-
1
-
-
84885424249
-
-
American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS) atlas, Reston, Va., 2003
-
American College of Radiology: Breast Imaging Reporting and Data System (BI-RADS) atlas, Reston, Va., 2003
-
-
-
-
2
-
-
0031116746
-
Mammography interpretation: The BI-RADS method
-
9105186
-
D'Orsi C, Kopans D: Mammography interpretation: the BI-RADS method. American Family Physician 55:1548-1550, 1997
-
(1997)
American Family Physician
, vol.55
, pp. 1548-1550
-
-
D'Orsi, C.1
Kopans, D.2
-
3
-
-
71549159121
-
The ACR BI-RADS experience: Learning from history
-
10.1016/j.jacr.2009.07.023
-
Burnside ES, et al: The ACR BI-RADS experience: learning from history. J American College of Radiology 6:851-860, 2009
-
(2009)
J American College of Radiology
, vol.6
, pp. 851-860
-
-
Burnside, E.S.1
-
4
-
-
0031830778
-
The Breast Imaging Reporting and Data System: Positive predictive value of mammographic features and final assessment categories
-
9648759 10.2214/ajr.171.1.9648759 1:STN:280:DyaK1czhtFSqsQ%3D%3D
-
Liberman L, Abramson A, Squires F, Glassman J, Morris E, Dershaw D: The Breast Imaging Reporting and Data System: positive predictive value of mammographic features and final assessment categories. AJR Am J Roentgenol 171:35-40, 1998
-
(1998)
AJR Am J Roentgenol
, vol.171
, pp. 35-40
-
-
Liberman, L.1
Abramson, A.2
Squires, F.3
Glassman, J.4
Morris, E.5
Dershaw, D.6
-
5
-
-
0026014076
-
Enhancing and evaluating diagnostic accuracy
-
10.1177/0272989X9101100102 1:STN:280:DyaK3M3jvVeqtQ%3D%3D
-
Swets J, Getty D, Pickett R, D'Orsi C, Seltzer S, McNeil B: Enhancing and evaluating diagnostic accuracy. J Medical Decision Making 11:9-18, 1991
-
(1991)
J Medical Decision Making
, vol.11
, pp. 9-18
-
-
Swets, J.1
Getty, D.2
Pickett, R.3
D'Orsi, C.4
Seltzer, S.5
McNeil, B.6
-
6
-
-
0031662313
-
Level of suspicion of a mammographic lesion: Use of features defined by BI-RADS lexicon and correlation with large-core breast biopsy
-
9709675 1:STN:280:DyaK1cznsFCjtA%3D%3D
-
Berube M, Curpen B, Ugolini P, Lalonde L, Ouimet-Oliva D: Level of suspicion of a mammographic lesion: use of features defined by BI-RADS lexicon and correlation with large-core breast biopsy. Can Assoc Radiol J 49:223-228, 1998
-
(1998)
Can Assoc Radiol J
, vol.49
, pp. 223-228
-
-
Berube, M.1
Curpen, B.2
Ugolini, P.3
Lalonde, L.4
Ouimet-Oliva, D.5
-
7
-
-
1842607583
-
Mammographic features and correlation with biopsy findings using 11-gauge stereotactic vacuum-assisted breast biopsy (SVABB)
-
Mendez A, Cabanillas F, Echenique M, Malekshamran K, Perez I, Ramos E: Mammographic features and correlation with biopsy findings using 11-gauge stereotactic vacuum-assisted breast biopsy (SVABB). Annals of Oncology 14:450-454, 2003
-
(2003)
Annals of Oncology
, vol.14
, pp. 450-454
-
-
Mendez, A.1
Cabanillas, F.2
Echenique, M.3
Malekshamran, K.4
Perez, I.5
Ramos, E.6
-
8
-
-
62649137776
-
Positive predictive value of specific mammographic findings according to reader and patient variables
-
19164116 10.1148/radiol.2503080541
-
Venkatesan A, Chu P, Kerlikowske K, Sickles E, Smith-Bindman R: Positive predictive value of specific mammographic findings according to reader and patient variables. Radiology 250:648-657, 2009
-
(2009)
Radiology
, vol.250
, pp. 648-657
-
-
Venkatesan, A.1
Chu, P.2
Kerlikowske, K.3
Sickles, E.4
Smith-Bindman, R.5
-
9
-
-
77950117039
-
Comparison of logistic regression and artificial neural network models in breast cancer risk estimation
-
19901087 10.1148/rg.301095057
-
Ayer T, Chhatwal J, Alagoz O, Kahn Jr, CE, Wood R, Burnside ES: Comparison of logistic regression and artificial neural network models in breast cancer risk estimation. RadioGraphics 30:13-22, 2010
-
(2010)
RadioGraphics
, vol.30
, pp. 13-22
-
-
Ayer, T.1
Chhatwal, J.2
Alagoz, O.3
Kahn, Jr.C.E.4
Wood, R.5
Burnside, E.S.6
-
10
-
-
63849134851
-
A logistic regression model based on the national mammography database format to aid breast cancer diagnosis
-
19304723 10.2214/AJR.07.3345
-
Chhatwal J, Alagoz O, Lindstrom MJ, Kahn Jr, CE, Shaffer KA, Burnside ES: A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. AJR Am J Roentgenol 192:1117-1127, 2009
-
(2009)
AJR Am J Roentgenol
, vol.192
, pp. 1117-1127
-
-
Chhatwal, J.1
Alagoz, O.2
Lindstrom, M.J.3
Kahn, Jr.C.E.4
Shaffer, K.A.5
Burnside, E.S.6
-
11
-
-
0043126911
-
Logistic regression and artificial neural network classification models: A methodology review
-
12968784 10.1016/S1532-0464(03)00034-0
-
Dreiseitl S, Ohno-Machado L: Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352-359, 2002
-
(2002)
J Biomed Inform
, vol.35
, pp. 352-359
-
-
Dreiseitl, S.1
Ohno-Machado, L.2
-
12
-
-
0030297904
-
Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
-
8892489 10.1016/S0895-4356(96)00002-9 1:STN:280:DyaK2s%2FkslGluw%3D%3D
-
Tu J: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 49:1225-1231, 1996
-
(1996)
J Clin Epidemiol
, vol.49
, pp. 1225-1231
-
-
Tu, J.1
-
14
-
-
0038713719
-
Mutual information as an index of diagnostic test performance
-
12874659 1:STN:280:DC%2BD3szkt1ejtw%3D%3D
-
Benish W: Mutual information as an index of diagnostic test performance. Methods of Information in Medicine 42:260-264, 2003
-
(2003)
Methods of Information in Medicine
, vol.42
, pp. 260-264
-
-
Benish, W.1
-
15
-
-
77955933965
-
Breast tissue composition and susceptibility to breast cancer
-
20616353 10.1093/jnci/djq239
-
Boyd N, Martin L, Bronskill M, Yaffe M, Duric N, Minkin S: Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 102:1224-1237, 2010
-
(2010)
J Natl Cancer Inst
, vol.102
, pp. 1224-1237
-
-
Boyd, N.1
Martin, L.2
Bronskill, M.3
Yaffe, M.4
Duric, N.5
Minkin, S.6
-
16
-
-
26444453928
-
Mammographic breast density as an intermediate phenotype for breast cancer
-
16198986 10.1016/S1470-2045(05)70390-9
-
Boyd N, et al: Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol 6:798-808, 2005
-
(2005)
Lancet Oncol
, vol.6
, pp. 798-808
-
-
Boyd, N.1
-
17
-
-
76149128688
-
Family history, mammographic density, and risk of breast cancer
-
20142244 10.1158/1055-9965.EPI-09-0881
-
Martin L, et al: Family history, mammographic density, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev 19:456-463, 2010
-
(2010)
Cancer Epidemiol Biomarkers Prev
, vol.19
, pp. 456-463
-
-
Martin, L.1
-
18
-
-
0017067087
-
Breast patterns as an index of risk for developing breast cancer
-
179369 10.2214/ajr.126.6.1130 1:STN:280:DyaE283gtlGrsw%3D%3D
-
Wolfe J: Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol 126:1130-1137, 1976
-
(1976)
AJR Am J Roentgenol
, vol.126
, pp. 1130-1137
-
-
Wolfe, J.1
-
19
-
-
0037418128
-
Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography
-
12558355 10.7326/0003-4819-138-3-200302040-00008
-
Carney P, et al: Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med 138:168-175, 2003
-
(2003)
Ann Intern Med
, vol.138
, pp. 168-175
-
-
Carney, P.1
-
20
-
-
0034608772
-
Breast density as a predictor of mammographic detection: Comparison of interval- and screen-detected cancers
-
10880551 10.1093/jnci/92.13.1081 1:STN:280:DC%2BD3cvivFyrtA%3D%3D
-
Mandelson M, et al: Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst 92:1081-1087, 2000
-
(2000)
J Natl Cancer Inst
, vol.92
, pp. 1081-1087
-
-
Mandelson, M.1
-
21
-
-
0032738807
-
Wisconsin Cancer Reporting System: A population-based registry
-
10555470 1:STN:280:DC%2BD3c%2FitlWksg%3D%3D
-
Foote M: Wisconsin Cancer Reporting System: a population-based registry. Wisconsin Medical Journal 98:17-18, 1999
-
(1999)
Wisconsin Medical Journal
, vol.98
, pp. 17-18
-
-
Foote, M.1
-
22
-
-
21244467519
-
On discriminative Bayesian network classifiers and logistic regression
-
Roos T, Wettig H, Grunwald P, Myllymaki P, Tirri H: On discriminative Bayesian network classifiers and logistic regression. Machine Learning 59:267-296, 2005
-
(2005)
Machine Learning
, vol.59
, pp. 267-296
-
-
Roos, T.1
Wettig, H.2
Grunwald, P.3
Myllymaki, P.4
Tirri, H.5
-
23
-
-
0031269184
-
On the optimality of the simple Bayesian classifier under zero-one loss
-
10.1023/A:1007413511361
-
Domingos P, Pazzani M: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29:103-130, 1997
-
(1997)
Machine Learning
, vol.29
, pp. 103-130
-
-
Domingos, P.1
Pazzani, M.2
-
24
-
-
76749092270
-
The Weka data mining software: An update
-
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten I: The Weka data mining software: an update. SIGKDD Explorations(11), 2009
-
(2009)
SIGKDD Explorations
, Issue.11
-
-
Hall, M.1
Frank, E.2
Holmes, G.3
Pfahringer, B.4
Reutemann, P.5
Witten, I.6
-
26
-
-
84885424498
-
A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features
-
Ince R, Mazzoni A, Bartels A, Logothetis N, Panzeri S: A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features. J Neuroscience Methods, 2011
-
(2011)
J Neuroscience Methods
-
-
Ince, R.1
Mazzoni, A.2
Bartels, A.3
Logothetis, N.4
Panzeri, S.5
-
27
-
-
0023710206
-
Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach
-
3203132 10.2307/2531595 1:STN:280:DyaL1M%2Fns12ksQ%3D%3D
-
DeLong E, DeLong D, Clarke-Pearson D: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837-845, 1988
-
(1988)
Biometrics
, vol.44
, pp. 837-845
-
-
Delong, E.1
Delong, D.2
Clarke-Pearson, D.3
-
28
-
-
33745561205
-
An Introduction to variable and feature selection
-
Guyon I, Elisseeff A: An Introduction to variable and feature selection. J Machine Learning Research 3:1157-1182, 2003
-
(2003)
J Machine Learning Research
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
29
-
-
0035661275
-
Application of the mutual information criterion for feature selection in compuet-aided diagnosis
-
11797941 10.1118/1.1418724 1:STN:280:DC%2BD38%2FntFOhug%3D%3D
-
Tourassi G, Frederick E, Markey M, Floyd C: Application of the mutual information criterion for feature selection in compuet-aided diagnosis. Medical Physics 28:2394-2402, 2001
-
(2001)
Medical Physics
, vol.28
, pp. 2394-2402
-
-
Tourassi, G.1
Frederick, E.2
Markey, M.3
Floyd, C.4
-
31
-
-
77957869898
-
Validation of results from knowledge discovery: Mass denisty as a predictor of breast cancer
-
10.1007/s10278-009-9235-3
-
Woods R, Oliphant L, Shinki K, Page CD, Shavlik J, Burnside E: Validation of results from knowledge discovery: mass denisty as a predictor of breast cancer. J Digital Imaging 23:554-561, 2010
-
(2010)
J Digital Imaging
, vol.23
, pp. 554-561
-
-
Woods, R.1
Oliphant, L.2
Shinki, K.3
Page, C.D.4
Shavlik, J.5
Burnside, E.6
-
32
-
-
79952354871
-
The mammographic density of a mass is a significant predictor of breast cancer
-
21177388 10.1148/radiol.10100328
-
Woods R, Sisney G, Salkowski L, Shinki K, Lin Y, Burnside E: The mammographic density of a mass is a significant predictor of breast cancer. Radiology 258:417-425, 2011
-
(2011)
Radiology
, vol.258
, pp. 417-425
-
-
Woods, R.1
Sisney, G.2
Salkowski, L.3
Shinki, K.4
Lin, Y.5
Burnside, E.6
-
33
-
-
77952590681
-
On the feature selection criterion based on an approximation of multimensional mutual information
-
10.1109/TPAMI.2010.62
-
Balagani K, Phoha V: On the feature selection criterion based on an approximation of multimensional mutual information. IEEE Trans Pattern Analysis and Machine Intelligence 32:1342-1343, 2010
-
(2010)
IEEE Trans Pattern Analysis and Machine Intelligence
, vol.32
, pp. 1342-1343
-
-
Balagani, K.1
Phoha, V.2
-
34
-
-
0028468293
-
Using mutual information for selecting features in supervised neural net learning
-
10.1109/72.298224 1:STN:280:DC%2BD1c7gvFarsQ%3D%3D
-
Battiti R: Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Networks 5:537-550, 1994
-
(1994)
IEEE Trans Neural Networks
, vol.5
, pp. 537-550
-
-
Battiti, R.1
-
35
-
-
24344458137
-
Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
-
10.1109/TPAMI.2005.159
-
Peng H, Long F, Ding C: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Analysis and Machine Intelligence 27:1226-1238, 2005
-
(2005)
IEEE Trans Pattern Analysis and Machine Intelligence
, vol.27
, pp. 1226-1238
-
-
Peng, H.1
Long, F.2
Ding, C.3
|