Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?

Annals of Oncology

Volumn 19, Issue 4, 2008, Pages 822-824

  Bahri, S ^a   Chen, Jeon H ^a,b   Yu, H J ^a   Kuzucan, A ^a   Nalcioglu, O ^a   Su, M Y ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CHINA MEDICAL UNIVERSITY HOSPITAL   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; BREAST CANCER; CANCER RECURRENCE; COMPUTER PREDICTION; CONTRAST ENHANCEMENT; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; EARLY DIAGNOSIS; ECHOMAMMOGRAPHY; HEALTH SURVEY; HUMAN; LYMPH NODE METASTASIS; MAJOR CLINICAL STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROGNOSIS; TELEPHONE; TUMOR VOLUME;

ADULT; AGED; AGED, 80 AND OVER; AXILLA; BREAST NEOPLASMS; FEMALE; FOLLOW-UP STUDIES; HUMANS; LYMPH NODES; LYMPHATIC METASTASIS; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; NEOPLASM RECURRENCE, LOCAL; NEOVASCULARIZATION, PATHOLOGIC; PREDICTIVE VALUE OF TESTS; QUESTIONNAIRES; SENSITIVITY AND SPECIFICITY; TELEPHONE;

EID: 41549095829     PISSN: 09237534     EISSN: 15698041     Source Type: Journal    
DOI: 10.1093/annonc/mdn043     Document Type: Article

References (14)

1. Montemurro F, Martincich L, Sarotto I et al. Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer. Eur Radiol 2007; 17: 1490-1497.
2. Stomper PC, Herman S, Kippenstein DL et al. Suspect breast lesions: findings at dynamic gadolinium-enhanced MR imaging correlated with mammographic and pathologic features. Radiology 1995; 197: 387-395.
3. Agrawal G, Chen JH, Baik HM et al. MR imaging features of breast cancer: a correlation study with HER-2 receptor. Ann of Oncol 2007; 18(11): 1903-1904.
4. Chen JH, Agrawal G, Feig B et al. Triple negative breast cancer: MR imaging features in 29 patients. Ann Oncol 2007 18(12): 2042-2043.
5. Szabo BK, Aspelin P, Kristoffersen WM et al. Invesive breast cancer: correlation of dynamic MR features with prognostic factors. Eur Radiol 2003; 13: 2425-2435.
6. Tuncbilek N, Karakas HM, Okten OO. Dynamic magnetic resonance imaging in determining histopathological prognestic factrs of invasive breast cancers. Eur J Radiol 2005; 53(2): 199-205.
7. Fischer U, Kopka L, Brinck U et al. Prognostic value of contrast-enhanced MR mammography in patients with breast cancer. Eur Radiol 1997; 7(7): 1002-1005.
8. American College of Radiology. Breast Imaging Reporting and Data System Atlas (BI-RADS altas). Reston, VA American College of Radiology, 2003.
9. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging. 1997; 7: 91-101.
10. Kaklamani V. A genetic signature can predict prognosis and response to therapy in breast cancer: Oncotype DX. Expert Rev Mol Diagn 2006; 6(6): 803-809.
11. Dobbe E, Gurney K, Kiekow S et al. Gene-expression assays: New tools to individualize treatment of early-stage breast cancer. Am J Health Syst Pharm 2008; 65(1): 23-28.
12. Mina L, Soule SE, Badve S et al. Predicting response to primary chemotherapy: Gene expression profiling of paraffin-embedded core biopsy tissue. Breast Cancer Res Treat 2007; 103(2): 197-208.
13. Morns SR, Carey LA. Gene expression profiling in breast cancer. Curr Opin Oncol 2007; 19(6): 547-551.
14. Harris L, Fritsche H, Mennel R et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007; 25(33): 5287-5312.