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Validation of results from knowledge discovery: mass density as a predictor of breast cancer.

Publication ,  Journal Article
Woods, RW; Oliphant, L; Shinki, K; Page, D; Shavlik, J; Burnside, E
Published in: J Digit Imaging
October 2010

The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming (ILP) and conditional probabilities, and validate this association in an independent dataset. We ran our ILP algorithm on 62,219 mammographic abnormalities. We set the Aleph ILP system to generate 10,000 rules per malignant finding with a recall >5% and precision >25%. Aleph reported the best rule for each malignant finding. A total of 80 unique rules were learned. A radiologist reviewed all rules and identified potentially interesting rules. High breast mass density appeared in 24% of the learned rules. We confirmed each interesting rule by calculating the probability of malignancy given each mammographic descriptor. High mass density was the fifth highest ranked predictor. To validate the association between mass density and malignancy in an independent dataset, we collected data from 180 consecutive breast biopsies performed between 2005 and 2007. We created a logistic model with benign or malignant outcome as the dependent variable while controlling for potentially confounding factors. We calculated odds ratios based on dichomotized variables. In our logistic regression model, the independent predictors high breast mass density (OR 6.6, CI 2.5-17.6), irregular mass shape (OR 10.0, CI 3.4-29.5), spiculated mass margin (OR 20.4, CI 1.9-222.8), and subject age (β = 0.09, p < 0.0001) significantly predicted malignancy. Both ILP and conditional probabilities show that high breast mass density is an important adjunct predictor of malignancy, and this association is confirmed in an independent data set of prospectively collected mammographic findings.

Duke Scholars

Published In

J Digit Imaging

DOI

EISSN

1618-727X

Publication Date

October 2010

Volume

23

Issue

5

Start / End Page

554 / 561

Location

United States

Related Subject Headings

  • Registries
  • Radiographic Image Interpretation, Computer-Assisted
  • Prospective Studies
  • Predictive Value of Tests
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Logistic Models
  • Humans
  • Female
  • Densitometry
 

Citation

APA
Chicago
ICMJE
MLA
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Woods, R. W., Oliphant, L., Shinki, K., Page, D., Shavlik, J., & Burnside, E. (2010). Validation of results from knowledge discovery: mass density as a predictor of breast cancer. J Digit Imaging, 23(5), 554–561. https://doi.org/10.1007/s10278-009-9235-3
Woods, Ryan W., Louis Oliphant, Kazuhiko Shinki, David Page, Jude Shavlik, and Elizabeth Burnside. “Validation of results from knowledge discovery: mass density as a predictor of breast cancer.J Digit Imaging 23, no. 5 (October 2010): 554–61. https://doi.org/10.1007/s10278-009-9235-3.
Woods RW, Oliphant L, Shinki K, Page D, Shavlik J, Burnside E. Validation of results from knowledge discovery: mass density as a predictor of breast cancer. J Digit Imaging. 2010 Oct;23(5):554–61.
Woods, Ryan W., et al. “Validation of results from knowledge discovery: mass density as a predictor of breast cancer.J Digit Imaging, vol. 23, no. 5, Oct. 2010, pp. 554–61. Pubmed, doi:10.1007/s10278-009-9235-3.
Woods RW, Oliphant L, Shinki K, Page D, Shavlik J, Burnside E. Validation of results from knowledge discovery: mass density as a predictor of breast cancer. J Digit Imaging. 2010 Oct;23(5):554–561.
Journal cover image

Published In

J Digit Imaging

DOI

EISSN

1618-727X

Publication Date

October 2010

Volume

23

Issue

5

Start / End Page

554 / 561

Location

United States

Related Subject Headings

  • Registries
  • Radiographic Image Interpretation, Computer-Assisted
  • Prospective Studies
  • Predictive Value of Tests
  • Nuclear Medicine & Medical Imaging
  • Mammography
  • Logistic Models
  • Humans
  • Female
  • Densitometry