ArticleApplied clinical informatics2022
Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model.
Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A review on knowledge and information extraction from PDF documents and storage approaches.Frontiers in artificial intelligence · 2025Pooled it
- OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Preoperative predictors of occult central lymph node metastasis in clinically node-negative papillary thyroid carcinoma: a common data model-based retrospective cohort study.Annals of surgical treatment and research · 2026Article
- Evidence categories in systematic assessment of cancer overdiagnosis.BMJ evidence-based medicine · 2025Review
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Improving Discrete Documentation of Cancer Staging-An Alert-Free Approach.Applied clinical informatics · 2025Article
- A scoping review of OMOP CDM adoption for cancer research using real world data.NPJ digital medicine · 2025Article
- Developing a named entity framework for thyroid cancer staging and risk level classification using large language models.NPJ digital medicine · 2025Article
- Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2024Article
- Coverage of Physical Therapy Assessments in the Observational Medical Outcomes Partnership Common Data Model.Applied clinical informatics · 2024Article
- Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.JMIR medical informatics · 2024Article
- A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.Mayo Clinic proceedings. Digital health · 2024Article
- Large Language Models for Efficient Medical Information Extraction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer 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.
objectiveWe 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.
methodsThyroid 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.
resultsThe 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%.
conclusionAs 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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