Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis

Magnetic Resonance Imaging

Volumn 32, Issue 3, 2014, Pages 197-205

  Wang, Teh Chen ^a   Huang, Yan Hao ^b   Huang, Chiun Sheng ^c   Chen, Jeon Hor ^d,e   Huang, Guei Yu ^b   Chang, Yeun Chung ^c   Chang, Ruey Feng ^b  

^a TAIPEI CITY HOSPITAL   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^c NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e E DA HOSPITAL   (Taiwan)

Author keywords

Breast; DCE MRI; Ellipsoid; GLCM; Morphology; Pharmacokinetic

Indexed keywords

BREAST; DCE-MRI; ELLIPSOID; GLCM; MORPHOLOGY; PHARMACOKINETIC;

ADULT; AGED; AGED, 80 AND OVER; BREAST NEOPLASMS; CONTRAST MEDIA; FEMALE; GADOLINIUM DTPA; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84894229667     PISSN: 0730725X     EISSN: 18735894     Source Type: Journal    
DOI: 10.1016/j.mri.2013.12.002     Document Type: Article

References (31)

1. Kuhl C.K., Schrading S., Bieling H.B., et al. MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet 2007, 370(9586):485-492.
2. Chen W.J., Giger M.L., Bick U., Newstead G.M. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys 2006, 33(8):2878-2887.
3. Chen W.J., Giger M.L., Lan L., Bick U. Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics. Med Phys 2004, 31(5):1076-1082.
4. Mahoney M.C., Gatsonis C., Hanna L., DeMartini W.B., Lehman C. Positive predictive value of BI-RADS MR imaging. Radiology 2012, 264(1):51-58.
5. Stoutjesdijk M.J., Futterer J.J., Boetes C., van Die L.E., Jager G., Barentsz J.O. Variability in the description of morphologic and contrast enhancement characteristics of breast lesions on magnetic resonance imaging. Invest Radiol 2005, 40(6):355-362.
6. Newell D., Nie K., Chen J.H., et al. 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 2010, 20(4):771-781.
7. Nie K., Chen J.H., Yu H.J., Chu Y., Nalcioglu O., Su M.Y. Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol 2008, 15(12):1513-1525.
8. Chen W., Giger M.L., Li H., Bick U., Newstead G.M. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 2007, 58(3):562-571.
9. Tofts P.S. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997, 7(1):91-101.
10. Tofts P.S., Brix G., Buckley D.L., et al. Estimating kinetic parameters from dynamic contrast-enhanced T-1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999, 10(3):223-232.
11. Chang Y.C., Huang Y.H., Huang C.S., Chang P.K., Chen J.H., Chang R.F. Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. Magn Reson Imaging 2012, 20(3):312-322.
12. Wiener J.I., Schilling K.J., Adami C., Obuchowski N.A. Assessment of suspected breast cancer by MRI: a prospective clinical trial using a combined kinetic and morphologic analysis. Am J Roentgenol 2005, 184(3):878-886.
13. Galbraith S.M., Lodge M.A., Taylor N.J., et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed 2002, 15(2):132-142.
14. Vedantham S., Shi L., Karellas A., O'Connell A.M. Dedicated breast CT: fibroglandular volume measurements in a diagnostic population. Med Phys 2012, 39(12):7317-7328.
15. Agner S.C., Xu J., Madabhushi A. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Med Phys 2013, 40(3):032305.
16. Moon W.K., Shen Y.W., Huang C.S., Chiang L.R., Chang R.F. Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images. Ultrasound Med Biol 2011, 37(4):539-548.
17. Haralick R.M., Shanmuga K., Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973, Smc3(6):610-621.
18. Holli K., Laaperi A.L., Harrison L., et al. Characterization of breast cancer types by texture analysis of magnetic resonance images. Acad Radiol 2010, 17(2):135-141.
19. Morris E., Liberman L. Breast MRI: diagnosis and intervention 2005, Springer, New York, [xviii, 513 pp.].
20. Bribiesca E. An easy measure of compactness for 2D and 3D shapes. Pattern Recogn 2008, 41(2):543-554.
21. Meinel L.A., Stolpen A.H., Berbaum K.S., Fajardo L.L., Reinhardt J.M. Breast MRI lesion classification: improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system. J Magn Reson Imaging 2007, 25(1):89-95.
22. Mulchrone K.F., Choudhury K.R. Fitting an ellipse to an arbitrary shape: implications for strain analysis. J Struct Geol 2004, 26(1):143-153.
23. Zhu Q., Poh L.-K. A transformation-invariant recursive subdivision method for shape analysis. Pattern Recognition 1988, vol. 832:833-835.
24. Chen W.J., Giger M.L., Bick U. A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol 2006, 13(1):63-72.
25. Kohavi R., John G.H. Wrappers for feature subset selection. Artif Intell 1997, 97(1-2):273-324.
26. Field A.P. Discovering statistics using SPSS 2009, Los Angeles, SAGE Publications, [xxxii, 821 pp.].
27. Xiao Y., Hua J., Dougherty E.R. Quantification of the impact of feature selection on the variance of cross-validation error estimation. EURASIP J Bioinform Syst Biol 2007, 16354.
28. Kira K., Rendell L.A. A practical approach to feature selection. Proceedings of the Ninth International Workshop on Machine Learning 1992, 249-256. Morgan Kaufmann Publishers Inc., Aberdeen, Scotland, United Kingdom.
29. Kononenko I. Estimating attributes: analysis and extensions of RELIEF. Proceedings of the European Conference on Machine Learning 1994, 171-182. Springer-Verlag New York, Inc., Catania, Italy.
30. Shen W.C., Chang R.F., Moon W.K., Chou Y.H., Huang C.S. Breast ultrasound computer-aided diagnosis using BI-RADS features. Acad Radiol 2007, 14(8):928-939.
31. Furman-Haran E., Schechtman E., Kelcz F., Kirshenbaum K., Degani H. Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions. Cancer 2005, 104(4):708-718.