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] [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
|
Mayo CS, Feng MU, Brock KK, Kudner R, Balter P, Buchsbaum JC, Caissie A, Covington E, Daugherty EC, Dekker AL, Fuller CD, Hallstrom AL, Hong DS, Hong JC, Kamran SC, Katsoulakis E, Kildea J, Krauze AV, Kruse JJ, McNutt T, Mierzwa M, Moreno A, Palta JR, Popple R, Purdie TG, Richardson S, Sharp GC, Satomi S, Tarbox LR, Venkatesan AM, Witztum A, Woods KE, Yao Y, Farahani K, Aneja S, Gabriel PE, Hadjiiski L, Ruan D, Siewerdsen JH, Bratt S, Casagni M, Chen S, Christodouleas JC, DiDonato A, Hayman J, Kapoor R, Kravitz S, Sebastian S, Von Siebenthal M, Bosch W, Hurkmans C, Yom SS, Xiao Y. Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer. Int J Radiat Oncol Biol Phys 2023; 117:533-550. [PMID: 37244628 PMCID: PMC10741247 DOI: 10.1016/j.ijrobp.2023.05.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/29/2023]
Abstract
PURPOSE The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Dan Ruan
- University of California, Los Angeles
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Sue S Yom
- University of California, San Francisco
| | | |
Collapse
|
3
|
Kapoor R, Sleeman WC, Ghosh P, Palta J. Infrastructure tools to support an effective Radiation Oncology Learning Health System. J Appl Clin Med Phys 2023; 24:e14127. [PMID: 37624227 PMCID: PMC10562037 DOI: 10.1002/acm2.14127] [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: 05/18/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 08/26/2023] Open
Abstract
PURPOSE Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. METHODS We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. RESULTS The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. CONCLUSIONS The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.
Collapse
Affiliation(s)
- Rishabh Kapoor
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - William C Sleeman
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Preetam Ghosh
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Jatinder Palta
- Department of Radiation OncologyVirginia Commonwealth UniversityRichmondVirginiaUSA
| |
Collapse
|
4
|
Puckett LL, Titi M, Kujundzic K, Dawes SL, Gore EM, Katsoulakis E, Park JH, Solanki AA, Kapoor R, Kelly M, Palta J, Chetty IJ, Jabbour SK, Liao Z, Movsas B, Thomas CR, Timmerman RD, Werner-Wasik M, Kudner R, Wilson E, Simone CB. Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel. Pract Radiat Oncol 2023; 13:413-428. [PMID: 37075838 DOI: 10.1016/j.prro.2023.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/21/2023]
Abstract
PURPOSE For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.
Collapse
Affiliation(s)
- Lindsay L Puckett
- Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin.
| | - Mohammad Titi
- Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin
| | | | | | - Elizabeth M Gore
- Department of Radiation Oncology, Medical College of Wisconsin and Clement J. Zablocki VA Medical Center, Milwaukee, Wisconsin
| | - Evangelia Katsoulakis
- Department of Radiation Oncology, James A. Haley Veterans Affairs Healthcare System, Tampa, Florida
| | - John H Park
- Department of Radiation Oncology, Kansas City VA Medical Center, Kansas City, Missouri; Department of Radiology, University of Missouri Kansas City School of Medicine, Kansas City, Missouri
| | - Abhishek A Solanki
- Department of Radiation Oncology, Loyola University and Hines VA Medical Center, Chicago, Illinois
| | - Rishabh Kapoor
- Department of Radiation Oncology, Virginia Commonwealth University and Hunter Holmes McGuire VA Medical Center, Richmond, Virginia
| | - Maria Kelly
- Department of Radiation Oncology, VHA National Radiation Oncology Program Office, Richmond, Virginia
| | - Jatinder Palta
- Department of Radiation Oncology, Virginia Commonwealth University and Hunter Holmes McGuire VA Medical Center, Richmond, Virginia; Department of Radiation Oncology, VHA National Radiation Oncology Program Office, Richmond, Virginia
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - Salma K Jabbour
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Zhongxing Liao
- Division of Radiation Oncology, Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Cancer Institute, Detroit, Michigan
| | - Charles R Thomas
- Radiation Oncology, Dartmouth Cancer Institute, Hanover, New Hampshire
| | - Robert D Timmerman
- Department of Radiation Oncology, University of Texas Southwestern Medical School, Dallas, Texas
| | - Maria Werner-Wasik
- Department of Radiation Oncology, Sydney Kimmel Cancer Center of Thomas Jefferson University, Philadelphia, Pennsylvania
| | - Randi Kudner
- American Society for Radiation Oncology, Arlington, Virginia
| | - Emily Wilson
- American Society for Radiation Oncology, Arlington, Virginia
| | - Charles B Simone
- Department of Radiation Oncology, New York Proton Center, New York, New York
| |
Collapse
|
5
|
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188319] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.
Collapse
|