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

Producción científica: Article › revisión exhaustiva

75 Citas (Scopus)

Resumen

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.

Idioma original	English
Páginas (desde-hasta)	170-177
Número de páginas	8
Publicación	IEEE Transactions on Medical Imaging
Volumen	22
N.º	2
DOI	https://doi.org/10.1109/TMI.2002.808361
Estado	Published - feb 2003

Nota bibliográfica

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.

Financiación

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.

Financiadores	Número del financiador
National Institutes of Health (NIH)	CA52823-06S1
National Childhood Cancer Registry – National Cancer Institute	P01CA052823

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Good health and well being

ASJC Scopus subject areas

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

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10.1109/TMI.2002.808361

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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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title = "ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis",

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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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.",

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AU - Gefen, Smadar

AU - Tretiak, Oleh J.

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AU - Donohue, Kevin D.

AU - Petropulu, Athina P.

AU - Shankar, P. Mohana

AU - Dumane, Vishruta A.

AU - Huang, Lexun

AU - Kutay, M. Alper

AU - Genis, Vladimir

AU - Forsberg, Flemming

AU - Reid, John M.

AU - Goldberg, Barry B.

N1 - 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.

PY - 2003/2

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N2 - 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.

AB - 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.

KW - Bootstrap

KW - Breast ultrasonic imaging

KW - ROC analysis

KW - Tissue characterization

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JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

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ROC analysis of ultrasound tissue characterization classifiers for breast cancer diagnosis

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ASJC Scopus subject areas

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