Correlation of perfusion parameters on dynamic contrast-enhanced MRI with prognostic factors and subtypes of breast cancers

Journal of Magnetic Resonance Imaging

Volumn 36, Issue 1, 2012, Pages 145-151

  Koo, Hye Ryoung ^a   Cho, Nariya ^a   Song, In Chan ^a   Kim, Hyeonjin ^b   Chang, Jung Min ^a   Yi, Ann ^a   Yun, Bo La ^a   Moon, Woo Kyung ^a  

^a Seoul National University Hospital   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

angiogenesis; breast cancer; dynamic contrast enhanced MRI; prognostic factors

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR 2; ESTROGEN RECEPTOR; GADOBUTROL; KI 67 ANTIGEN; PROGESTERONE RECEPTOR; PROTEIN BCL 2; PROTEIN P53;

ADULT; AGED; ARTICLE; AXILLARY LYMPH NODE; BREAST CANCER; BREAST CARCINOMA; CANCER GRADING; CANCER PROGNOSIS; CONTRAST ENHANCEMENT; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMMUNOHISTOCHEMISTRY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; TUMOR VOLUME;

ADULT; AGED; ALGORITHMS; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE ANGIOGRAPHY; MIDDLE AGED; NEOVASCULARIZATION, PATHOLOGIC; PROGNOSIS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STATISTICS AS TOPIC;

EID: 84862739562     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.23635     Document Type: Article

References (32)

1. Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology 1999; 211: 101-110. (Pubitemid 29161739)
2. Orel SG, Schnall MD,. MR imaging of the breast for the detection, diagnosis, and staging of breast cancer. Radiology 2001; 220: 13-30. (Pubitemid 32577955)
3. Szab B, Aspelin P, Kristoffersen Wiberg M, Tot T, Bon B,. Invasive breast cancer: correlation of dynamic MR features with prognostic factors. Eur Radiol 2003; 13: 2425-2435. (Pubitemid 37407473)
4. Jinguji M, Kajiya Y, Kamimura K, et al. Rim enhancement of breast cancers on contrast-enhanced MR imaging: relationship with prognostic factors. Breast Cancer 2006; 13: 64-73.
5. Teifke A, Behr O, Schmidt M, et al. Dynamic MR imaging of breast lesions: correlation with microvessel distribution pattern and histologic characteristics of prognosis. Radiology 2006; 239: 351-360.
6. Lee S, Cho N, Kim S, et al. Correlation between high resolution dynamic MR features and prognostic factors in breast cancer. Korean J Radiol 2008; 9: 10-18.
7. Stomper PC, Herman S, Klippenstein DL, et al. Suspect breast lesions: findings at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features. Radiology 1995; 197: 387-395.
8. Fischer U, Kopka L, Brinck U, Korabiowska M, Schauer A, Grabbe E,. Prognostic value of contrast-enhanced MR mammography in patients with breast cancer. Eur Radiol 1997; 7: 1002-1005. (Pubitemid 127537120)
9. Li SP, Padhani AR, Taylor NJ, et al. Vascular characterisation of triple negative breast carcinomas using dynamic MRI. Eur Radiol 2011; 21: 1364-1373.
10. Tofts PS,. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997; 7: 91-101. (Pubitemid 28365643)
11. Tofts PS, Brix G, Buckley DL, 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: 223-232. (Pubitemid 29463104)
12. Yankeelov TE, Gore JC,. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples. Curr Med Imaging Rev 2009; 3: 91-107. (Pubitemid 46773870)
13. Pickup S, Zhou R, Glickson J,. MRI estimation of the arterial input function in mice. Acad Radiol 2003; 10: 963-968. (Pubitemid 37071378)
14. Loveless ME, Halliday J, Liess C, et al. A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magn Reson Med 2012; 67: 226-236.
15. Rausch DR, Hendrick RE,. How to optimize clinical breast MR imaging practices and techniques on your 1.5-T system. Radiographics 2006; 26: 1469-1484. (Pubitemid 44518775)
16. Mussurakis S, Buckley DL, Horsman A,. Dynamic MR imaging of invasive breast cancer: correlation with tumour grade and other histological factors. Br J Radiol 1997; 70: 446-451. (Pubitemid 27229263)
17. Baltzer PA, Vag T, Dietzel M, et al. Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer. Eur Radiol 2010; 20: 1563-1571.
18. Su MY, Cheung YC, Fruehauf JP, et al. 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: 467-477. (Pubitemid 37187600)
19. Ah-See ML, Makris A, Taylor NJ, et al. Early changes in functional dynamic magnetic resonance imaging predict for pathologic response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res 2008; 14: 6580-6589.
20. Thukral A, Thomasson DM, Chow CK, et al. Inflammatory breast cancer: dynamic contrast-enhanced MR in patients receiving bevacizumab-initial experience. Radiology 2007; 244: 727-735. (Pubitemid 47292083)
21. Li SP, Makris A, Beresford MJ, et al. Use of dynamic contrast-enhanced MR imaging to predict survival in patients with primary breast cancer undergoing neoadjuvant chemotherapy. Radiology 2011; 260: 68-78.
22. Pickles MD, Manton DJ, Lowry M, Turnbull LW,. 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: 498-505.
23. van Dijke CF, Brasch RC, Roberts TP, et al. Mammary carcinoma model: correlation of macromolecular contrast-enhanced MR imaging characterizations of tumor microvasculature and histologic capillary density. Radiology 1996; 198: 813-818. (Pubitemid 26066303)
24. Dvorak HF, Brown LF, Detmar M, Dvorak AM,. Vascular permeability factor/vascular endothelial growth factor, microvascular hyperpermeability, and angiogenesis. Am J Pathol 1995; 146: 1029-1039.
25. Schlemmer HP, Merkle J, Grobholz R, et al. Can pre-operative contrast-enhanced dynamic MR imaging for prostate cancer predict microvessel density in prostatectomy specimens? Eur Radiol 2004; 14: 309-317. (Pubitemid 38181089)
26. Fritz-Hansen T, Rostrup E, Larsson HB, Sondergaard L, Ring P, Henriksen O,. Measurement of the arterial concentration of Gd-DTPA using MRI: a step toward quantitative perfusion imaging. Magn Reson Med 1996; 36: 225-231. (Pubitemid 26271742)
27. Ashton E, Raunig D, Ng C, Kelcz F, McShane T, Evelhoch J,. Scan-rescan variability in perfusion assessment of tumors in MRI using both model and data-derived arterial input functions. J Magn Reson Imaging 2008; 28: 791-796.
28. Padhani AR, Hayes C, Landau S, Leach MO,. Reproducibility of quantitative dynamic MRI of normal human tissues. NMR Biomed 2002; 15: 143-153. (Pubitemid 34213404)
29. Chen J, Yao J, Thomasson D,. Automatic determination of arterial input function for dynamic contrast enhanced MRI in tumor assessment. Med Image Comput Comput Assist Interv 2008; 11 (Pt 1): 594-601.
30. Li X, Welch EB, Arlinghaus LR, et al. A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Phys Med Biol 2011; 56: 5753-5769.
31. Parker GJ, Roberts C, Macdonald A, et al. Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magn Reson Med 2006; 56: 993-1000. (Pubitemid 44691341)
32. Rose CJ, Mills SJ, O'Connor JP, et al. Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps. Magn Reson Med 2009; 62: 488-499.