Multinodular disease: Anatomic localization at thin-section CT - Multireader evaluation of a simple algorithm

James F. Gruden, W. Richard Webb, David P. Naidich, Georgeann McGuinness

Research output: Contribution to journalArticle

58 Citations (Scopus)

Abstract

PURPOSE: To evaluate the interobserver variability and accuracy of an algorithm for anatomic localization of small nodules evident on thin-section computed tomographic (CT) images of the lungs. MATERIALS AND METHODS: Four experienced chest radiologists independently evaluated thin-section CT images in 58 patients by using an algorithm and a standard score sheet. Nodules were placed into four possible anatomic locations or categories: perilymphatic, random, associated with small airways disease, or centrilobular. Algorithm accuracy was assessed by comparing the localization by the observers to that expected for each specific disease in the study group on the basis of reports in the literature. Interobserver variability was assessed by placing cases into one of three groups: (a) complete concordance, (b) triple concordance, and (c) discordant. RESULTS: All observers agreed in 79% (46 of 58) of the cases with regard to nodule localization; three of the four concurred in an additional 17% (10 of 58). The observers were correct in 218 (94%) of 232 localizations in the 58 cases. There were no apparent differences in the number of either discordant or incorrect localizations between the observers. The most noteworthy source of error and of disagreement between observers was the confusion of perilymphatic and small airways disease-associated nodules in a small number of cases. CONCLUSION: The pro posed algorithm is reproducible and accurate in the majority of cases and facilitates nodule localization at thin-section CT.

Original languageEnglish (US)
Pages (from-to)711-720
Number of pages10
JournalRadiology
Volume210
Issue number3
StatePublished - Mar 1999

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Observer Variation
Research Design
Thorax
Lung
Radiologists

Keywords

  • Computed tomography (CT), thin-section
  • Lung neoplasm, secondary
  • Lung, CT
  • Lung, infection
  • Lung, interstitial disease
  • Lung, nodule
  • Mycobacteria
  • Sarcoidosis
  • Tuberculosis

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology

Cite this

Gruden, J. F., Webb, W. R., Naidich, D. P., & McGuinness, G. (1999). Multinodular disease: Anatomic localization at thin-section CT - Multireader evaluation of a simple algorithm. Radiology, 210(3), 711-720.

Multinodular disease : Anatomic localization at thin-section CT - Multireader evaluation of a simple algorithm. / Gruden, James F.; Webb, W. Richard; Naidich, David P.; McGuinness, Georgeann.

In: Radiology, Vol. 210, No. 3, 03.1999, p. 711-720.

Research output: Contribution to journalArticle

Gruden, JF, Webb, WR, Naidich, DP & McGuinness, G 1999, 'Multinodular disease: Anatomic localization at thin-section CT - Multireader evaluation of a simple algorithm', Radiology, vol. 210, no. 3, pp. 711-720.
Gruden, James F. ; Webb, W. Richard ; Naidich, David P. ; McGuinness, Georgeann. / Multinodular disease : Anatomic localization at thin-section CT - Multireader evaluation of a simple algorithm. In: Radiology. 1999 ; Vol. 210, No. 3. pp. 711-720.
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N2 - PURPOSE: To evaluate the interobserver variability and accuracy of an algorithm for anatomic localization of small nodules evident on thin-section computed tomographic (CT) images of the lungs. MATERIALS AND METHODS: Four experienced chest radiologists independently evaluated thin-section CT images in 58 patients by using an algorithm and a standard score sheet. Nodules were placed into four possible anatomic locations or categories: perilymphatic, random, associated with small airways disease, or centrilobular. Algorithm accuracy was assessed by comparing the localization by the observers to that expected for each specific disease in the study group on the basis of reports in the literature. Interobserver variability was assessed by placing cases into one of three groups: (a) complete concordance, (b) triple concordance, and (c) discordant. RESULTS: All observers agreed in 79% (46 of 58) of the cases with regard to nodule localization; three of the four concurred in an additional 17% (10 of 58). The observers were correct in 218 (94%) of 232 localizations in the 58 cases. There were no apparent differences in the number of either discordant or incorrect localizations between the observers. The most noteworthy source of error and of disagreement between observers was the confusion of perilymphatic and small airways disease-associated nodules in a small number of cases. CONCLUSION: The pro posed algorithm is reproducible and accurate in the majority of cases and facilitates nodule localization at thin-section CT.

AB - PURPOSE: To evaluate the interobserver variability and accuracy of an algorithm for anatomic localization of small nodules evident on thin-section computed tomographic (CT) images of the lungs. MATERIALS AND METHODS: Four experienced chest radiologists independently evaluated thin-section CT images in 58 patients by using an algorithm and a standard score sheet. Nodules were placed into four possible anatomic locations or categories: perilymphatic, random, associated with small airways disease, or centrilobular. Algorithm accuracy was assessed by comparing the localization by the observers to that expected for each specific disease in the study group on the basis of reports in the literature. Interobserver variability was assessed by placing cases into one of three groups: (a) complete concordance, (b) triple concordance, and (c) discordant. RESULTS: All observers agreed in 79% (46 of 58) of the cases with regard to nodule localization; three of the four concurred in an additional 17% (10 of 58). The observers were correct in 218 (94%) of 232 localizations in the 58 cases. There were no apparent differences in the number of either discordant or incorrect localizations between the observers. The most noteworthy source of error and of disagreement between observers was the confusion of perilymphatic and small airways disease-associated nodules in a small number of cases. CONCLUSION: The pro posed algorithm is reproducible and accurate in the majority of cases and facilitates nodule localization at thin-section CT.

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