Evaluation of the effect ofcomputer-aided classification ofbenign and malignant lesions onreader performance in automated three-dimensional breast ultrasound

Tao Tan, Bram Platel, Thorsten Twellmann, Guido van Schie, Roel Mus, André Grivegnée, Ritse M. Mann, Nico Karssemeijer

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)

Abstract

Rationale and Objectives: To investigate the effect of a newly developed computer-aided diagnosis (CAD) system on reader interpretation of breast lesions in automated three-dimensional (3D) breast ultrasound. Materials and Methods: A CAD system was developed to differentiate malignant lesions from benign lesions including automated lesion segmentation in three dimensions; extraction of lesion features such as spiculation, margin contrast, and posterior acoustic behavior; and a classification stage. Eighty-eight patients with breast lesions were included for an observer study: 47 lesions were malignant and 41 were benign. Eleven readers (seven radiologists and four residents) read the cases with and without CAD. We compared the performance of readers with and without CAD using receiver operating characteristic (ROC) analysis. Results: The CAD system had an area under the ROC curve (AUC) of 0.92 for discriminating benign and malignant lesions, whereas the unaided reader AUC ranged from 0.77 to 0.92. Mean performance of inexperienced readers improved when CAD was used (AUC=0.85 versus 0.90; P=.007), whereas mean performance of experienced readers did not change with CAD (AUC=0.89). Conclusions: By using the CAD system for classification of lesions in automated 3D breast ultrasound, which on its own performed as good as the best readers, the performance of inexperienced readers improved while that of experienced readers remained unaffected.

Original languageEnglish
Pages (from-to)1381-1388
Number of pages8
JournalAcademic Radiology
Volume20
Issue number11
DOIs
Publication statusPublished - Nov 2013
Externally publishedYes

Keywords

  • Breast cancer
  • Computer-assisted diagnosis
  • Image interpretation
  • Ultrasound

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