Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression.
Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated d...
| Publicado en: | BioMed Research International pp. 1 - 18 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
3/12/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155726244&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155726244 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 3/12/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155726244 155726244 155726244 10.1155/2022/6157861 155726244 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression. aug: au: Achilonu, Okechinyere J. Singh, Elvira Nimako, Gideon Eijkemans, René M. J. C. Musenge, Eustasius affil: Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Parktown, Johannesburg, South Africa sug: subj: Information Retrieval Pathology, Molecular Trends Breast Neoplasms Trends Natural Language Processing Breast Neoplasms Pathology Algorithms Tumor Markers, Biological Metabolism South Africa Age Factors Race Factors Neoplasm Grading Epidermal Growth Factors Descriptive Statistics Human ab: Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated decision support. Herewith, we developed a rule-based natural language processing algorithm that retrieved and extracted important BC parameters from free-text pathology reports towards exploring molecular subtypes and Ki67-proliferation trends. We considered malignant BC pathology reports with different free-text narrative attributes from the South African National Health Laboratory Service. The reports were preprocessed and parsed through the algorithm. Parameters extracted by the algorithm were validated against manually extracted parameters. For all parameters extracted, we obtained accurate annotations of 83-100%, 93-100%, 91-100%, and 92-100% precision, recall, F 1 -score, and kappa, respectively. There was a significant trend in the proportion of each molecular subtype by patient age, histologic type, grade, Ki67, and race. The findings also showed significant association in the Ki67 trend with hormone receptors, human epidermal growth factors, age, grade, and race. Our approach bridges the gap between data availability and actionable knowledge and provides a framework that could be adapted and reused in other cancers and beyond cancer studies. Information extracted from these reports showed interesting trends that may be exploited for BC screening and treatment resources in South Africa. Finally, this study strongly encourages the implementation of a synoptic style pathology report in South Africa. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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