Automatic classification of mammography reports by BI-RADS breast tissue composition class

Percha, Bethany; Nassif, Houssam; Lipson, Jafi A.; Burnside, Elizabeth S.; Rubin, Daniel L.

doi:10.1136/amiajnl-2011-000607

Cited by 51 publications

(34 citation statements)

References 25 publications

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“…For breast imaging reports, information extraction of Breast Imaging-Reporting and Data System (BI-RADS) assessment categories and breast tissue composition categories has been successfully completed [14,15]. To our knowledge, the use of information extraction to identify specific imaging findings for a variety of breast imaging modalities has not been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

et al. 2015

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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 Fmeasure. 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.

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Section: Introductionmentioning

confidence: 99%

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

et al. 2015

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“…A recent publication has described an algorithm that classifies mammography reports by BI-RADS breasts tissue composition, with a very high accuracy of greater than 99.0 % [12]. Overall, this NLP breast composition extractor demonstrated slightly superior performance to our NLP BI-RADS category extractor.…”

Section: Discussionmentioning

confidence: 81%

“…NLP algorithms can extract meaningful information from free text and have been successfully applied to radiology reports to identify positive findings, recommendations, and tumor status [6][7][8][9]. Specific to breast imaging, NLP has been applied to mammography reports to identify findings suspicious for breast cancer [10], correlate findings and their locations [11], determine BI-RADS breast tissue composition [12], and extract multiple other reported attributes [13]. We hypothesized that NLP can accurately extract BI-RADS final assessment categories from radiology reports.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing

et al. 2013

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The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies.

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“…For example, Percha et al used a series of regular expression based rules to classify mammography reports into BI-RADS breast tissue composition categories (e.g. fatty, dense), achieving an accuracy of >99% [38], whereas Harkema et al achieved an average accuracy of 74% when extracting complex variables relevant to measuring the quality of colonoscopy exams (e.g. "had the patient had a previous colonoscopy?")…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Using Clinical Element Models (CEM) to Standardize Phenotype Variables in the Database of Genotypes and Phenotypes (dbGaP)

Lin

Tharp

Conway

et al. 2012

2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology

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The database of Genotypes and Phenotypes (dbGaP) contains various types of data generated from genome-wide association studies (GWAS). These data can be used to facilitate novel scientific discoveries and to reduce cost and time for exploratory research. However, idiosyncrasies and inconsistencies in phenotype variable names are a major barrier to reusing these data. We addressed these challenges in standardizing phenotype variables by formalizing their descriptions using Clinical Element Models (CEM). Designed to represent clinical data, CEMs were highly expressive and thus were able to represent a majority (77.5%) of the 215 phenotype variable descriptions. However, their high expressivity also made it difficult to directly apply them to research data such as phenotype variables in dbGaP. Our study suggested that simplification of the template models makes it more straightforward to formally represent the key semantics of phenotype variables.

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Automatic classification of mammography reports by BI-RADS breast tissue composition class

Cited by 51 publications

References 25 publications

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry

Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing

Feasibility of Using Clinical Element Models (CEM) to Standardize Phenotype Variables in the Database of Genotypes and Phenotypes (dbGaP)

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