### Abstract

Objectives. A mathematical model is presented that allows the computation of the sensitivity and specificity of breast screening based on receiver-operating characteristic (ROC) curve shape, the positive predictive value (PPV) of screening mammography, and the cancer incidence, f. Methods. The normal and cancer populations are modeled as normal distributions with independent means and standard deviations. The distributions are scaled such that the area of the normal population is equal to 1 - f and that of the cancer population is f. The PPV for screening mammography is used to determine the operating point on the ROC curve. Knowing this leads directly to the computation of sensitivity and specificity. The derivation is general and is applicable to both symmetrical and asymmetrical ROC curves. Results. For symmetric ROC curves and typical values for the PPV of mammography (about 8%) and cancer incidence (f = 0.003), an A_{z} value of 0.95 was required to achieve 63% sensitivity and an A_{z} value of 0.98 led to 86% sensitivity. Conclusion. A model was developed that should allow researchers to deduce sensitivity and specificity for screening mammography based on ROC curve measurements and using realistic values of PPV and f. This model allows A_{z} values to be related to the probability of breast cancer detection.

Original language | English (US) |
---|---|

Pages (from-to) | 228-237 |

Number of pages | 10 |

Journal | Medical Decision Making |

Volume | 22 |

Issue number | 3 |

DOIs | |

State | Published - 2002 |

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### Keywords

- Breast cancer
- Cancer detection rates
- Mammography
- Positive predictive value
- Receiver-operating characteristic curves
- Screening
- Sensitivity
- Specificity

### ASJC Scopus subject areas

- Public Health, Environmental and Occupational Health
- Health Informatics
- Health Information Management
- Nursing(all)

### Cite this

**Determining sensitivity of mammography from screening data, cancer incidence, and receiver-operating characteristic curve parameters.** / Boone, John M; Lindfors, Karen K; Seibert, J Anthony.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Determining sensitivity of mammography from screening data, cancer incidence, and receiver-operating characteristic curve parameters

AU - Boone, John M

AU - Lindfors, Karen K

AU - Seibert, J Anthony

PY - 2002

Y1 - 2002

N2 - Objectives. A mathematical model is presented that allows the computation of the sensitivity and specificity of breast screening based on receiver-operating characteristic (ROC) curve shape, the positive predictive value (PPV) of screening mammography, and the cancer incidence, f. Methods. The normal and cancer populations are modeled as normal distributions with independent means and standard deviations. The distributions are scaled such that the area of the normal population is equal to 1 - f and that of the cancer population is f. The PPV for screening mammography is used to determine the operating point on the ROC curve. Knowing this leads directly to the computation of sensitivity and specificity. The derivation is general and is applicable to both symmetrical and asymmetrical ROC curves. Results. For symmetric ROC curves and typical values for the PPV of mammography (about 8%) and cancer incidence (f = 0.003), an Az value of 0.95 was required to achieve 63% sensitivity and an Az value of 0.98 led to 86% sensitivity. Conclusion. A model was developed that should allow researchers to deduce sensitivity and specificity for screening mammography based on ROC curve measurements and using realistic values of PPV and f. This model allows Az values to be related to the probability of breast cancer detection.

AB - Objectives. A mathematical model is presented that allows the computation of the sensitivity and specificity of breast screening based on receiver-operating characteristic (ROC) curve shape, the positive predictive value (PPV) of screening mammography, and the cancer incidence, f. Methods. The normal and cancer populations are modeled as normal distributions with independent means and standard deviations. The distributions are scaled such that the area of the normal population is equal to 1 - f and that of the cancer population is f. The PPV for screening mammography is used to determine the operating point on the ROC curve. Knowing this leads directly to the computation of sensitivity and specificity. The derivation is general and is applicable to both symmetrical and asymmetrical ROC curves. Results. For symmetric ROC curves and typical values for the PPV of mammography (about 8%) and cancer incidence (f = 0.003), an Az value of 0.95 was required to achieve 63% sensitivity and an Az value of 0.98 led to 86% sensitivity. Conclusion. A model was developed that should allow researchers to deduce sensitivity and specificity for screening mammography based on ROC curve measurements and using realistic values of PPV and f. This model allows Az values to be related to the probability of breast cancer detection.

KW - Breast cancer

KW - Cancer detection rates

KW - Mammography

KW - Positive predictive value

KW - Receiver-operating characteristic curves

KW - Screening

KW - Sensitivity

KW - Specificity

UR - http://www.scopus.com/inward/record.url?scp=0035999545&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035999545&partnerID=8YFLogxK

U2 - 10.1177/02789X02022003005

DO - 10.1177/02789X02022003005

M3 - Article

C2 - 12058780

AN - SCOPUS:0035999545

VL - 22

SP - 228

EP - 237

JO - Medical Decision Making

JF - Medical Decision Making

SN - 0272-989X

IS - 3

ER -