1,721,016 research outputs found
A link between seasonality and radiation-related toxicity: The big time or time will tell?: Response to Nowicka et al.
No full text
A call for careful interpretation of outcome of nonsurgical approach in clinical stage IIIa non-small cell lung cancer in the National Cancer Database.
No full textDesigning a Digital Medical Interview Assistant for Radiology.
Full text linkRadiologists rarely interact with the patients whose radiological images they are reviewing due to time and resource constraints. However, relevant information about the patient's medical history could improve reporting performance and quality. In this work, our objective was to collect requirements for a digital medical interview assistant (DMIA) that collects the medical history from patients by means of a conversational agent and structures as well as provides the collected data to radiologists. Requirements were gathered based on a narrative literature review, a patient questionnaire and input from a radiologist. Based on these results, a system architecture for the DMIA was developed. 37 functional and 17 non-functional requirements were identified. The resulting architecture comprises five components, namely Chatbot, Natural language processing (NLP), Administration, Content Definition and Workflow Engine. To be able to quickly adapt the chatbot content according to the information needs of a specific radiological examination, there is a need for developing a sustainable process for the content generation that considers standardized data modelling as well as rewording of clinical language into consumer health vocabulary understandable to a diverse patient user group
(Common) Data Elements in Radiation Oncology: A Systematic Literature Review.
No full textPURPOSE
Structured medical data documentation is highly relevant in a data-driven discipline such as radiation oncology. Defined (common) data elements (CDEs) can be used to record data in clinical trials, health records, or computer systems for improved standardization and data exchange. The International Society for Radiation Oncology Informatics initiated a project for a scientific literature analysis of defined data elements for structured documentation in radiation oncology.
METHODS
We performed a systematic literature review on both PubMed and Scopus to analyze publications relevant to the utilization of specified data elements for the documentation of radiation therapy (RT)-related information. Relevant publications were retrieved as full-text and searched for published data elements. Finally, the extracted data elements were quantitatively analyzed and classified.
RESULTS
We found a total of 452 publications, of which 46 were considered relevant for structured data documentation. Twenty-nine publications addressed defined RT-specific data elements, of which 12 publications provided data elements. Only two publications focused on data elements in radiation oncology. The 29 analyzed publications were heterogeneous regarding the subject and usage of the defined data elements, and different concepts/terms for defined data elements were used.
CONCLUSION
The literature about structured data documentation in radiation oncology using defined data elements is scarce. There is a need for a comprehensive list of RT-specific CDEs the radio-oncologic community can rely on. As it has been done in other medical fields, establishing such a list would be of great value for clinical practice and research as it would promote interoperability and standardization
Adjuvant immunotherapy in resected early non-small cell lung cancer-battle lost, hopefully not the war!
No full textTreatment options for isolated locoregional recurrences of nonsmall cell lung cancer after surgery: yes, radiation therapy too!
No full textToward Data-Driven Radiation Oncology Using Standardized Terminology as a Starting Point: Cross-sectional Study.
Full text linkBACKGROUND
The inability to seamlessly exchange information across radiation therapy ecosystems is a limiting factor in the pursuit of data-driven clinical practice. The implementation of semantic interoperability is a prerequisite for achieving the full capacity of the latest developments in personalized and precision medicine, such as mathematical modeling, advanced algorithmic information processing, and artificial intelligence approaches.
OBJECTIVE
This study aims to evaluate the state of terminology resources (TRs) dedicated to radiation oncology as a prerequisite for an oncology semantic ecosystem. The goal of this cross-sectional analysis is to quantify the state of the art in radiation therapy specific terminology.
METHODS
The Unified Medical Language System (UMLS) was searched for the following terms: radio oncology, radiation oncology, radiation therapy, and radiotherapy. We extracted 6509 unique concepts for further analysis. We conducted a quantitative analysis of available source vocabularies (SVs) and analyzed all UMLS SVs according to the route source, number, author, location of authors, license type, the lexical density of TR, and semantic types. Descriptive data are presented as numbers and percentages.
RESULTS
The concepts were distributed across 35 SVs. The median number of unique concepts per SV was 5 (range 1-5479), with 14% (5/35) of SVs containing 94.59% (6157/6509) of the concepts. The SVs were created by 29 authors, predominantly legal entities registered in the United States (25/35, 71%), followed by international organizations (6/35, 17%), legal entities registered in Australia (2/35, 6%), and the Netherlands and the United Kingdom with 3% (1/35) of authors each. Of the total 35 SVs, 16 (46%) did not have any restrictions on use, whereas for 19 (54%) of SVs, some level of restriction was required. Overall, 57% (20/35) of SVs were updated within the last 5 years. All concepts found within radiation therapy SVs were labeled with one of the 29 semantic types represented within UMLS. After removing the stop words, the total number of words for all SVs together was 56,219, with a median of 25 unique words per SV (range 3-50,682). The total number of unique words in all SVs was 1048, with a median of 19 unique words per vocabulary (range 3-406). The lexical density for all concepts within all SVs was 0 (0.02 rounded to 2 decimals). Median lexical density per unique SV was 0.7 (range 0.0-1.0). There were no dedicated radiation therapy SVs.
CONCLUSIONS
We did not identify any dedicated TRs for radiation oncology. Current terminologies are not sufficient to cover the need of modern radiation oncology practice and research. To achieve a sufficient level of interoperability, of the creation of a new, standardized, universally accepted TR dedicated to modern radiation therapy is required
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