Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer
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...
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| Published in: | Journal of digital imaging Vol. 23; no. 5; pp. 554 - 561 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Springer-Verlag
01.10.2010
Springer Nature B.V |
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| ISSN: | 0897-1889, 1618-727X, 1618-727X |
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| 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. |
|---|---|
| AbstractList | 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 ( beta =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. 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.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. 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. 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. 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.[PUBLICATION ABSTRACT] |
| Author | Shinki, Kazuhiko Woods, Ryan W. Oliphant, Louis Shavlik, Jude Page, David Burnside, Elizabeth |
| Author_xml | – sequence: 1 givenname: Ryan W. surname: Woods fullname: Woods, Ryan W. organization: Department of Radiology, University of Wisconsin School of Medicine and Public Health – sequence: 2 givenname: Louis surname: Oliphant fullname: Oliphant, Louis organization: Department of Biostatistics and Medical Informatics, University of Wisconsin – sequence: 3 givenname: Kazuhiko surname: Shinki fullname: Shinki, Kazuhiko organization: Department of Statistics, University of Wisconsin – sequence: 4 givenname: David surname: Page fullname: Page, David organization: Department of Biostatistics and Medical Informatics, University of Wisconsin – sequence: 5 givenname: Jude surname: Shavlik fullname: Shavlik, Jude organization: Department of Biostatistics and Medical Informatics, University of Wisconsin – sequence: 6 givenname: Elizabeth surname: Burnside fullname: Burnside, Elizabeth email: eburnside@uwhealth.org organization: Department of Radiology, University of Wisconsin School of Medicine and Public Health |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19760292$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_ejrad_2024_111614 crossref_primary_10_1007_s10278_013_9588_5 crossref_primary_10_3389_fgene_2022_822858 crossref_primary_10_3389_fonc_2023_1110657 |
| Cites_doi | 10.1148/radiology.196.3.7644649 10.2214/ajr.171.1.9648759 10.1109/51.853478 10.1148/radiology.184.2.1620838 10.1148/radiology.167.2.3282256 10.1016/j.jacr.2007.09.003 10.1148/radiology.192.2.8029411 10.2214/ajr.157.1.1646563 10.2214/AJR.07.2153 10.1148/radiology.219.2.r01ma11475 10.1007/978-3-662-04599-2_3 10.1148/radiology.165.1.3628796 10.1148/radiology.178.1.1984295 |
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| Copyright | Society for Imaging Informatics in Medicine 2009 Society for Imaging Informatics in Medicine 2010 |
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| References | DzeroskiSLavracNDzeroskiSLavracNIntroduction to inductive logic programmingRelational Data Mining2001BerlinSpringer4871 VizcainoIGadeaLAndreoLShort-term follow-up results in 795 nonpalpable probably benign lesions detected at screening mammographyRadiology200121924754831:STN:280:DC%2BD3M3mt1eitg%3D%3D11323475 American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS), 4th edition. Reston, VA, 2003 DangPAKalraMKBlakeMANatural language processing using online analytic processing for assessing recommendations in radiology reportsJ Am Coll Radiol20085319720410.1016/j.jacr.2007.09.00318312968 CiattoSCataliottiLDistanteVNonpalpable lesions detected with mammography: review of 512 consecutive casesRadiology19871651991021:STN:280:DyaL2szhvVKgtQ%3D%3D3628796 HallFMStorellaJMSilverstoneDZWyshakGNonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammographyRadiology198816723533581:STN:280:DyaL1c7ptVeltw%3D%3D3282256 JacksonVPDinesKABassettLWGoldRHReynoldsHEDiagnostic importance of the radiographic density of noncalcified breast masses: analysis of 91 lesionsAJR Am J Roentgenol1991157125281:STN:280:DyaK3M3mtl2nug%3D%3D1646563 Burnside ES, Davis J, Costa VS, et al: Knowledge discovery from structured mammography reports using inductive logic programming. AMIA Annu Symp Proc 96–100, 2005 LangMKirpekarNBurkleTLaumannSProkoschHUResults from data mining in a radiology department: the relevance of data qualityStud Health Technol Inform2007129Pt 157658017911782 LibermanLAbramsonAFSquiresFBGlassmanJRMorrisEADershawDDThe breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categoriesAJR Am J Roentgenol1998171135401:STN:280:DyaK1czhtFSqsQ%3D%3D9648759 CiosKJTeresinskaAKoniecznaSPotockaJSharmaSA knowledge discovery approach to diagnosing myocardial perfusionIEEE Eng Med Biol Mag200019417251:STN:280:DC%2BD3M%2FlvFWjsg%3D%3D10.1109/51.85347810916729 GrinsteadCMSnellJLIntroduction to Probability19972ProvidenceAmerican Mathematical Society BakerJAKornguthPJLoJYWillifordMEFloydCEJrBreast cancer: prediction with artificial neural network based on BI-RADS® standardized lexiconRadiology199519638178221:STN:280:DyaK2Mznt1GgtQ%3D%3D7644649 Srinivasan A: The Aleph Manual, 4th edition:http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html. Accessed May 24th, 2009. LehmanCDRutterCMEbyPRWhiteEBuistDSTaplinSHLesion and patient characteristics associated with malignancy after a probably benign finding on community practice mammographyAJR Am J Roentgenol2008190251151510.2214/AJR.07.215318212240 SicklesEANonpalpable, circumscribed, noncalcified solid breast masses: likelihood of malignancy based on lesion size and age of patientRadiology199419224394421:STN:280:DyaK2czgt1agsA%3D%3D8029411 VarasXLeborgneFLeborgneJHNonpalpable, probably benign lesions: role of follow-up mammographyRadiology199218424094141:STN:280:DyaK38zhvF2nsQ%3D%3D1620838 HelvieMAPennesDRRebnerMAdlerDDMammographic follow-up of low-suspicion lesions: compliance rate and diagnostic yieldRadiology199117811551581:STN:280:DyaK3M%2FnsV2luw%3D%3D1984295 EganRLBreast Imaging : Diagnosis and Morphology of Breast Diseases1988PhiladelphiaSaunders CD Lehman (9235_CR12) 2008; 190 9235_CR1 M Lang (9235_CR3) 2007; 129 KJ Cios (9235_CR4) 2000; 19 X Varas (9235_CR15) 1992; 184 MA Helvie (9235_CR11) 1991; 178 PA Dang (9235_CR2) 2008; 5 L Liberman (9235_CR13) 1998; 171 JA Baker (9235_CR8) 1995; 196 FM Hall (9235_CR10) 1988; 167 I Vizcaino (9235_CR16) 2001; 219 S Ciatto (9235_CR9) 1987; 165 RL Egan (9235_CR19) 1988 CM Grinstead (9235_CR6) 1997 EA Sickles (9235_CR14) 1994; 192 S Dzeroski (9235_CR5) 2001 9235_CR18 9235_CR7 VP Jackson (9235_CR17) 1991; 157 |
| References_xml | – reference: LehmanCDRutterCMEbyPRWhiteEBuistDSTaplinSHLesion and patient characteristics associated with malignancy after a probably benign finding on community practice mammographyAJR Am J Roentgenol2008190251151510.2214/AJR.07.215318212240 – reference: VizcainoIGadeaLAndreoLShort-term follow-up results in 795 nonpalpable probably benign lesions detected at screening mammographyRadiology200121924754831:STN:280:DC%2BD3M3mt1eitg%3D%3D11323475 – reference: JacksonVPDinesKABassettLWGoldRHReynoldsHEDiagnostic importance of the radiographic density of noncalcified breast masses: analysis of 91 lesionsAJR Am J Roentgenol1991157125281:STN:280:DyaK3M3mtl2nug%3D%3D1646563 – reference: HallFMStorellaJMSilverstoneDZWyshakGNonpalpable breast lesions: recommendations for biopsy based on suspicion of carcinoma at mammographyRadiology198816723533581:STN:280:DyaL1c7ptVeltw%3D%3D3282256 – reference: SicklesEANonpalpable, circumscribed, noncalcified solid breast masses: likelihood of malignancy based on lesion size and age of patientRadiology199419224394421:STN:280:DyaK2czgt1agsA%3D%3D8029411 – reference: GrinsteadCMSnellJLIntroduction to Probability19972ProvidenceAmerican Mathematical Society – reference: Burnside ES, Davis J, Costa VS, et al: Knowledge discovery from structured mammography reports using inductive logic programming. AMIA Annu Symp Proc 96–100, 2005 – reference: VarasXLeborgneFLeborgneJHNonpalpable, probably benign lesions: role of follow-up mammographyRadiology199218424094141:STN:280:DyaK38zhvF2nsQ%3D%3D1620838 – reference: CiosKJTeresinskaAKoniecznaSPotockaJSharmaSA knowledge discovery approach to diagnosing myocardial perfusionIEEE Eng Med Biol Mag200019417251:STN:280:DC%2BD3M%2FlvFWjsg%3D%3D10.1109/51.85347810916729 – reference: DzeroskiSLavracNDzeroskiSLavracNIntroduction to inductive logic programmingRelational Data Mining2001BerlinSpringer4871 – reference: American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS), 4th edition. Reston, VA, 2003 – reference: Srinivasan A: The Aleph Manual, 4th edition:http://www.comlab.ox.ac.uk/activities/machinelearning/Aleph/aleph_toc.html. Accessed May 24th, 2009. – reference: DangPAKalraMKBlakeMANatural language processing using online analytic processing for assessing recommendations in radiology reportsJ Am Coll Radiol20085319720410.1016/j.jacr.2007.09.00318312968 – reference: HelvieMAPennesDRRebnerMAdlerDDMammographic follow-up of low-suspicion lesions: compliance rate and diagnostic yieldRadiology199117811551581:STN:280:DyaK3M%2FnsV2luw%3D%3D1984295 – reference: LibermanLAbramsonAFSquiresFBGlassmanJRMorrisEADershawDDThe breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categoriesAJR Am J Roentgenol1998171135401:STN:280:DyaK1czhtFSqsQ%3D%3D9648759 – reference: LangMKirpekarNBurkleTLaumannSProkoschHUResults from data mining in a radiology department: the relevance of data qualityStud Health Technol Inform2007129Pt 157658017911782 – reference: EganRLBreast Imaging : Diagnosis and Morphology of Breast Diseases1988PhiladelphiaSaunders – reference: CiattoSCataliottiLDistanteVNonpalpable lesions detected with mammography: review of 512 consecutive casesRadiology19871651991021:STN:280:DyaL2szhvVKgtQ%3D%3D3628796 – reference: BakerJAKornguthPJLoJYWillifordMEFloydCEJrBreast cancer: prediction with artificial neural network based on BI-RADS® standardized lexiconRadiology199519638178221:STN:280:DyaK2Mznt1GgtQ%3D%3D7644649 – volume: 196 start-page: 817 issue: 3 year: 1995 ident: 9235_CR8 publication-title: Radiology doi: 10.1148/radiology.196.3.7644649 – volume: 171 start-page: 35 issue: 1 year: 1998 ident: 9235_CR13 publication-title: AJR Am J Roentgenol doi: 10.2214/ajr.171.1.9648759 – volume: 19 start-page: 17 issue: 4 year: 2000 ident: 9235_CR4 publication-title: IEEE Eng Med Biol Mag doi: 10.1109/51.853478 – volume: 184 start-page: 409 issue: 2 year: 1992 ident: 9235_CR15 publication-title: Radiology doi: 10.1148/radiology.184.2.1620838 – ident: 9235_CR1 – volume: 167 start-page: 353 issue: 2 year: 1988 ident: 9235_CR10 publication-title: Radiology doi: 10.1148/radiology.167.2.3282256 – volume: 5 start-page: 197 issue: 3 year: 2008 ident: 9235_CR2 publication-title: J Am Coll Radiol doi: 10.1016/j.jacr.2007.09.003 – volume: 192 start-page: 439 issue: 2 year: 1994 ident: 9235_CR14 publication-title: Radiology doi: 10.1148/radiology.192.2.8029411 – volume-title: Introduction to Probability year: 1997 ident: 9235_CR6 – volume: 157 start-page: 25 issue: 1 year: 1991 ident: 9235_CR17 publication-title: AJR Am J Roentgenol doi: 10.2214/ajr.157.1.1646563 – ident: 9235_CR18 – volume: 190 start-page: 511 issue: 2 year: 2008 ident: 9235_CR12 publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.07.2153 – volume: 219 start-page: 475 issue: 2 year: 2001 ident: 9235_CR16 publication-title: Radiology doi: 10.1148/radiology.219.2.r01ma11475 – ident: 9235_CR7 – start-page: 48 volume-title: Relational Data Mining year: 2001 ident: 9235_CR5 doi: 10.1007/978-3-662-04599-2_3 – volume: 129 start-page: 576 issue: Pt 1 year: 2007 ident: 9235_CR3 publication-title: Stud Health Technol Inform – volume: 165 start-page: 99 issue: 1 year: 1987 ident: 9235_CR9 publication-title: Radiology doi: 10.1148/radiology.165.1.3628796 – volume: 178 start-page: 155 issue: 1 year: 1991 ident: 9235_CR11 publication-title: Radiology doi: 10.1148/radiology.178.1.1984295 – volume-title: Breast Imaging : Diagnosis and Morphology of Breast Diseases year: 1988 ident: 9235_CR19 |
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| Snippet | The purpose of our study is to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming... |
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| SubjectTerms | Abnormalities Algorithms Biopsy Breast Breast - pathology Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Densitometry - methods Density Dependent variables Female Humans Imaging Logic programming Logistic Models Logistics Mammography Mathematical models Medicine Medicine & Public Health Predictive Value of Tests Prospective Studies Radiographic Image Interpretation, Computer-Assisted - methods Radiology Registries Regression |
| Title | Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer |
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