1
|
Kuhl CK, Schrading S, Bieling HB,
Wardelmann E, Leutner CC, Koenig R, Kuhn W and Schild HH: MRI for
diagnosis of pure ductal carcinoma in situ: A prospective
observational study. Lancet. 370:485–492. 2007. View Article : Google Scholar : PubMed/NCBI
|
2
|
Chang YC, Huang YH, Huang CS, Chang PK,
Chen JH and Chang RF: Classification of breast mass lesions using
model-based analysis of the characteristic kinetic curve derived
from fuzzy c-means clustering. Magn Reson Imaging. 30:312–322.
2012. View Article : Google Scholar : PubMed/NCBI
|
3
|
Vag T, Baltzer PA, Dietzel M, Zoubi R,
Gajda M, Camara O and Kaiser WA: Kinetic analysis of lesions
without mass effect on breast MRI using manual and
computer-assisted methods. Eur Radiol. 21:893–898. 2011. View Article : Google Scholar : PubMed/NCBI
|
4
|
Böttcher J, Renz DM, Zahm DM, Pfeil A,
Fallenberg EM, Streitparth F, Maurer MH, Hamm B and Engelken FJ:
Response to neoadjuvant treatment of invasive ductal breast
carcinomas including outcome evaluation: MRI analysis by an
automatic CAD system in comparison to visual evaluation. Acta
Oncol. 53:759–768. 2014. View Article : Google Scholar : PubMed/NCBI
|
5
|
Renz DM, Böttcher J, Baltzer PA, Dietzel
M, Vag T, Gajda M, Camara O, Runnebaum IB and Kaiser WA: The
contralateral synchronous breast carcinoma: A comparison of
histology, localization, and magnetic resonance imaging
characteristics with the primary index cancer. Breast Cancer Res
Treat. 120:449–459. 2010. View Article : Google Scholar : PubMed/NCBI
|
6
|
Renz DM, Diekmann F, Schmitzberger FF,
Pietsch H, Fallenberg EM, Durmus T, Huppertz A, Böttcher J, Bick U,
Hamm B, et al: Pharmacokinetic approach for dynamic breast MRI to
indicate signal intensity time curves of benign and malignant
lesions by using the tumor flow residence time. Invest Radiol.
48:69–78. 2013. View Article : Google Scholar : PubMed/NCBI
|
7
|
Li C, Xu C, Gui C and Fox MD: Distance
regularized level set evolution and its application to image
segmentation. IEEE Trans Image Process. 19:3243–3254. 2010.
View Article : Google Scholar : PubMed/NCBI
|
8
|
Sun L, Chen G, Zhou Y, Zhang L, Jin Z, Liu
W, Wu G, Jin F, Li K and Chen B: Clinical significance of MSKCC
nomogram on guiding the application of touch imprint cytology and
frozen section in intraoperative assessment of breast sentinel
lymph nodes. Oncotarget. 8:78105–78112. 2017.PubMed/NCBI
|
9
|
Croshaw R, Shapiro-Wright H, Svensson E,
Erb K and Julian T: Accuracy of clinical examination, digital
mammogram, ultrasound, and MRI in determining postneoadjuvant
pathologic tumor response in operable breast cancer patients. Ann
Surg Oncol. 18:3160–3163. 2011. View Article : Google Scholar : PubMed/NCBI
|
10
|
Sardanelli F, Podo F, Santoro F, Manoukian
S, Bergonzi S, Trecate G, Vergnaghi D, Federico M, Cortesi L,
Corcione S, et al: High Breast Cancer Risk Italian 1 (HIBCRIT-1)
Study. Multicenter surveillance of women at high genetic breast
cancer risk using mammography, ultrasonography, and
contrast-enhanced magnetic resonance imaging (the high breast
cancer risk italian 1 study): Final results. Invest Radiol.
46:94–105. 2011. View Article : Google Scholar : PubMed/NCBI
|
11
|
Lehman CD, Gatsonis C, Kuhl CK, Hendrick
RE, Pisano ED, Hanna L, Peacock S, Smazal SF, Maki DD, Julian TB,
et al; ACRIN Trial 6667 Investigators Group, . MRI evaluation of
the contralateral breast in women with recently diagnosed breast
cancer. N Engl J Med. 356:1295–1303. 2007. View Article : Google Scholar : PubMed/NCBI
|
12
|
Pediconi F, Miglio E, Telesca M, Luciani
ML, Kirchin MA, Passariello R and Catalano C: Effect of
preoperative breast magnetic resonance imaging on surgical decision
making and cancer recurrence rates. Invest Radiol. 47:128–135.
2012. View Article : Google Scholar : PubMed/NCBI
|
13
|
Johansen R, Jensen LR, Rydland J, Goa PE,
Kvistad KA, Bathen TF, Axelson DE, Lundgren S and Gribbestad IS:
Predicting survival and early clinical response to primary
chemotherapy for patients with locally advanced breast cancer using
DCE-MRI. J Magn Reson Imaging. 29:1300–1307. 2009. View Article : Google Scholar : PubMed/NCBI
|
14
|
Abramson RG, Li X, Hoyt TL, Su PF,
Arlinghaus LR, Wilson KJ, Abramson VG, Chakravarthy AB and
Yankeelov TE: Early assessment of breast cancer response to
neoadjuvant chemotherapy by semi-quantitative analysis of
high-temporal resolution DCE-MRI: Preliminary results. Magn Reson
Imaging. 31:1457–1464. 2013. View Article : Google Scholar : PubMed/NCBI
|
15
|
Baltzer PA, Renz DM, Kullnig PE, Gajda M,
Camara O and Kaiser WA: Application of computer-aided diagnosis
(CAD) in MR-mammography (MRM): Do we really need whole lesion time
curve distribution analysis? Acad Radiol. 16:435–442. 2009.
View Article : Google Scholar : PubMed/NCBI
|
16
|
Renz DM, Böttcher J, Diekmann F,
Poellinger A, Maurer MH, Pfeil A, Streitparth F, Collettini F, Bick
U, Hamm B, et al: Detection and classification of
contrast-enhancing masses by a fully automatic computer-assisted
diagnosis system for breast MRI. J Magn Reson Imaging.
35:1077–1088. 2012. View Article : Google Scholar : PubMed/NCBI
|
17
|
Beresford MJ, Padhani AR, Taylor NJ,
Ah-See ML, Stirling JJ, Makris A, d'Arcy JA and Collins DJ: Inter-
and intraobserver variability in the evaluation of dynamic breast
cancer MRI. J Magn Reson Imaging. 24:1316–1325. 2006. View Article : Google Scholar : PubMed/NCBI
|
18
|
Brodersen J and Siersma VD: Long-term
psychosocial consequences of false-positive screening mammography.
Ann Fam Med. 11:106–115. 2013. View
Article : Google Scholar : PubMed/NCBI
|
19
|
Yang Q, Li L, Zhang J, Shao G and Zheng B:
A computerized global MR image feature analysis scheme to assist
diagnosis of breast cancer: A preliminary assessment. Eur J Radiol.
83:1086–1091. 2014. View Article : Google Scholar : PubMed/NCBI
|
20
|
Partridge SC, Rahbar H, Murthy R, Chai X,
Kurland BF, DeMartini WB and Lehman CD: Improved diagnostic
accuracy of breast MRI through combined apparent diffusion
coefficients and dynamic contrast-enhanced kinetics. Magn Reson
Med. 65:1759–1767. 2011. View Article : Google Scholar : PubMed/NCBI
|
21
|
El Khouli RH, Macura KJ, Kamel IR, Jacobs
MA and Bluemke DA: 3-T dynamic contrast-enhanced MRI of the breast:
Pharmacokinetic parameters versus conventional kinetic curve
analysis. AJR Am J Roentgenol. 197:1498–1505. 2011. View Article : Google Scholar : PubMed/NCBI
|
22
|
Bluemke DA, Gatsonis CA, Chen MH,
DeAngelis GA, DeBruhl N, Harms S, Heywang-Köbrunner SH, Hylton N,
Kuhl CK, Lehman C, et al: Magnetic resonance imaging of the breast
prior to biopsy. JAMA. 292:2735–2742. 2004. View Article : Google Scholar : PubMed/NCBI
|
23
|
Choi HK, Cho N, Moon WK, Im SA, Han W and
Noh DY: Magnetic resonance imaging evaluation of residual ductal
carcinoma in situ following preoperative chemotherapy in breast
cancer patients. Eur J Radiol. 81:737–743. 2012. View Article : Google Scholar : PubMed/NCBI
|
24
|
Pinker-Domenig K, Bogner W, Gruber S,
Bickel H, Duffy S, Schernthaner M, Dubsky P, Pluschnig U, Rudas M,
Trattnig S, et al: High resolution MRI of the breast at 3 T: Which
BI-RADS® descriptors are most strongly associated with
the diagnosis of breast cancer. Eur Radiol. 22:322–330. 2012.
View Article : Google Scholar : PubMed/NCBI
|
25
|
Pinker K, Bogner W, Baltzer P, Gruber S,
Bickel H, Brueck B, Trattnig S, Weber M, Dubsky P, Bago-Horvath Z,
et al: Improved diagnostic accuracy with multiparametric magnetic
resonance imaging of the breast using dynamic contrast-enhanced
magnetic resonance imaging, diffusion-weighted imaging, and
3-dimensional proton magnetic resonance spectroscopic imaging.
Invest Radiol. 49:421–430. 2014. View Article : Google Scholar : PubMed/NCBI
|
26
|
Levman J, Warner E, Causer P and Martel A:
Semi-automatic region-of-interest segmentation based computer-aided
diagnosis of mass lesions from dynamic contrast-enhanced magnetic
resonance imaging based breast cancer screening. J Digit Imaging.
27:670–678. 2014. View Article : Google Scholar : PubMed/NCBI
|
27
|
Williams TC, DeMartini WB, Partridge SC,
Peacock S and Lehman CD: Breast MR imaging: Computer-aided
evaluation program for discriminating benign from malignant
lesions. Radiology. 244:94–103. 2007. View Article : Google Scholar : PubMed/NCBI
|
28
|
Renz DM, Durmus T, Böttcher J, Taupitz M,
Diekmann F, Huppertz A, Pfeil A, Maurer MH, Streitparth F, Bick U,
et al: Comparison of gadoteric acid and gadobutrol for detection as
well as morphologic and dynamic characterization of lesions on
breast dynamic contrast-enhanced magnetic resonance imaging. Invest
Radiol. 49:474–484. 2014. View Article : Google Scholar : PubMed/NCBI
|
29
|
Newell D, Nie K, Chen JH, Hsu CC, Yu HJ,
Nalcioglu O and Su MY: Selection of diagnostic features on breast
MRI to differentiate between malignant and benign lesions using
computer-aided diagnosis: Differences in lesions presenting as mass
and non-mass-like enhancement. Eur Radiol. 20:771–781. 2010.
View Article : Google Scholar : PubMed/NCBI
|
30
|
D'Orsi CJ, Sickles EA, Mendelson EB and
Morris EA: ACR BI-RADS® Atlas, Breast Imaging Reporting
and Data System. American College of Radiology; Reston, VA:
2013
|
31
|
Kong Y, Deng Y and Dai Q: Discriminative
clustering and feature selection for brain MRI segmentation. IEEE
Signal Process Lett. 22:573–577. 2015. View Article : Google Scholar
|
32
|
Kong Y, Li Y, Wu J and Shu H: Noise
reduction of diffusion tensor images by sparse representation and
dictionary learning. Biomed Eng Online. 15:52016. View Article : Google Scholar : PubMed/NCBI
|