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Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model

Background  Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outco...

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Autores principales: Yoo, Sooyoung, Yoon, Eunsil, Boo, Dachung, Kim, Borham, Kim, Seok, Paeng, Jin Chul, Yoo, Ie Ryung, Choi, In Young, Kim, Kwangsoo, Ryoo, Hyun Gee, Lee, Sun Jung, Song, Eunhye, Joo, Young-Hwan, Kim, Junmo, Lee, Ho-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200482/
https://www.ncbi.nlm.nih.gov/pubmed/35705182
http://dx.doi.org/10.1055/s-0042-1748144
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author Yoo, Sooyoung
Yoon, Eunsil
Boo, Dachung
Kim, Borham
Kim, Seok
Paeng, Jin Chul
Yoo, Ie Ryung
Choi, In Young
Kim, Kwangsoo
Ryoo, Hyun Gee
Lee, Sun Jung
Song, Eunhye
Joo, Young-Hwan
Kim, Junmo
Lee, Ho-Young
author_facet Yoo, Sooyoung
Yoon, Eunsil
Boo, Dachung
Kim, Borham
Kim, Seok
Paeng, Jin Chul
Yoo, Ie Ryung
Choi, In Young
Kim, Kwangsoo
Ryoo, Hyun Gee
Lee, Sun Jung
Song, Eunhye
Joo, Young-Hwan
Kim, Junmo
Lee, Ho-Young
author_sort Yoo, Sooyoung
collection PubMed
description Background  Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date. Objective  We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports. Methods  Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data. Results  The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%. Conclusion  As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer–specific data for retrospective observational research and participate in multicenter studies.
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spelling pubmed-92004822022-06-16 Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model Yoo, Sooyoung Yoon, Eunsil Boo, Dachung Kim, Borham Kim, Seok Paeng, Jin Chul Yoo, Ie Ryung Choi, In Young Kim, Kwangsoo Ryoo, Hyun Gee Lee, Sun Jung Song, Eunhye Joo, Young-Hwan Kim, Junmo Lee, Ho-Young Appl Clin Inform Background  Cancer staging information is an essential component of cancer research. However, the information is primarily stored as either a full or semistructured free-text clinical document which is limiting the data use. By transforming the cancer-specific data to the Observational Medical Outcome Partnership Common Data Model (OMOP CDM), the information can contribute to establish multicenter observational cancer studies. To the best of our knowledge, there have been no studies on OMOP CDM transformation and natural language processing (NLP) for thyroid cancer to date. Objective  We aimed to demonstrate the applicability of the OMOP CDM oncology extension module for thyroid cancer diagnosis and cancer stage information by processing free-text medical reports. Methods  Thyroid cancer diagnosis and stage-related modifiers were extracted with rule-based NLP from 63,795 thyroid cancer pathology reports and 56,239 Iodine whole-body scan reports from three medical institutions in the Observational Health Data Sciences and Informatics data network. The data were converted into the OMOP CDM v6.0 according to the OMOP CDM oncology extension module. The cancer staging group was derived and populated using the transformed CDM data. Results  The extracted thyroid cancer data were completely converted into the OMOP CDM. The distributions of histopathological types of thyroid cancer were approximately 95.3 to 98.8% of papillary carcinoma, 0.9 to 3.7% of follicular carcinoma, 0.04 to 0.54% of adenocarcinoma, 0.17 to 0.81% of medullary carcinoma, and 0 to 0.3% of anaplastic carcinoma. Regarding cancer staging, stage-I thyroid cancer accounted for 55 to 64% of the cases, while stage III accounted for 24 to 26% of the cases. Stage-II and -IV thyroid cancers were detected at a low rate of 2 to 6%. Conclusion  As a first study on OMOP CDM transformation and NLP for thyroid cancer, this study will help other institutions to standardize thyroid cancer–specific data for retrospective observational research and participate in multicenter studies. Georg Thieme Verlag KG 2022-06-15 /pmc/articles/PMC9200482/ /pubmed/35705182 http://dx.doi.org/10.1055/s-0042-1748144 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Yoo, Sooyoung
Yoon, Eunsil
Boo, Dachung
Kim, Borham
Kim, Seok
Paeng, Jin Chul
Yoo, Ie Ryung
Choi, In Young
Kim, Kwangsoo
Ryoo, Hyun Gee
Lee, Sun Jung
Song, Eunhye
Joo, Young-Hwan
Kim, Junmo
Lee, Ho-Young
Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title_full Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title_fullStr Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title_full_unstemmed Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title_short Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model
title_sort transforming thyroid cancer diagnosis and staging information from unstructured reports to the observational medical outcome partnership common data model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200482/
https://www.ncbi.nlm.nih.gov/pubmed/35705182
http://dx.doi.org/10.1055/s-0042-1748144
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