Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.
Purpose: To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classification system.Materials and Meth...
| Published in: | Radiology Vol. 258; no. 1; pp. 73 - 81 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
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Wolters Kluwer Health
2011 Jan
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104969941&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104969941 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00338419 0IQ jtl: Radiology issn: 00338419 maglogo: N pubinfo: dt: 2011 Jan vid: 258 iid: 1 pid: 79357 pub: Wolters Kluwer Health place: New York, New York artinfo: ui: 104969941 104969941 NLM20971779 2010891443 10.1148/radiol.10081308 NLM20971779 PMC3009385 104969941 ppf: 73 ppct: 8 formats: tig: atl: Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents. aug: au: Singh S Maxwell J Baker JA Nicholas JL Lo JY Singh, Swatee Maxwell, Jeff Baker, Jay A Nicholas, Jennifer L Lo, Joseph Y affil: Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, 2424 Erwin Rd, Ste 302, Durham, NC 27705, USA sug: subj: Breast Neoplasms Classification Clinical Competence Diagnosis, Computer Assisted Methods Adolescence Adult Aged Biopsy Breast Neoplasms Radiography Discriminant Analysis Female Human Internship and Residency Mammography Middle Age Observer Bias ROC Curve Sensitivity and Specificity Adolescent: 13-18 years Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Female ab: Purpose: To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classification system.Materials and Methods: Institutional review board approval was obtained for this HIPAA-compliant study, and the requirement to obtain informed consent was waived. Images of 50 breast lesions were individually interpreted by seven dedicated breast imagers and 10 radiology residents, yielding 850 lesion interpretations. Lesions were described with use of 11 descriptors from the Breast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen κ statistic. Those 11 features were selected, along with patient age, and merged together by a linear discriminant analysis (LDA) classification model trained by using 1005 previously existing cases. Variability in the recommendations of the computer model for different observers was also calculated with the Cohen κ statistic.Results: A significant difference was observed for six lesion features, and radiology residents had greater interobserver variability in their selection of five of the six features than did dedicated breast imagers. The LDA model accurately classified lesions for both sets of observers (area under the receiver operating characteristic curve = 0.94 for residents and 0.96 for dedicated imagers). Sensitivity was maintained at 100% for residents and improved from 98% to 100% for dedicated breast imagers. For residents, the computer model could potentially improve the specificity from 20% to 40% (P < .01) and the κ value from 0.09 to 0.53 (P < .001). For dedicated breast imagers, the computer model could increase the specificity from 34% to 43% (P = .16) and the κ value from 0.21 to 0.61 (P < .001).Conclusion: Among findings showing a significant difference, there was greater interobserver variability in lesion descriptions among residents; however, an LDA model using data from either dedicated breast imagers or residents yielded a consistently high performance in the differentiation of benign from malignant breast lesions, demonstrating potential for improving specificity and decreasing interobserver variability in biopsy recommendations. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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