ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis

Smadar Gefen, Oleh J. Tretiak, Catherine W. Piccoli, Kevin D. Donohue, Athina P. Petropulu, P. Mohana Shankar, Vishruta A. Dumane, Lexun Huang, M. Alper Kutay, Vladimir Genis, Flemming Forsberg, John M. Reid, Barry B. Goldberg

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

Breast cancer diagnosis through ultrasound tissue characterization was studied using receiver operating characteristic (ROC) analysis of combinations of acoustic features, patient age, and radiological findings. A features fusion method was devised that operates even if only partial diagnostic data are available. The ROC methodology uses ordinal dominance theory and bootstrap resampling to evaluate Az and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an Az of 0.96 with a confidence interval of [0.93, 0.99] at a significance level of 0.05. The combined features show statistically significant improvement over prebiopsy radiological findings. These results indicate that ultrasound tissue characterization, in combination with patient record and clinical findings, may greatly reduce the need to perform biopsies of benign breast lesions.

Original languageEnglish
Pages (from-to)170-177
Number of pages8
JournalIEEE Transactions on Medical Imaging
Volume22
Issue number2
DOIs
StatePublished - Feb 2003

Bibliographical note

Funding Information:
Manuscript received December 15, 2001; revised October 24, 2002. This work was supported by the National Institutes of Health (NIH) under Grant CA52823-06S1. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was M. Giger. Asterisk indicates corresponding author.

Keywords

  • Bootstrap
  • Breast ultrasonic imaging
  • ROC analysis
  • Tissue characterization

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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