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

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6 Citations (Scopus)

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 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.

Original languageEnglish (US)
Pages (from-to)228-237
Number of pages10
JournalMedical Decision Making
Volume22
Issue number3
DOIs
StatePublished - 2002

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Mammography
Early Detection of Cancer
ROC Curve
Incidence
Sensitivity and Specificity
Population
Neoplasms
Normal Distribution
Breast
Theoretical Models
Research Personnel
Breast Neoplasms

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

@article{83edfebdbf42489e94568a959a993abe,
title = "Determining sensitivity of mammography from screening data, cancer incidence, and receiver-operating characteristic curve parameters",
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 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.",
keywords = "Breast cancer, Cancer detection rates, Mammography, Positive predictive value, Receiver-operating characteristic curves, Screening, Sensitivity, Specificity",
author = "Boone, {John M} and Lindfors, {Karen K} and Seibert, {J Anthony}",
year = "2002",
doi = "10.1177/02789X02022003005",
language = "English (US)",
volume = "22",
pages = "228--237",
journal = "Medical Decision Making",
issn = "0272-989X",
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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

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