Automatic abstraction of imaging observations with their characteristics from mammography reports

Bozkurt, Selen; Lipson, Jafi A.; Utku, S.; Rubin, Daniel L.

doi:10.1136/amiajnl-2014-003009

Cited by 28 publications

(29 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We previously developed an NLP system for automatic annotation and extraction of imaging observations that characterize breast lesions, the locations of the lesions, and other attributes of breast lesions described in mammography reports [38]. We used BI-RADS [39] to provide a controlled terminology for the terms used in mammography reports for describing named entities (imaging observations and locations of lesions).…”

Section: Methodsmentioning

confidence: 99%

“…The BI-RADS terminology contains descriptors , which are specialized terms that describe breast density and lesion features (types of imaging observations). Since BI-RADS is not distributed in a structured format, we previously created a simple ontology structure of this terminology for our system (“BI-RADS ontology”) [38]. Our NLP system takes as input a free text mammography report and produces as output a set of information frames summarizing each lesion described in the report and its attributes, with all terms being normalized to the BI-RADS terminology [38] (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…2a). The system performed extraction of imaging observations with their modifiers from text reports with precision = 94.9%, recall = 90.9%, and F-measure = 92% [38]. …”

Section: Methodsmentioning

confidence: 99%

“…This clinical information is reported in the history section of the mammography reports, so our module segmented this section of the report and extracted the information using a similar rule-based approach (Fig. 2b) that we described previously [38]. We used the Con-Text algorithm [41], which determines whether the clinical conditions mentioned in clinical reports are negated, hypothetical, historical, or experienced by someone other than the patient.…”

Section: Methodsmentioning

confidence: 99%

“…For this evaluation, we used the same 300 mammography reports which we previously used to evaluate our NLP system [38]. …”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Using automatically extracted information from mammography reports for decision-support

Bozkurt

Gimenez

Burnside

et al. 2016

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

Objective To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate goal of this system is to provide decision support as part of the workflow of producing the radiology report. Materials and methods We built a system that uses an NLP information extraction system (which extract BI-RADS descriptors and clinical information from mammography reports) to provide the necessary inputs to a Bayesian network (BN) decision support system (DSS) that estimates lesion malignancy from BI-RADS descriptors. We used this integrated system to predict diagnosis of breast cancer from radiology text reports and evaluated it with a reference standard of 300 mammography reports. We collected two different outputs from the DSS: (1) the probability of malignancy and (2) the BI-RADS final assessment category. Since NLP may produce imperfect inputs to the DSS, we compared the difference between using perfect (“reference standard”) structured inputs to the DSS (“RS-DSS”) vs NLP-derived inputs (“NLP-DSS”) on the output of the DSS using the concordance correlation coefficient. We measured the classification accuracy of the BI-RADS final assessment category when using NLP-DSS, compared with the ground truth category established by the radiologist. Results The NLP-DSS and RS-DSS had closely matched probabilities, with a mean paired difference of 0.004 ± 0.025. The concordance correlation of these paired measures was 0.95. The accuracy of the NLP-DSS to predict the correct BI-RADS final assessment category was 97.58%. Conclusion The accuracy of the information extracted from mammography reports using the NLP system was sufficient to provide accurate DSS results. We believe our system could ultimately reduce the variation in practice in mammography related to assessment of malignant lesions and improve management decisions.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…2a). The system performed extraction of imaging observations with their modifiers from text reports with precision = 94.9%, recall = 90.9%, and F-measure = 92% [38]. …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…For this evaluation, we used the same 300 mammography reports which we previously used to evaluate our NLP system [38]. …”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Using automatically extracted information from mammography reports for decision-support

Bozkurt

Gimenez

Burnside

et al. 2016

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods

Patel

Puppala

Ogunti

et al. 2016

Cancer

View full text Add to dashboard Cite

BACKGROUND: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. METHODS: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. RESULTS: The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P 5.0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P 5.0078 and P 5.0002, respectively). CONCLUSIONS: Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support.

show abstract

Natural Language Processing for Cardiovascular Applications

Tariq

Santos

Banerjee

2022

Artificial Intelligence in Cardiothoracic Imaging

View full text Add to dashboard Cite

Automatic abstraction of imaging observations with their characteristics from mammography reports

Cited by 28 publications

References 35 publications

Using automatically extracted information from mammography reports for decision-support

Using automatically extracted information from mammography reports for decision-support

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods

Natural Language Processing for Cardiovascular Applications

Contact Info

Product

Resources

About