1. Kaufmann M,
von Minckwitz G,
Smith R,
Valero V,
Gianni L,
Eiermann W,
Howell A,
Costa SD,
Beuzeboc P,
Untch M,
Blohmer JU,
Sinn HP,
Sittek R,
Souchon R,
Tulusan AH,
Volm T,
Senn HJ (2003).
International expert panel on the use of primary (preoperative) systemic treatment of operable breast cancer: review and recommendations.
J Clin Oncol 21: 2600-8.
2. Heys SD,
Hutcheon AW,
Sarkar TK,
Ogston KN,
Miller ID,
Payne S,
Smith I,
Walker LG,
Eremin O (2002).
Neoadjuvant docetaxel in breast cancer: 3-year survival results from the Aberdeen trial.
Clin Breast Cancer 3: S69-74.
3. von Minckwitz G,
Sinn HP,
Raab G (2008).
Clinical response after two cycles compared to HER2,
Ki-67,
p53,
and bcl-2 in independently predicting a pathological complete response after preoperative chemotherapy in patients with operable carcinoma of the breast.
Breast Cancer Res 10: R30.
4. Esserman LJ,
Kaplan E,
Partridge S,
Tripathy D,
Rugo H,
Park J,
Hwang S,
Kuerer H,
Sudilovsky D,
Lu Y,
Hylton N (2001).
MRI phenotype is associated with response to doxorubicin and cyclophosphamide neoadjuvant chemotherapy in Stage III breast cancer.
Ann Surg Oncol 8: 549-59.
5. Uematsu T,
Kasami M,
Yuen S (2010).
Neoadjuvant chemotherapy for breast cancer: correlation between the baseline MR imaging findings and responses to therapy.
Eur Radiol 20: 2315-22.
6. Pickles MD,
Manton DJ,
Lowry M,
Turnbull LW (2009).
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 71: 498-505.
7. Craciunescu OI,
Blackwell KL,
Jones EL,
Macfall JR,
Yu D,
Vujaskovic Z,
Wong TZ,
Liotcheva V,
Rosen EL,
Prosnitz LR,
Samulski TV,
Dewhirst MW (2009).
DCE-MRI parameters have potential to predict response of locally advanced breast cancer patients to neoadjuvant chemotherapy and hyperthermia: a pilot study.
Int J Hyperthermia 25: 405-15.
8. Bhooshan N,
Giger ML,
Jansen SA,
Li H,
Lan L,
Newstead GM (2010).
Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers.
Radiology 254: 680-90.
9. Holli K,
Lääperi AL,
Harrison L,
Luukkaala T,
Toivonen T,
Ryymin P,
Dastidar P,
Soimakallio S,
Eskola H (2010).
Characterization of breast cancer types by texture analysis of magnetic resonance images.
Acad Radiol 17: 135-141.
10. American College of Radiology (2003).
Breast imaging reporting and data system (BI-RADS) (4th ed): Reston.
11. Haralick RM,
Dinstein I,
Shanmugan K (1973).
Textural features for image classification.
IEEE Transactions on Systems,
Man,
and Cybernetics SMC-3: 610-621.
12. Tang X (1998).
Texture information in run-length matrices.
IEEE Trans Image Process 7: 1602-9.
13.
Depeursinge A,
Foncubierta-Rodríguez A,
Vargas A,
et al (2013).
Rotation-covariant texture analysis of 4D dual-energy CT as an indicator of local pulmonary perfusion.
IEEE International Symposium on Biomedical Imaging: From Nano to Macro; April,
2013; San Francisco,
CA,USA.
14. Pampel FC (2000).
Logistic regression: A primer.
In Sage University Papers Series on Quantitative Applications in the Social Sciences,
Thousand Oaks (ed) pp 7 - 132: California.
15. Cristianini,
N.,
and Shawe-Taylor,
J. (2000).
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,
First Edition (Cambridge: Cambridge University Press).
16. Zhang Y,
Moore GR,
Laule C,
Bjarnason TA,
Kozlowski P,
Traboulsee A,
et al (2013).
Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis.
Ann Neurol.
74: 91-9.
17. Ahmed A,
Gibbs P,
Pickles M,
Turnbull L (2013).
Texture Analysis in Assessment and Prediction of Chemotherapy Response in Breast Cancer.
J Magn Reson Imaging 38: 89-101.
18. Michoux N,
et al (2013).
Texture analysis of MR images to predict breast tumor response to neoadjuvant chemotherapy.
Abstract 1958,
ECR 2013,
Vienna,
Austria.
19. Loizou CP,
Murray V,
Pattichis MS,
Seimenis I,
Pantziaris M,
Pattichis CS (2011).
Multiscale amplitude-Modulation frequency-modulation (AM–FM) texture analysis of multiple sclerosis in brain MRI images.
IEEE Trans Info Tech Biomed 15: 119-128.
20. Drabycz S,
Mitchell JR (2008).
Texture quantification of medical images using a novel complex space-frequency transform.
Int J CARS 3: 465-475.