• No results found

Chapter 6 Conclusions and Future Work

6.4 Future works

The work described in this thesis suggests several directions for future work.

 The main goal of any CAD system is support a doctor in making the correct decision. The current system takes either clinical or biological datasets as input but not both. This is because there is no dataset found in the public databases that contains both types of examination for the same patient. Therefore, in future, we can evolve the framework by collecting a dataset containing both types of examination and integrating the decision of the clinical and biological subsystem in order to enhance the diagnostic accuracy of the framework.

 As well as the ultrasound, mammography too is considered an important examination in the early detection of breast cancer. Both share some morphological features such as shape and margin. Therefore, we would suggest the inclusion of the new feature, CRD, in mammography based CAD systems in order to enhance their effectiveness in differentiating cancer from benign cases.

 Microarray technology has been widely used for diagnosing several other diseases such as, leukaemia, lymphoma and ovarian cancers. We would suggest the use of the methodology developed in this study to identify biomarkers for enhancing the diagnostic accuracy of these diseases too.

 Microarrays have been used not only for diagnosis but also for prognosis. This method could identify biological biomarkers to be utilized for stratification of cancer patients as well as to identify patients at higher risks that might require differential therapeutic interventions.

References

Aaroe, J., Lindahl, T., Dumeaux, V., Saebo, S., Tobin, D., Hagen, N., ..et al. (2010). Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer

Research, 12(1), R7.

Acuna, E., & Rodriguez, C. (2004). The treatment of missing values and its effect in the classifier accuracy. In: Banks, D., House, L., McMorris, E. R., Arabie, P. & Gaul, W. (eds.) Classification,

Clustering and Data Mining Applications, (pp. 639–648) Heidelberg, Berlin: Springer-Verlag.

Afanasyeva, E. A., Mestdagh, P., Kumps, C., Vandesompele, J., Ehemann, V., Theissen, J., et al. (2011). MicroRNA miR-885-5p targets CDK2 and MCM5, activates p53 and inhibits proliferation and survival. Cell Death and Differentiation, 18(6), 974-984.

Affymetrix. (2002). Statistical Algorithms Description Document. Affymetrix, Inc.

Al-Tarawneh, M., Khatib, S., & Arqub, K. (2010). Cancer incidence in Jordan, 1996-2005/Incidence du cancer en Jordanie entre 1996 et 2005. Eastern Mediterranean Health Journal, 16(8), 837- 845.

Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2008). Molecular biology of the

cell. ( 5th ed.) New York: Garland Press.

Altman, D. G., & Bland, J. M. (1994). Statistics Notes: Diagnostic tests 1: sensitivity and specificity.

BMJ, 308(6943), 1552-.BMJ 1994;308:1552 (11 June) .

Retrived from http://www.bmj.com/content/308/6943/1552.pdf

American_Cancer_Society. (2009). Breast Cancer Facts & Figures2009-2010 (p9). Atlanta: American Cancer Society, Inc.

American Cancer Society. (2012). Treatment Types. Rretrieved (18/1/2012) from

http://www.cancer.org/treatment/treatmentsandsideeffects/treatmenttypes/index . Amin, S., Kumar, A., Nilchi, L., Wright, K., & Kozlowski, M. (2011). Breast Cancer Cells Proliferation Is

Regulated by Tyrosine Phosphatase SHP1 through c-jun N-Terminal Kinase and Cooperative Induction of RFX-1 and AP-4 Transcription Factors. Molecular Cancer Research, 9(8), 1112- 1125.

Annibaldi, A., & Widmann, C. (2010). Glucose metabolism in cancer cells. Current Opinion in Clinical

Nutrition & Metabolic Care, 13(4), 466-470 410.1097/MCO.1090b1013e32833a35577.

Arfelli, F. (1998). Low-dose phase contrast x-ray medical imaging. Physics in Medicine and Biology, 43(10), 2845-2852.

Auffarth, B. ( 2007). Spectral Graph Clustering. Course report for T´ecnicas Avanzadas de Aprendizaje Universitat de Barcelona.

Babaei, S., Akker, E., Ridder, J., & Reinders, M. (2011). Integrating Protein Family Sequence Similarities with Gene Expression to Find Signature Gene Networks in Breast Cancer

Metastasis. In M. Loog, L. Wessels, M. T. Reinders & D. Ridder (Eds.), Pattern Recognition in

Bioinformatics (Vol. 7036, pp. 247-259): Springer Berlin Heidelberg.

Backes, C., Meese, E., Lenhof, H.-P., & Keller, A. (2010). A dictionary on microRNAs and their putative target pathways. Nucleic Acids Research.

Balakrishnama, S., & Ganapathiraju, A. (1998). Linear Discriminant Analysis - A brief Tutorial. Institute for Signal and information processing.

http://www.music.mcgill.ca/~ich/classes/mumt611/classifiers/lda_theory.pdf Balakrishnan, R., & Ranganathan, K. (2000). A text book of Graph Theory (first ed.). New York:

Springer.

Bandyopadhyay, N., Kahveci, T., Goodison, S., Sun, Y., & Ranka, S. (2009). Pathway-Based Feature Selection Algorithm for Cancer Microarray Data. Advances in Bioinformatics, 2009 (2009) . 1- 17

Bartel, D. P. (2004). MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell, 116(2), 281- 297.

Bath, P. A., Craigs, C., Maheswaran, R., Raymond, J., & Willett, P. (2005). Use of Graph Theory to Identify Patterns of Deprivation and High Morbidity and Mortality in Public Health Data Sets.

Beitsch, P. D., & Clifford, E. (2000). Detection of carcinoma cells in the blood of breast cancer patients. The American Journal of Surgery, 180(6), 446-449.

Beresford, M., Wilson, G., & Makris, A. (2006). Measuring proliferation in breast cancer: practicalities and applications. Breast Cancer Research, 8(6), 216-227.

Bermudo, R., Abia, D., Mozos, A., Garcia-Cruz, E., Alcaraz, A., Ortiz, A. R., et al. (2011). Highly sensitive molecular diagnosis of prostate cancer using surplus material washed off from biopsy

needles. Br J Cancer, 105(10), 1600-1607.

Biosystems, A. (2006). TaqMan® Gene Expression Assays for Validating Hits From Fluorescent

Microarrays. www.appliedbiosystems.com

Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning.

Artificial Intelligence, 97(1-2), 245-271.

Bou Kheir, T., Futoma-Kazmierczak, E., Jacobsen, A., Krogh, A., Bardram, L., Hother, C.,.. et al. (2011). miR-449 inhibits cell proliferation and is down-regulated in gastric cancer. Molecular Cancer, 10(1), 29-40.

Boyerinas, B., Park, S.-M., Hau, A., Murmann, A. E., & Peter, M. E. (2010). The role of let-7 in cell differentiation and cancer. Endocrine-Related Cancer, 17(1), 19-36.

Brennecke, J., Hipfner, D. R., Stark, A., Russell, R. B., & Cohen, S. M. (2003). bantam Encodes a Developmentally Regulated microRNA that Controls Cell Proliferation and Regulates the Proapoptotic Gene hid in Drosophila. Cell, 113(1), 25-36.

Brest, P., Lassalle, S., Hofman, V., Bordone, O., Gavric Tanga, V., Bonnetaud, C.,.. et al. (2011). MiR- 129-5p is required for histone deacetylase inhibitor-induced cell death in thyroid cancer cells.

Endocrine-Related Cancer, 18(6), 711-719.

Burges., C. J. C. (1998). A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining

and Knowledge Discovery, 2(2), 121-167.Burroni, M., Corona, R., Dell’Eva, G., Sera, F., Bono,

R., Puddu, P., et al. (2004). Melanoma Computer-Aided Diagnosis. Clinical Cancer Research, 10(6), 1881-1886.

Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., et al. (2002). Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 99(24), 15524-15529.

Calin, G. A., Liu, C.G., Sevignani, C., Ferracin, M., Felli, N., Dumitru, C. D., et al. (2004). MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proceedings of

the National Academy of Sciences of the United States of America, 101(32), 11755-11760.

Canutescu, A. A., Shelenkov, A. A., & Dunbrack, R. L. (2003). A graph-theory algorithm for rapid protein side-chain prediction. Protein Science, 12(9), 2001-2014.

Chang, R.F., Wu, W.J., Moon, W., & Chen, D.R. (2005). Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Research and Treatment, 89(2), 179-185.

Chang, Y. H., Wang, Y. C., & Chen, B. S. (2007). Nonlinear dynamic trans/cis regulatory circuit for gene transcription via microarray data. Gene Regulation and Systems Biology, 1, 151-166.

Chaves, R., Ramírez, J., Górriz, J. M., López, M., Salas-Gonzalez, D., Álvarez, I., et al. (2009). SVM- based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting. Neuroscience Letters, 461(3), 293-297. Chen, C.C., Hardy, D. B., & Mendelson, C. R. (2011). Progesterone Receptor Inhibits Proliferation of

Human Breast Cancer Cells via Induction of MAPK Phosphatase 1 (MKP-1/DUSP1). Journal of

Biological Chemistry, 286(50), 43091-43102.

Chen, C.M., Chou, Y.H., Han, K.C., Hung, G.S., Tiu, C.M., Chiou, H.J., et al. (2003). Breast Lesions on Sonograms: Computer-aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks1. Radiology, 226(2), 504-514.

Chen, C.Z., Li, L., Lodish, H. F., & Bartel, D. P. (2004). MicroRNAs Modulate Hematopoietic Lineage Differentiation. Science, 303(5654), 83-86.

Chen, H., Xu, Y., Ma, Y., & Ma, B. (2010). Neural Network Ensemble-Based Computer-Aided Diagnosis for Differentiation of Lung Nodules on CT Images: Clinical Evaluation. Academic Radiology, 17(5), 595-602.

Chen, L., Wang, X., Wang, H., Li, Y., Yan, W., Han, L., et al. (2012). miR-137 is frequently down- regulated in glioblastoma and is a negative regulator of Cox-2. European journal of cancer,

48(16):3104-11.

Chen, Y., Zhang, J., Wang, H., Zhao, J., Xu, C., Du, Y., et al. (2012). miRNA-135a promotes breast cancer cell migration and invasion by targeting HOXA10. BMC Cancer, 12(1), 111.

Chien, J., Fan, J.-B., Bell, D. A., April, C., Klotzle, B., Ota, T., et al. (2009). Analysis of gene expression in stage I serous tumors identifies critical pathways altered in ovarian cancer. Gynecologic

Oncology, 114(1), 3-11.

Cheng. H. D, Shana. S, Jua. W, Guoa. Y, Zhang. L. (2010). Automated breast cancer detection and

classification using ultrasound images: A survey.Pattern Recognition 43 (2010) 299 –317.

Chow, L. W. C., Lui, K. L., Chan, J. C. Y., Chan, T. C., Ho, P. K., Lee, W. Y., et al. (2005). Association Between Body Mass Index and Risk of Formation of Breast Cancer in Chinese Women. Asian

Journal of Surgery, 28(3), 179-184.

Cittelly, D., Das, P., Spoelstra, N., Edgerton, S., Richer, J., Thor, A., et al. (2010). Downregulation of miR-342 is associated with tamoxifen resistant breast tumors. Molecular Cancer, 9(1), 317. Clancy, S. (2008) DNA transcription. Nature Education 1(1). Retrieved from

http://www.nature.com/scitable/topicpage/dna-transcription-426

Cohen, A. L., Soldi, R., Zhang, H., Gustafson, A. M., Wilcox, R., Welm, B. E., et al. (2011). A

pharmacogenomic method for individualized prediction of drug sensitivity. Mol Syst Biol, 7. 513-525

Cooper, S. A. P. (2005a). Of the areola. On the anatomy of the breast, by Sir Astley Paston Cooper

1840, 1, 11. Retrieved from http://jdc.jefferson.edu/cooper/60/

Cooper, S. A. P. (2005b). Of the internal parts of the breast, or mammary gland. On the anatomy of

the breast, by Sir Astley Paston Cooper, 1840, 1, 24.

Cooper, S. A. P. (2005c). Structure of the breast in the human female. On the anatomy of the breast,

by Sir Astley Paston Cooper 1840, vol(1) 6. Retrived from: http://jdc.jefferson.edu/cooper/6/

Cox, P., & Goding, C. (1991). Transcription and cancer. BR J Cancer., 73(5), 651-662.

Cuk, K., Zucknick, M., Heil, J., Madhavan, D., Schott, S., Turchinovich, A., et al. (2012). Circulating microRNAs in plasma as early detection markers for breast cancer. International Journal of

Cancer, 132(1).

Dagliyan, O., Uney-Yuksektepe, F., Kavakli, I. H., & Turkay, M. (2011). Optimization Based Tumor Classification from Microarray Gene Expression Data. PLoS ONE, 6(2), e14579.

Dai, J. J., Lieu, L., & Rocke., D. (2006). Dimension Reduction for Classification with Gene Expression Microarray Data. Statistical Applications in Genetics and Molecular Biology, 5(1), Article 6. Chen, D.R., & Hsiao, Y. H. (2008). Computer-aided Diagnosis in Breast Ultrasound. Journal of Medical

Ultrasound, 16(1), 46-56.

Darzi, M., AsgharLiaei, A., Hosseini, M., & Asghari, H. (2011). Feature Selection for Breast Cancer Diagnosis:A Case-Based Wrapper Approach. World Academy of Science, Engineering and

Technology, 77, 1142-1145.

Datta, S., & Datta, S. (2003). Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics, 19(4), 459-466.

Dawany, N. B., Dampier, W. N., & Tozeren, A. (2011). Large-scale integration of microarray data reveals genes and pathways common to multiple cancer types. International Journal of

Cancer, 128(12), 2881-2891.

Daxin, J., Chun, T., & Aidong, Z. (2004). Cluster Analysis for Gene Expression Data: A Survey. IEEE

Trans. on Knowl. and Data Eng., 16(11), 1370-1386.

de Souto, M., Costa, I., de Araujo, D., Ludermir, T., & Schliep, A. (2008). Clustering cancer gene expression data: a comparative study. BMC Bioinformatics, 9(1), 497.

Debouck, C., & Metcalf, B. (2000). The Impact of Genomics on Drug Discovery. Annual Review of

Pharmacology and Toxicology, 40(1), 193-208.

Dee, K. E., & Sickles, E. A. (2001). Medical Audit of Diagnostic Mammography Examinations: Comparison with Screening Outcomes Obtained Concurrently. Am. J. Roentgenol., 176(3), 729-733.

Dennis, G., Sherman, B., Hosack, D., Yang, J., Gao, W., Lane, H. C., et al. (2003). DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biology, 4(5), P3.

Dowsett, M., Archer, C., Assersohn, L., Gregory, R. K., Ellis, P. A., Salter, J., et al. (1999). Clinical studies of apoptosis and proliferation in breast cancer. Endocrine-Related Cancer, 6(1), 25- 28.

Drukker, K., Giger, M. L., & Metz, C. E. (2005). Robustness of computerized lesion detection and classification scheme across different breast US platforms. [Article]. Radiology, 237(3), 834- 840.

Drukker, K., Giger, M. L., Vyborny, C. J., & Mendelson, E. B. (2004). Computerized detection and classification of cancer on breast ultrasound. [Article]. Academic Radiology, 11(5), 526-535. Drukker, K., Lorenzo, P., & Giger, M.. (2010). Repeatability in computer-aided diagnosis: Application

to breast cancer diagnosis on sonography. Medical Physics., 37(6), 2659-2669.

Duan, Q., Wang, X., Gong, W., Ni, L., Chen, C., He, X., et al. (2012). ER Stress Negatively Modulates the Expression of the miR-199a/214 Cluster to Regulates Tumor Survival and Progression in Human Hepatocellular Cancer. PLoS ONE, 7(2), e31518.

Dykxhoorn, D. M., Wu, Y., Xie, H., Yu, F., Lal, A., Petrocca, F., et al. (2009). miR-200 Enhances Mouse Breast Cancer Cell Colonization to Form Distant Metastases. PLoS ONE, 4(9), e7181.

Elmore, S. (2007). Apoptosis: A Review of Programmed Cell Death. Toxicologic Pathology, 35(4), 495- 516.

Eva Singletary S. M., FACS. (2002). Rating the risk factors for breast cancer. ANNALS OF SURGERY, 237, 474-478.

Examines., F. I. What is normal breast development? Retrieved 10/1/ 2012, from http://www.007b.com/breast_development.php

Eyster., K. M., & Lindahl., R. (2001). Molecular medicine: a primer for clinicians. Part XII: DNA microarrays and their application to clinical medicine. S D J Med journal 54(2), 57-61. Fan, X., Shi, L., Fang, H., Cheng, Y., Perkins, R., & Tong, W. (2010). DNA Microarrays Are Predictive of

Cancer Prognosis: A Re-evaluation. Clinical Cancer Research, 16(2), 629-636.

Fang, X., Evans, K., Willis, R. C., Burrell, A., Hoang, Q., Xu, W., et al. (2006). High-Throughput Sample Preparation from Whole Blood for Gene Expression Analysis. Journal of the Association for

Laboratory Automation, 11(6), 381-386.

Fassan, M., Baffa, R., Palazzo, J., Lloyd, J., Crosariol, M., Liu, C.-G., et al. (2009). MicroRNA expression profiling of male breast cancer. Breast Cancer Research, 11(4), R58.

Feng, Z., Marti, A., Jehn, B., Altermatt, H. J., Chicaiza, G., & Jaggi, R. (1995). Glucocorticoid and progesterone inhibit involution and programmed cell death in the mouse mammary gland.

The Journal of Cell Biology, 131(4), 1095-1103.

Foundation, N. B. C. (2012). Types of Breast Cancer. Retrieved 16/1, 2012, from http://www.nationalbreastcancer.org/About-Breast-Cancer/Types.aspx

Fu, J. C., Lee, S. K., Wong, S. T. C., Yeh, J. Y., Wang, A. H., & Wu, H. K. (2005). Image segmentation feature selection and pattern classification for mammographic microcalcifications.

Computerized Medical Imaging and Graphics, 29(6), 419-429.

Fu, S. W., Chen, L., & Man, Y.G. (2011). miRNA Biomarkers in Breast Cancer Detection and Management. Journal of Cancer., 2, 116–122.

Gasco, M., Shami, S., & Crook, T. (2002). The p53 pathway in breast cancer. Breast Cancer Res, 4(2), 70 - 76.

Geng, X., Liu, T.Y., Qin, T., & Li, H. (2007). Feature selection for ranking. Paper presented at the Proceedings of the 30th annual international ACM SIGIR conference on Research and

development in information retrieval. ACM, Amsterdam, The Netherlands, 407-414.

Genovese, G., Ergun, A., Shukla, S. A., Campos, B., Hanna, J., Ghosh, P., et al. (2012). microRNA Regulatory Network Inference Identifies miR-34a as a Novel Regulator of TGF-β Signaling in Glioblastoma. Cancer Discovery, 2(8), 736-749.

Ghavami, S., Hashemi, M., Ande, S. R., Yeganeh, B., Xiao, W., Eshraghi, M., et al. (2009). Apoptosis and cancer: mutations within caspase genes. Journal of Medical Genetics, 46(8), 497-510.

Gilad-Bachrach, R., Navot, A., & Tishby, N. (2004). Margin based feature selection - theory and

algorithms. Paper presented at the Proceedings of the twenty-first international conference

on Machine learning. ACM New York, NY, USA, p43.

Giricz, O., Reynolds, P. A., Ramnauth, A., Liu, C., Wang, T., Stead, L., et al. (2012). Hsa-miR-375 is differentially expressed during breast lobular neoplasia and promotes loss of mammary acinar polarity. The Journal of Pathology, 226(1), 108-119.

Glazebrook, K. N., Morton, M. J., & Reynolds, C. (2005). Vascular Tumors of the Breast:

Mammographic, Sonographic, and MRI Appearances. Am. J. Roentgenol., 184(1), 331-338. Gorunescu., F. (2007). Data Mining Techniques in Computer-Aided Diagnosis Non-Invasive Cancer

Detection. World Academy of Science, Engineering and Technology, 34, 280-283.

Graham, J. D., & Clarke, C. L. (1997). Physiological Action of Progesterone in Target Tissues. Endocrine

Reviews, 18(4), 502-519.

Gray, H., Williams, P. L., & Bannister, L. H. (1995). Gray's anatomy. Edinburgh: Churchill Livingstone. Griffiths-Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2008). miRBase: tools for microRNA

genomics. Nucleic Acids Research, 36(suppl 1), D154-D158.doi: 10.1093/nar/gkm952. Gui, J., Tian, Y., Wen, X., Zhang, W., Zhang, P., Gao, J., et al. (2010). Serum microRNA characterization

identifies miR-885-5p as a potential marker for detecting liver pathologies. Clinical Science, 120, 183-193.

Guo, A.-Y., Sun, J., Jia, P., & Zhao, Z. (2010). A Novel microRNA and transcription factor mediated regulatory network in schizophrenia. BMC Systems Biology, 4(1), 10.

Guttilla, I. K., & White, B. A. (2009). Coordinate Regulation of FOXO1 by miR-27a, miR-96, and miR- 182 in Breast Cancer Cells. Journal of Biological Chemistry, 284(35), 23204-23216.

Haldar, S., Negrini, M., Monne, M., Sabbioni, S., & Croce, C. M. (1994). Down-Regulation of bcl-2 by p53 in Breast Cancer Cells. Cancer Research, 54(8), 2095-2097.

Hall, M. (1999). Correlation-based Feature Selection for Machine Learning. University of Waikato. Retrived (4/4/2012) from: http://www.cs.waikato.ac.nz/~mhall/thesis.pdf.

Hao., Y., Xing., H., & He., C. (2012). Identification of the Her-2 expression-regulating miRNAs by microdissection and RT-PCR in breast cancer tissue. Cancer Research, 72(8).

Hardman, L. (2010). Breast Cancer. Lucent Books.

Harold, E. (2006). Anatomy of the breast. Women's Health Medicine, 3(1), 47-49.

Harvey Lodish, A. B., S. Lawrence Zipursky, Paul Matsudaira, , James Darnell, & Company, W. H. F. (1995). Molecular Cell Biology (3rd ed.). New York: W. H. Freeman.

He, X., Zha, H., H.Q. Ding, C., & D. Simon, H. (2002). Web document clustering using hyperlink structures. Computational Statistics & Data Analysis, 41(1), 19-45.

Heinig, J., Witteler, R., Schmitz, R., Kiesel, L., & Steinhard, J. (2008). Accuracy of classification of breast ultrasound findings based on criteria used for BI-RADS. Ultrasound in Obstetrics and

Gynecology, 32(4), 573-578.

Helmut Madjar, E. B. M. (2008). The Practice of Breast Ultrasound Technigues.Findings.differential

Diagnosis (2nd ed.) New York: Thieme Verlag.

Hong, A. S., Rosen, E. L., Soo, M. S., & Baker, J. A. (2005). BI-RADS for Sonography: Positive and Negative Predictive Values of Sonographic Features. American Journal of Roentgenology, 184(4), 1260-1265.

Hu, Z., Dong, J., Wang, L. E., Ma, H., Liu, J., Zhao, Y., et al. (2012). Serum microRNA profiling and breast cancer risk: the use of miR-484/191 as endogenous controls. Carcinogenesis, 33(4), 828-834.

Huang, Y.L., Wang, K.L., & Chen, D.R. (2006). Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput. Appl., 15(2), 164-169.

Huang, Y. L., Chen, D. R., Jiang, Y. R., Kuo, S. J., Wu, H. K., & Moon, W. K. (2008). Computer-aided diagnosis using morphological features for classifying breast lesions on ultrasound.

Ultrasound in Obstetrics and Gynecology, 32(4), 565-572.

Huo, Z., Giger, M. L., Vyborny, C. J., Olopade, F. I., & Wolverton, D. E. (1998). Computer-aided diagnosis: analysis of mammographic parenchymal patterns and classification of masses on digitized mammograms Engineering in Medicine and Biology Society, 2, 1017 - 1020

Imaginis. (2012). Metastatic Breast Cancer. Retrieved on 1/10/ 2012, from http://www.imaginis.com

Imam, J. S., Buddavarapu, K., Lee-chang, J. S., Ganapathy, S., Camosy, C., Chen, Y., et al. (2010).

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