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

doi:10.1109/TMI.2002.808361

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 journal › Article › peer-review

72 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 A_z and confidence intervals in simple as well as paired data analyses. The combined diagnostic feature had an A_z 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 language	English
Pages (from-to)	170-177
Number of pages	8
Journal	IEEE Transactions on Medical Imaging
Volume	22
Issue number	2
DOIs	https://doi.org/10.1109/TMI.2002.808361
State	Published - 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.

Funding

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.

Funders	Funder number
National Institutes of Health (NIH)	CA52823-06S1
National Childhood Cancer Registry – National Cancer Institute	P01CA052823

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/TMI.2002.808361

Cite this

Gefen, S., Tretiak, O. J., Piccoli, C. W., Donohue, K. D., Petropulu, A. P., Shankar, P. M., Dumane, V. A., Huang, L., Kutay, M. A., Genis, V., Forsberg, F., Reid, J. M., & Goldberg, B. B. (2003). ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis. IEEE Transactions on Medical Imaging, 22(2), 170-177. https://doi.org/10.1109/TMI.2002.808361

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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.",

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AU - Kutay, M. Alper

AU - Genis, Vladimir

AU - Forsberg, Flemming

AU - Reid, John M.

AU - Goldberg, Barry B.

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ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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