Exploring the negative likelihood ratio and how it can be used to minimize false-positives in breast imaging

Wei T. Yang; Jay R. Parikh; A. Thomas Stavros; Pam Otto; Greg Maislin

doi:10.2214/AJR.17.18774

Exploring the negative likelihood ratio and how it can be used to minimize false-positives in breast imaging

Wei T. Yang, Jay R. Parikh, A. Thomas Stavros, Pam Otto, Greg Maislin

Research output: Contribution to journal › Review article › peer-review

9 Scopus citations

Abstract

OBJECTIVE. This article describes the defnition and proposed utilization of negative likelihood ratios (NLRs) as statistical parameters in breast imaging. Examples with calculations are provided using BI-RADS category 4 subcategories. CONCLUSION. By auditing individual performance early and often against American College of Radiology benchmark positive predictive value ranges for the BI-RADS category 4 subcategories, and by fully understanding NLRs and their application in breast imaging, radiologists can minimize false-positive fndings and unnecessary biopsies.

Original language	English (US)
Pages (from-to)	301-306
Number of pages	6
Journal	American Journal of Roentgenology
Volume	210
Issue number	2
DOIs	https://doi.org/10.2214/AJR.17.18774
State	Published - Feb 2018

Keywords

BI-RADS
Bayes theorem
Breast imaging biopsy
Negative likelihood ratio
Posttest probability
Pretest probability

ASJC Scopus subject areas

Radiology Nuclear Medicine and imaging

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10.2214/AJR.17.18774

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title = "Exploring the negative likelihood ratio and how it can be used to minimize false-positives in breast imaging",

abstract = "OBJECTIVE. This article describes the defnition and proposed utilization of negative likelihood ratios (NLRs) as statistical parameters in breast imaging. Examples with calculations are provided using BI-RADS category 4 subcategories. CONCLUSION. By auditing individual performance early and often against American College of Radiology benchmark positive predictive value ranges for the BI-RADS category 4 subcategories, and by fully understanding NLRs and their application in breast imaging, radiologists can minimize false-positive fndings and unnecessary biopsies.",

keywords = "BI-RADS, Bayes theorem, Breast imaging biopsy, Negative likelihood ratio, Posttest probability, Pretest probability",

author = "Yang, {Wei T.} and Parikh, {Jay R.} and {Thomas Stavros}, A. and Pam Otto and Greg Maislin",

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AU - Yang, Wei T.

AU - Parikh, Jay R.

AU - Thomas Stavros, A.

AU - Otto, Pam

AU - Maislin, Greg

N1 - Publisher Copyright: © American Roentgen Ray Society.

PY - 2018/2

Y1 - 2018/2

N2 - OBJECTIVE. This article describes the defnition and proposed utilization of negative likelihood ratios (NLRs) as statistical parameters in breast imaging. Examples with calculations are provided using BI-RADS category 4 subcategories. CONCLUSION. By auditing individual performance early and often against American College of Radiology benchmark positive predictive value ranges for the BI-RADS category 4 subcategories, and by fully understanding NLRs and their application in breast imaging, radiologists can minimize false-positive fndings and unnecessary biopsies.

AB - OBJECTIVE. This article describes the defnition and proposed utilization of negative likelihood ratios (NLRs) as statistical parameters in breast imaging. Examples with calculations are provided using BI-RADS category 4 subcategories. CONCLUSION. By auditing individual performance early and often against American College of Radiology benchmark positive predictive value ranges for the BI-RADS category 4 subcategories, and by fully understanding NLRs and their application in breast imaging, radiologists can minimize false-positive fndings and unnecessary biopsies.

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KW - Bayes theorem

KW - Breast imaging biopsy

KW - Negative likelihood ratio

KW - Posttest probability

KW - Pretest probability

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Exploring the negative likelihood ratio and how it can be used to minimize false-positives in breast imaging

Abstract

Keywords

ASJC Scopus subject areas

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