medical physicist (73%) and the majority were from the United States (83%). There were 83% of respondents who indicated that they believe that having a nomenclature standard is important or very important and 61% had adopted all or portions of TG-263 in their clinics. For those yet to adopt TG-263, the staffing and implementation efforts were the main cause for delaying adoption. Fewer respondents had trouble adopting TG-263 for organs at risk (29%) versus target (44%) nomenclature. Common themes in written feedback were lack of physician support and available resources, especially in vendor systems, to facilitate adoption. CONCLUSIONS While there is strong support and belief in the benefit of standardized nomenclature, the widespread adoption of TG-263 has been hindered by the effort needed by staff for implementation. Feedback from the survey is being utilized to drive the focus of the update efforts and create tools to facilitate easier adoption of TG-263.
Collapse
Affiliation(s)
| | - Krithika Suresh
- Department of Radiation OncologyMichigan MedicineAnn ArborMichiganUSA
| | - Brian M. Anderson
- Department of Radiation OncologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | | | - Kathryn Dess
- Department of Radiation OncologyMichigan MedicineAnn ArborMichiganUSA
| | - Jeremy G. Price
- Department of Radiation OncologyFox Chase Cancer CenterPhiladelphiaPennsylvaniaUSA
| | - Alexander Moncion
- Department of Radiation OncologyMichigan MedicineAnn ArborMichiganUSA
| | | | - Lakshmi Santanam
- Medical Physics DepartmentMemorial Sloan‐Kettering Cancer CenterNew YorkNew YorkUSA
| | - Ying Xiao
- Department of Radiation OncologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Charles Mayo
- Department of Radiation OncologyMichigan MedicineAnn ArborMichiganUSA
| |
Collapse
|
2
|
Liu C, Liu Z, Holmes J, Zhang L, Zhang L, Ding Y, Shu P, Wu Z, Dai H, Li Y, Shen D, Liu N, Li Q, Li X, Zhu D, Liu T, Liu W. Artificial general intelligence for radiation oncology. META-RADIOLOGY 2023; 1:100045. [PMID: 38344271 PMCID: PMC10857824 DOI: 10.1016/j.metrad.2023.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.
Collapse
Affiliation(s)
- Chenbin Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | | | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | - Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Peng Shu
- School of Computing, University of Georgia, USA
| | - Zihao Wu
- School of Computing, University of Georgia, USA
| | - Haixing Dai
- School of Computing, University of Georgia, USA
| | - Yiwei Li
- School of Computing, University of Georgia, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, China
- Shanghai United Imaging Intelligence Co., Ltd, China
- Shanghai Clinical Research and Trial Center, China
| | - Ninghao Liu
- School of Computing, University of Georgia, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | | | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, USA
| |
Collapse
|
3
|
Refsgaard L, Skarsø ER, Ravkilde T, Nissen HD, Olsen M, Boye K, Laursen KL, Bekke SN, Lorenzen EL, Brink C, Thorsen LBJ, Offersen BV, Korreman SS. End-to-end framework for automated collection of large multicentre radiotherapy datasets demonstrated in a Danish Breast Cancer Group cohort. Phys Imaging Radiat Oncol 2023; 27:100485. [PMID: 37705727 PMCID: PMC10495662 DOI: 10.1016/j.phro.2023.100485] [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: 04/28/2023] [Revised: 08/18/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Abstract
Large Digital Imaging and Communications in Medicine (DICOM) datasets are key to support research and the development of machine learning technology in radiotherapy (RT). However, the tools for multi-centre data collection, curation and standardisation are not readily available. Automated batch DICOM export solutions were demonstrated for a multicentre setup. A Python solution, Collaborative DICOM analysis for RT (CORDIAL-RT) was developed for curation, standardisation, and analysis of the collected data. The setup was demonstrated in the DBCG RT-Nation study, where 86% (n = 7748) of treatments in the inclusion period were collected and quality assured, supporting the applicability of the end-to-end framework.
Collapse
Affiliation(s)
- Lasse Refsgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Emma Riis Skarsø
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Thomas Ravkilde
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Henrik Dahl Nissen
- Department of Oncology, Vejle Hospital, University Hospital of Southern Denmark, Denmark
| | - Mikael Olsen
- Department of Oncology, Zealand University Hospital, Department of Clinical Oncology and Palliative Care, Næstved, Denmark
| | - Kristian Boye
- Department of Oncology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Kasper Lind Laursen
- Department of Medical Physics, Aalborg University Hospital, Aalborg, Denmark
| | - Susanne Nørring Bekke
- Department of Oncology, Copenhagen University Hospital – Herlev and Gentofte, Copenhagen, Denmark
| | - Ebbe Laugaard Lorenzen
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Carsten Brink
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark
| | - Lise Bech Jellesmark Thorsen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Birgitte Vrou Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Denmark
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Stine Sofia Korreman
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| |
Collapse
|