Saslow D, Boetes C, Burke W, Harms S, Leach MO, Lehman CD, Morris E, Pisano E, Schnall M, Sener S: American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography. CA Cancer J Clin. 2007, 57 (2): 75-89. 10.3322/canjclin.57.2.75.
Article
PubMed
Google Scholar
Kuhl CK, Schrading S, Bieling HB, Wardelmann E, Leutner CC, Koenig R, Kuhn W, Schild HH: MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. Lancet. 2007, 370 (9586): 485-492. 10.1016/S0140-6736(07)61232-X.
Article
PubMed
Google Scholar
Bartholow T, Becich M, Chandran U, Parwani A: Immunohistochemical analysis of ezrin-radixin-moesin-binding phosphoprotein 50 in prostatic adenocarcinoma. BMC Urol. 2011, 11 (1): 12-10.1186/1471-2490-11-12.
Article
PubMed
PubMed Central
Google Scholar
Yoshikawa MI, Ohsumi S, Sugata S, Kataoka M, Takashima S, Kikuchi K, Mochizuki T: Comparison of breast cancer detection by diffusion-weighted magnetic resonance imaging and mammography. Radiat Med. 2007, 25 (5): 218-223. 10.1007/s11604-007-0128-4.
Article
PubMed
Google Scholar
Iima M, Le Bihan D, Okumura R, Okada T, Fujimoto K, Kanao S, Tanaka S, Fujimoto M, Sakashita H, Togashi K: Apparent diffusion coefficient as an MR imaging biomarker of low-risk ductal carcinoma in situ: a pilot study. Radiology. 2011, 260 (2): 364-372. 10.1148/radiol.11101892.
Article
PubMed
Google Scholar
Marini C, Iacconi C, Giannelli M, Cilotti A, Moretti M, Bartolozzi C: Quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesion. Eur Radiol. 2007, 17 (10): 2646-2655. 10.1007/s00330-007-0621-2.
Article
CAS
PubMed
Google Scholar
Hatakenaka M, Soeda H, Yabuuchi H, Matsuo Y, Kamitani T, Oda Y, Tsuneyoshi M, Honda H: Apparent diffusion coefficients of breast tumors: clinical application. Magn Reson Med Sci. 2008, 7 (1): 23-29. 10.2463/mrms.7.23.
Article
PubMed
Google Scholar
Guo Y, Cai YQ, Cai ZL, Gao YG, An NY, Ma L, Mahankali S, Gao JH: Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging. 2002, 16 (2): 172-178. 10.1002/jmri.10140.
Article
PubMed
Google Scholar
Partridge SC, Mullins CD, Kurland BF, Allain MD, DeMartini WB, Eby PR, Lehman CD: Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: effects of lesion type and size. Am J Roentgenol. 2010, 194 (6): 1664-1673. 10.2214/AJR.09.3534.
Article
Google Scholar
Sinha S, Lucas-Quesada FA, Sinha U, DeBruhl N, Bassett LW: In vivo diffusion-weighted MRI of the breast: Potential for lesion characterization. J Magn Reson Imaging. 2002, 15 (6): 693-704. 10.1002/jmri.10116.
Article
PubMed
Google Scholar
Woodhams R, Matsunaga K, Kan S, Hata H, Ozaki M, Iwabuchi K, Kuranami M, Watanabe M, Hayakawa K: ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci. 2005, 4 (1): 35-42. 10.2463/mrms.4.35.
Article
PubMed
Google Scholar
Pickles M, Manton D, Lowry M, Turnbull L: Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy. Eur J Radiol. 2009, 71 (3): 498-505. 10.1016/j.ejrad.2008.05.007.
Article
PubMed
Google Scholar
Su MY, Cheung YC, Fruehauf JP, Yu H, Nalcioglu O, Mechetner E, Kyshtoobayeva A, Chen SC, Hsueh S, McLaren CE: Correlation of dynamic contrast enhancement MRI parameters with microvessel density and VEGF for assessment of angiogenesis in breast cancer. J Magn Reson Imaging. 2003, 18 (4): 467-477. 10.1002/jmri.10380.
Article
PubMed
Google Scholar
Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD: Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. Am J Roentgenol. 2009, 193 (6): 1716-1722. 10.2214/AJR.08.2139.
Article
Google Scholar
Schelfout K, Van Goethem M, Kersschot E, Colpaert C, Schelfhout AM, Leyman P, Verslegers I, Biltjes I, Van Den Haute J, Gillardin JP, Tjalma W, Van Der A, Buytaert JC: Contrast-enhanced MR imaging of breast lesions and effect on treatment. Eur J Surg Oncol. 2004, 30 (5): 501-507. 10.1016/j.ejso.2004.02.003.
Article
CAS
PubMed
Google Scholar
Jansen SA, Fan X, Karczmar GS, Abe H, Schmidt RA, Giger M, Newstead GM: DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement?. Med Phys. 2008, 35 (7): 3102-3109. 10.1118/1.2936220.
Article
PubMed
PubMed Central
Google Scholar
Zhang Y, Fukatsu H, Naganawa S, Satake H, Sato Y, Ohiwa M, Endo T, Ichihara S, Ishigaki T: The role of contrast-enhanced MR mammography for determining candidates for breast conservation surgery. Breast Cancer. 2002, 9 (3): 231-239. 10.1007/BF02967595.
Article
PubMed
Google Scholar
Kuhl CK: MRI of breast tumors. Eur Radiol. 2000, 10 (1): 46-58. 10.1007/s003300050006.
Article
CAS
PubMed
Google Scholar
Yabuuchi H, Matsuo Y, Okafuji T, Kamitani T, Soeda H, Setoguchi T, Sakai S, Hatakenaka M, Kubo M, Sadanaga N, Yamamoto H, Honda H: Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images. J Magn Reson Imaging. 2008, 28 (5): 1157-1165. 10.1002/jmri.21570.
Article
PubMed
Google Scholar
Yili Z, Xiaoyan H, Hongwen D, Yun Z, Xin C, Peng W, Youmin G: The value of diffusion-weighted imaging in assessing the ADC changes of tissues adjacent to breast carcinoma. BMC Cancer. 2009, 9 (1): 18-10.1186/1471-2407-9-18.
Article
PubMed
PubMed Central
Google Scholar
Mendez A, Pizzorni Ferrarese F, Summers P, Petralia G, Menegaz G: DCE-MRI and DWI integration for breast lesions assessment and heterogeneity quantification. Int J Biomed Imaging. 2012, 2012: 2-4.
Article
Google Scholar
Cheng HD, Shan J, Ju W, Guo Y, Zhang L: Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recogn. 2010, 43 (1): 299-317. 10.1016/j.patcog.2009.05.012.
Article
Google Scholar
Sahiner B, Chan H, Roubidoux MA, Hadjiiski LM, Helvie MA, Paramagul C, Bailey J, Nees AV, Blane C: Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy. Radiology. 2007, 242 (3): 716-724. 10.1148/radiol.2423051464.
Article
PubMed
PubMed Central
Google Scholar
Shen WC, Chang RF, Moon WK, Chou YH, Huang CS: Breast ultrasound computer-aided diagnosis using BI-RADS features. Acad Radiol. 2007, 14 (8): 928-939. 10.1016/j.acra.2007.04.016.
Article
PubMed
Google Scholar
Meinel LA, Stolpen AH, Berbaum KS, Fajardo LL, Reinhardt JM: 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. 10.1002/jmri.20794.
Article
PubMed
Google Scholar
Huang Y, Wang K, Chen D: Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl. 2006, 15 (2): 164-169. 10.1007/s00521-005-0019-5.
Article
Google Scholar
Yankeelov TE, Lepage M, Chakravarthy A, Broome EE, Niermann KJ, Kelley MC, Meszoely I, Mayer IA, Herman CR, McManus K, Price RR, Gore JC: Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging. 2007, 25 (1): 1-13. 10.1016/j.mri.2006.09.006.
Article
PubMed
Google Scholar
Woodhams R, Ramadan S, Stanwell P, Sakamoto S, Hata H, Ozaki M, Kan S, Inoue Y: Diffusion-weighted imaging of the breast: principles and clinical applications. Radiographics. 2011, 31 (4): 1059-1084. 10.1148/rg.314105160.
Article
PubMed
Google Scholar
Agner SC, Soman S, Libfeld E, McDonald M, Thomas K, Englander S, Rosen MA, Chin D, Nosher J, Madabhushi A: Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging. 2011, 24 (3): 446-463. 10.1007/s10278-010-9298-1.
Article
PubMed
Google Scholar
Nie K, Chen J, Yu HJ, Chu Y, Nalcioglu O, Su M: Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI. Acad Radiol. 2008, 15 (12): 1513-1525. 10.1016/j.acra.2008.06.005.
Article
PubMed
PubMed Central
Google Scholar
Chen H, Yang B, Liu J, Liu D: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl. 2011, 38 (7): 9014-9022. 10.1016/j.eswa.2011.01.120.
Article
Google Scholar
Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Mach Learn. 2002, 46 (1–3): 389-422.
Article
Google Scholar
Akay MF: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl. 2009, 36 (2): 3240-3247. 10.1016/j.eswa.2008.01.009.
Article
Google Scholar
Shakhnarovich G, Darrell T, Indyk P: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice, vol. 3. 2005, Cambridge, MA, USA: MIT press
Google Scholar
Delen D, Walker G, Kadam A: Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med. 2005, 34 (2): 113-127. 10.1016/j.artmed.2004.07.002.
Article
PubMed
Google Scholar
Wu K: Analysis of parameter selections for fuzzy c-means. Pattern Recogn. 2012, 45 (1): 407-415. 10.1016/j.patcog.2011.07.012.
Article
Google Scholar
Xu C, Prince JL: Snakes, shapes, and gradient vector flow. IEEE Trans Image Process. 1998, 7 (3): 359-369. 10.1109/83.661186.
Article
CAS
PubMed
Google Scholar
Chang RF, Wu WJ, Moon WK, Chen DR: Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis. Ultrasound Med Biol. 2003, 29 (5): 679-686. 10.1016/S0301-5629(02)00788-3.
Article
PubMed
Google Scholar
Basu S, Hall LO, Goldgof DB, Yuhua G, Kumar V, Jung C, Gillies RJ, Gatenby RA: Developing a Classifier Model for Lung Tumors in CT-Scan Images. Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on: 2014-01-09 2011. 2011, Anchorage: IEEE, 1306-1312.
Chapter
Google Scholar
Castellano G, Bonilha L, Li LM, Cendes F: Texture analysis of medical images. Clin Radiol. 2004, 59 (12): 1061-1069. 10.1016/j.crad.2004.07.008.
Article
CAS
PubMed
Google Scholar
Hylton NM: Vascularity assessment of breast lesions with gadolinium-enhanced MR imaging. Magn Reson Imaging Clin N Am. 2001, 9 (2): 321-332.
CAS
PubMed
Google Scholar
Abdolmaleki P, Buadu LD, Naderimansh H: Feature extraction and classification of breast cancer on dynamic magnetic resonance imaging using artificial neural network. Cancer Lett. 2001, 171 (2): 183-191. 10.1016/S0304-3835(01)00508-0.
Article
CAS
PubMed
Google Scholar
Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, Schild HH: Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?. Radiology. 1999, 211 (1): 101-110. 10.1148/radiology.211.1.r99ap38101.
Article
CAS
PubMed
Google Scholar
Rubesova E, Grell AS, De Maertelaer V, Metens T, Chao SL, Lemort M: Quantitative diffusion imaging in breast cancer: a clinical prospective study. J Magn Reson Imaging. 2006, 24 (2): 319-324. 10.1002/jmri.20643.
Article
PubMed
Google Scholar
Wenkel E, Geppert C, Schulz-Wendtland R, Uder M, Kiefer B, Bautz W, Janka R: Diffusion weighted imaging in breast MRI: comparison of two different pulse sequences. Acad Radiol. 2007, 14 (9): 1077-1083. 10.1016/j.acra.2007.06.006.
Article
PubMed
Google Scholar
Petralia G, Bonello L, Priolo F, Summers P, Bellomi M: Breast MR with special focus on DW-MRI and DCE-MRI. Cancer Imaging. 2011, 11: 76-90. 10.1102/1470-7330.2011.0014.
Article
CAS
PubMed
PubMed Central
Google Scholar
Cai H, Ng M: Feature weighting by RELIEF based on local hyperplane approximation. Proceedings of the 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume Part II:2012. 2012, Kuala Lumpur, Malaysia: Springer-Verlag, 335-346.
Chapter
Google Scholar
Cai H, Ng M: Optimal Combination of Feature Weight Learning and Classification Based on Local Approximation. Proceedings of the Third International Conference on Data and Knowledge Engineering: November 21-23, 2012, Fujian. Edited by: Yang X. 2012, Berlin Heidelberg: Springer, 86-94.
Google Scholar
Cai H, Ruan P, Ng M, Akutsu T: Feature weight estimation for gene selection: a local hyperlinear learning approach. BMC Bioinforma. 2014, 15 (1): 70-10.1186/1471-2105-15-70.
Article
Google Scholar