title: Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.
abstract: 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 = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .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. Cancer 2017;114-121. © 2016 American Cancer Society.
authors: Houston Methodist Cancer Center, Houston, Texas.|Cancer Research Program, Houston Methodist Research Institute, Houston, Texas.|Department of Medicine, Weill Cornell Medicine, New York, New York.
authors: Tejal A
authors: TA
authors: Patel
authors: Department of Informatics Development, Houston Methodist Hospital, Houston, Texas.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas.
authors: Mamta
authors: M
authors: Puppala
authors: Department of Informatics Development, Houston Methodist Hospital, Houston, Texas.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas.
authors: Richard O
authors: RO
authors: Ogunti
authors: Houston Methodist Cancer Center, Houston, Texas.|Cancer Research Program, Houston Methodist Research Institute, Houston, Texas.
authors: Joe E
authors: JE
authors: Ensor
authors: Department of Informatics Development, Houston Methodist Hospital, Houston, Texas.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas.
authors: Tiancheng
authors: T
authors: He
authors: Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas School of Public Health, Houston, Texas.
authors: Jitesh B
authors: JB
authors: Shewale
authors: Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.|Department of Mathematics, Technical University of Munich, Garching, Germany.
authors: Donna P
authors: DP
authors: Ankerst
authors: Division of Hematology Oncology CTRC, University of Texas Health Science Center San Antonio, San Antonio, Texas.
authors: Virginia G
authors: VG
authors: Kaklamani
authors: Houston Methodist Cancer Center, Houston, Texas.|Cancer Research Program, Houston Methodist Research Institute, Houston, Texas.|Department of Medicine, Weill Cornell Medicine, New York, New York.
authors: Angel A
authors: AA
authors: Rodriguez
authors: Cancer Research Program, Houston Methodist Research Institute, Houston, Texas.|Department of Informatics Development, Houston Methodist Hospital, Houston, Texas.|Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute, Houston, Texas.|Department of Radiology, Neurology, and Neuroscience, Weill Cornell Medicine, New York, New York.|Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.
authors: Stephen T C
authors: ST
authors: Wong
authors: Houston Methodist Cancer Center, Houston, Texas.|Cancer Research Program, Houston Methodist Research Institute, Houston, Texas.|Department of Medicine, Weill Cornell Medicine, New York, New York.
authors: Jenny C
authors: JC
authors: Chang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology*; D057225:Data Mining / Q000379:methods; D020000:Decision Support Systems, Clinical; D006801:Humans; D008327:Mammography / Q000379:methods; D008875:Middle Aged; D009323:Natural Language Processing; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D012984:Software
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; C508053:ERBB2 protein, human
keywords: data mining; imaging characteristics; mammographic to pathologic correlation; natural language processing; subtypes of breast cancer