Accepted for/Published in: JMIR Formative Research
Date Submitted: Jan 28, 2021
Date Accepted: Sep 18, 2021
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Toward data-driven radiation oncology – standardized terminology as a starting point
ABSTRACT
Background:
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 the achievement of full capacity of the latest developments in personalized and precision medicine, such as mathematical modeling, advanced algorithmic information processing as well as artificial intelligence approaches.
Objective:
In this work, we evaluate the current state of terminology resources (TR) dedicated to radiation oncology, as a prerequisite of an oncology semantic ecosystem.
Methods:
Unified Medical Language System™ (UMLS) was searched for the following terms: terms “radio oncology”, “radiation oncology”, “radiation therapy” and “radiotherapy.” We extracted a total of 6509 unique concepts for further analysis. Data processing and lexical analysis was done with Python v3.7 and python library The Natural Language Toolkit (NLTK) version 3.5.
Results:
Concepts were distributed across 35 terminology resources (TRs). The median number of unique concepts per TR was 5 (range 1 – 5479). Five TRs (14% of all TRs) contained 95% (6157) concepts. TRs were created by 29 authors. Authors of the TRs were predominantly legal entities registered in the U.S. (n=25, 71.4%), followed by international organizations (n=6, 17.1%), and legal entities registered in Australia (n=2, 5.7%), the Netherlands and United Kingdom with 1 (2.9%) author each. Out of total 35 TRs, 45.7% (n=16) did not have any restrictions on usage, while as for 54.3% (n=19) of TRs some level of restriction was required. Twenty (57%) of the TRs were updated within the last five years. All concepts found within RT-TRs were labelled with one of 29 semantic types represented within UMLS. After removal of “stop words”, the total number of all words for all TRs together was 56,219 with in median 25 unique words per TR (range 3 - 50682). The total number of unique words in all TRs was 1048 with in median 19 unique words per vocabulary (range 3 - 406). Lexical density for all concepts within all TRs was zero (0.02 rounded to two decimals). Median lexical density per unique TR was 0.7 (range 0.0 -1.0). There was no dedicated radiation therapy TRs.
Conclusions:
We did not identify any dedicated terminology resource 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, the creation of a new, standardized, universally accepted terminology resource, dedicated to modern radiation therapy, is mandatory. Clinical Trial: Not applicable
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