ArticleOpen medicine (Warsaw, Poland)2019
Analysis of Hormone Receptor Status in Primary and Recurrent Breast Cancer Via Data Mining Pathology Reports.
Article in Open medicine (Warsaw, Poland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.Journal of Korean medical science · 2026Article
- Article
- Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression.BioMed research international · 2022Article
- Article
- Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.BioMed research international · 2020Article
- Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHormone receptors of breast cancer, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (Her-2), are important prognostic factors for breast cancer.
objectiveThe current study aimed to develop a method to retrieve the statistics of hormone receptor expression status, documented in pathology reports, given their importance in research for primary and recurrent breast cancer, and quality management of pathology laboratories.
methodA two-stage text mining approach via regular expression-based word/phrase matching, was developed to retrieve the data.
resultsThe method achieved a sensitivity of 98.8%, 98.7% and 98.4% for extraction of ER, PR, and Her-2 results. The hormone expression status from 3679 primary and 44 recurrent breast cancer cases was successfully retrieved with the method. Statistical analysis of these data showed that the recurrent disease had a significantly lower positivity rate for ER (54.5% vs 76.5%, p=0.001278) than primary breast cancer and a higher positivity rate for Her-2 (48.8% vs 16.2%, p=9.79e-8). These results corroborated the previous literature.
conclusionText mining on pathology reports using the developed method may benefit research of primary and recurrent breast cancer.
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Registered trials
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