Name
Title | ||
|---|---|---|
Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10278-009-9235-3 | J. Digital Imaging |
Keywords | Field | DocType |
Data mining,mammography,machine learning | Mammography,Confounding,Breast cancer,Computer science,Spiculated mass,Predictive value of tests,Malignancy,Odds ratio,Statistics,Logistic regression | Journal |
Volume | Issue | ISSN |
23 | 5 | 1618-727X |
Citations | PageRank | References |
5 | 0.57 | 2 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryan W. Woods | 1 | 14 | 2.44 |
| Louis Oliphant | 2 | 43 | 3.44 |
| Kazuhiko Shinki | 3 | 5 | 0.57 |
| David Page | 4 | 533 | 61.12 |
| Jude W. Shavlik | 5 | 3057 | 619.89 |
| Elizabeth S. Burnside | 6 | 199 | 27.84 |