Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.
Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 5; pp. 567 - 576 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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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=109465614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109465614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2015 vid: 28 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109465614 109465614 109465614 10.1007/s10278-014-9762-4 NLM25561069 PMC4570892 109465614 ppf: 567 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry. aug: au: Lacson, Ronilda Harris, Kimberly Brawarsky, Phyllis Tosteson, Tor Onega, Tracy Tosteson, Anna Kaye, Abby Gonzalez, Irina Birdwell, Robyn Haas, Jennifer affil: Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston USA sug: subj: Cancer Screening Breast Neoplasms Diagnosis Natural Language Processing Information Retrieval National Cancer Institute (U.S.) Automation Reports Radiography Resource Databases Data Curation Vocabulary, Controlled Prevalence Evaluation Research Random Sample Validation Studies kappa Statistic Confidence Intervals Descriptive Statistics Human Funding Source ab: Breast cancer screening is central to early breast cancer detection. Identifying and monitoring process measures for screening is a focus of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative, which requires participating centers to report structured data across the cancer screening continuum. We evaluate the accuracy of automated information extraction of imaging findings from radiology reports, which are available as unstructured text. We present prevalence estimates of imaging findings for breast imaging received by women who obtained care in a primary care network participating in PROSPR ( n = 139,953 radiology reports) and compared automatically extracted data elements to a 'gold standard' based on manual review for a validation sample of 941 randomly selected radiology reports, including mammograms, digital breast tomosynthesis, ultrasound, and magnetic resonance imaging (MRI). The prevalence of imaging findings vary by data element and modality (e.g., suspicious calcification noted in 2.6 % of screening mammograms, 12.1 % of diagnostic mammograms, and 9.4 % of tomosynthesis exams). In the validation sample, the accuracy of identifying imaging findings, including suspicious calcifications, masses, and architectural distortion (on mammogram and tomosynthesis); masses, cysts, non-mass enhancement, and enhancing foci (on MRI); and masses and cysts (on ultrasound), range from 0.8 to1.0 for recall, precision, and F-measure. Information extraction tools can be used for accurate documentation of imaging findings as structured data elements from text reports for a variety of breast imaging modalities. These data can be used to populate screening registries to help elucidate more effective breast cancer screening processes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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