Literature DB >> 9718500

A breast density index for digital mammograms based on radiologists' ranking.

J M Boone1, K K Lindfors, C S Beatty, J A Seibert.   

Abstract

The purpose of this study was to develop and evaluate a computerized method of calculating a breast density index (BDI) from digitized mammograms that was designed specifically to model radiologists' perception of breast density. A set of 153 pairs of digitized mammograms (cranio-caudal, CC, and mediolateral oblique, MLO, views) were acquired and preprocessed to reduce detector biases. The sets of mammograms were ordered on an ordinal scale (a scale based only on relative rank-ordering) by two radiologists, and a cardinal (an absolute numerical score) BDI value was calculated from the ordinal ranks. The images were also assigned cardinal BDI values by the radiologists in a subsequent session. Six mathematical features (including fractal dimension and others) were calculated from the digital mammograms, and were used in conjunction with single value decomposition and multiple linear regression to calculate a computerized BDI. The linear correlation coefficient between different ordinal ranking sessions were as follows: intraradiologist intraprojection (CC/CC): r = 0.978; intraradiologist interprojection (CC/MLO): r = 0.960; and interradiologist intraprojection (CC/CC): r = 0.968. A separate breast density index was derived from three separate ordinal rankings by one radiologist (two with CC views, one with the MLO view). The computer derived BDI had a correlation coefficient (r) of 0.907 with the radiologists' ordinal BDI. A comparison between radiologists using a cardinal scoring system (which is closest to how radiologists actually evaluate breast density) showed r = 0.914. A breast density index calculated by a computer but modeled after radiologist perception of breast density may be valuable in objectively measuring breast density. Such a metric may prove valuable in numerous areas, including breast cancer risk assessment and in evaluating screening techniques specifically designed to improve imaging of the dense breast.

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Year:  1998        PMID: 9718500      PMCID: PMC3453202          DOI: 10.1007/bf03168733

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  48 in total

1.  Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study.

Authors:  J N Wolfe; A F Saftlas; M Salane
Journal:  AJR Am J Roentgenol       Date:  1987-06       Impact factor: 3.959

2.  Ultrasound of the breast in the symptomatic and X-ray dense breast.

Authors:  P B Guyer; K C Dewbury
Journal:  Clin Radiol       Date:  1985-01       Impact factor: 2.350

3.  Breast parenchymal patterns and their relationship to risk for having or developing carcinoma.

Authors:  J N Wolfe; S Albert; S Belle; M Salane
Journal:  Radiol Clin North Am       Date:  1983-03       Impact factor: 2.303

4.  Breast patterns as an index of risk for developing breast cancer.

Authors:  J N Wolfe
Journal:  AJR Am J Roentgenol       Date:  1976-06       Impact factor: 3.959

5.  Risk of developing breast cancer determined by mammography.

Authors:  J N Wolfe
Journal:  Prog Clin Biol Res       Date:  1977

6.  Risk for breast cancer development determined by mammographic parenchymal pattern.

Authors:  J N Wolfe
Journal:  Cancer       Date:  1976-05       Impact factor: 6.860

7.  Target ultrasonic mammography. An additional diagnostic tool for the detection of breast cancer.

Authors:  S J Liem
Journal:  Diagn Imaging Clin Med       Date:  1985

8.  Changes in Wolfe mammographic patterns with aging.

Authors:  D Flook; R W Gilhome; J Harman; I H Gravelle; D J Webster
Journal:  Br J Radiol       Date:  1987-05       Impact factor: 3.039

9.  Magnetic resonance imaging in the diagnosis of breast disease.

Authors:  N Dash; A R Lupetin; R H Daffner; Z L Deeb; R J Sefczek; R L Schapiro
Journal:  AJR Am J Roentgenol       Date:  1986-01       Impact factor: 3.959

10.  Breast parenchymal patterns: analysis of 332 incident breast carcinomas.

Authors:  J N Wolfe; S Albert; S Belle; M Salane
Journal:  AJR Am J Roentgenol       Date:  1982-01       Impact factor: 3.959

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