Abstract
The aim of this study was to determine the feasibility of automated detection of adrenal nodules, a common finding on CT, using a newly developed search engine that mines dictated radiology reports. To ensure Health Insurance Portability and Accountability Act compliance, we utilized a preexisting de-identified database of 32,974 CT reports from February 1, 2009 to February 28, 2010. Common adrenal descriptors from 29 staff radiologists were used to develop an automated rule-based algorithm targeting adrenal findings. Each sentence within the free text of reports was searched with an adapted NegEx negation algorithm. The algorithm was refined using a 2-week test period of reports and subsequently validated using a 6-week period. Manual review of the 3,693 CT reports in the validation period identified 222 positive reports while the algorithm detected 238 positive reports. The algorithm identified one true positive report missed on manual review for a total of 223 true positive reports. This resulted in a precision of 91% (217 of 238) and a recall of 97% (217 of 223). The sensitivity of the query was 97.3% (95% confidence interval (CI), 93.9–98.9%), and the specificity was 99.3% (95% CI, 99.1–99.6%). The positive predictive value of the algorithm was 91.0% (95% CI, 86.6–94.3%), and the negative predictive value was 99.8% (95% CI, 99.6–99.9%). The prevalence of true positive adrenal findings identified by the query (7.1%) was nearly identical to the true prevalence (7.2%). Automated detection of language describing common findings in imaging reports, such as adrenal nodules on CT, is feasible.
Similar content being viewed by others
References
Kloos RT, et al: Incidentally discovered adrenal masses. Endocr Rev 16(4):460–484, 1995
Song JH, Chaudhry FS, Mayo-Smith WW: The incidental adrenal mass on CT: prevalence of adrenal disease in 1,049 consecutive adrenal masses in patients with no known malignancy. AJR Am J Roentgenol 190(5):1163–1168, 2008
Song JH, Chaudhry FS, Mayo-Smith WW: The incidental indeterminate adrenal mass on CT (>10 H) in patients without cancer: is further imaging necessary? Follow-up of 321 consecutive indeterminate adrenal masses. AJR Am J Roentgenol 189(5):1119–1123, 2007
Grumbach MM, et al: Management of the clinically inapparent adrenal mass (“incidentaloma”). Ann Intern Med 138(5):424–429, 2003
Berland LL, et al: Managing incidental findings on abdominal CT: white paper of the ACR incidental findings committee. J Am Coll Radiol 7(10):754–773, 2010
Management of the Clinically Inapparent Adrenal Mass (Incidentaloma): National Institutes of Health State-of-the-Science Conference Statement February 4–6, 2002. 2002. Available from: http://consensus.nih.gov/2002/2002AdrenalIncidentalomasos021html.htm.
Zeiger MA, et al: The American Association of Clinical Endocrinologists and American Association of Endocrine Surgeons medical guidelines for the management of adrenal incidentalomas. Endocr Pract 15(Suppl 1):1–20, 2009
Graham DJ, McHenry CR: The adrenal incidentaloma: guidelines for evaluation and recommendations for management. Surg Oncol Clin N Am 7(4):749–764, 1998
Swanson DR: Searching natural language text by computer. Machine indexing and text searching offer an approach to the basic problems of library automation. Science 132:1099–1104, 1960
Friedman C, Hripcsak G: Natural language processing and its future in medicine. Acad Med 74(8):890–895, 1999
Hripcsak G, et al: Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med 122(9):681–688, 1995
Sager N, et al: Natural language processing and the representation of clinical data. J Am Med Inform Assoc 1(2):142–160, 1994
Langlotz CP: Automatic structuring of radiology reports: harbinger of a second information revolution in radiology. Radiology 224(1):5–7, 2002
Elkin PL, et al: A controlled trial of automated classification of negation from clinical notes. BMC Med Inform Decis Mak 5:13, 2005
Huang Y, Lowe HJ: A novel hybrid approach to automated negation detection in clinical radiology reports. J Am Med Inform Assoc 14(3):304–311, 2007
Mutalik PG, Deshpande A, Nadkarni PM: Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. J Am Med Inform Assoc 8(6):598–609, 2001
Benitah N, et al: Minor morphologic abnormalities of adrenal glands at CT: prognostic importance in patients with lung cancer. Radiology 235(2):517–522, 2005
Chapman WW, et al: A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 34(5):301–310, 2001
Aronow DB, Fangfang F, Croft WB: Ad hoc classification of radiology reports. J Am Med Inform Assoc 6(5):393–411, 1999
Vella A, Nippoldt TB, Morris 3rd, JC: Adrenal hemorrhage: a 25-year experience at the Mayo Clinic. Mayo Clin Proc 76(2):161–168, 2001
Bovio S, et al: Prevalence of adrenal incidentaloma in a contemporary computerized tomography series. J Endocrinol Invest 29(4):298–302, 2006
Hedeland H, Ostberg G, Hokfelt B: On the prevalence of adrenocortical adenomas in an autopsy material in relation to hypertension and diabetes. Acta Med Scand 184(3):211–214, 1968
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Table
Rights and permissions
About this article
Cite this article
Zopf, J.J., Langer, J.M., Boonn, W.W. et al. Development of Automated Detection of Radiology Reports Citing Adrenal Findings. J Digit Imaging 25, 43–49 (2012). https://doi.org/10.1007/s10278-011-9425-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10278-011-9425-7