Annals of Oncology Advance Access originally published online on March 5, 2008
Annals of Oncology 2008 19(4):822-824; doi:10.1093/annonc/mdn043
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Can dynamic contrast-enhanced MRI (DCE-MRI) predict tumor recurrence and lymph node status in patients with breast cancer?
The lymph node status is regarded as one of the most important prognostic factors for the overall and disease-free survival of patients with breast cancer. While morphological features and contrast enhancement kinetics of breast cancer shown on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been correlated with tumor histological type, grade, and biomarkers [1–4], there were only few studies reporting the association of lesion features such as rim enhancement and kinetic feature (early maximal enhancement and washout) with node status and showed controversial results [5–7]. In this study, we investigated the MRI features of the primary tumor between patients who had early recurrence versus those who remained cancer free and also between node-positive and -negative patients.We analyzed 62 patients (30–83 years old, median 58) with histologically confirmed breast cancer who were enrolled into a breast MRI study during years 2000–2003. A telephone survey was conducted in 2006 to follow-up all patients regarding their disease status. Of the 62 patients, six had confirmed cancer recurrence in the previously treated breast. The MRI features of these 62 patients were retrospectively reviewed and compared between the six with early recurrence versus those who were cancer free. Of these six patients with early recurrence, three had positive nodes (sentinel and/or axillary) at the time of first cancer diagnosis and three had negative nodes. Of the 56 patients who were cancer free, 28 had positive node and the other 28 had negative nodes.
Breast MRI was carried out on a 1.5T MR scanner. The protocol included precontrast images and dynamic contrast-enhanced imaging. The characteristics of primary tumor were analyzed. The longest and perpendicular dimension of the tumor size was measured on contrast-enhanced MRI and then converted to one-dimensional size. The morphological appearances were characterized using features described in BI-RADS MRI lexicon [8], separated into mass lesions and nonmass-like enhancements. The following enhancement kinetic parameters were analyzed: the % enhancement at 1 min (E1), 2 min (E2), 7 min (E3), and the washout slope between 7 and 2 min. Furthermore, pharmacokinetic parameters, including transfer constant (Ktrans) and exchange rate constant (kep), were also analyzed with the Toft's two-compartmental model [9].
The comparison of lesion morphology, size, and enhancement kinetic parameters in three groups was summarized in Table 1. LN(+) group had more irregular mass lesion (19/28, 68%) compared with LN(–) group (12/28, 43%), fewer round mass (4/28, 14% versus 10/28, 36%), and more nonmass-like lesions (3/28 versus 0/28). The tumor size in the LN(+) group (0.7–4.0 cm, mean 1.8 cm) was bigger compared with that in the LN(–) group (0.5–3.0 cm, mean 1.5 cm), but not significant (P = 0.12). The % enhancements at 1 min (E1) and 2 min (E2) were about the same, but the washout slope was significantly faster in the LN(+) group (P = 0.02). After analyzing the enhancement kinetics with the pharmacokinetic model, the parameter Ktrans was higher in the LN(+) group, with borderline significance (P = 0.07). Similar as the washout slope, the kep was faster in the LN(+) group (0.48 versus 0.38 1/min, P = 0.01), indicating significant differences between the two groups. The mean values of these parameters in the recurrence group are also listed in the table. Since only six cases were found in the recurrence group, none of the parameters showed significant difference compared with the other two groups. Thus, the MRI morphology or enhancement kinetics could not predict recurrence.
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Noninvasive MRI may evaluate the tumor angiogenic activity. On the basis of the fact that angiogenesis is required to support cancer growth and metastasis, it may be hypothesized that a tumor with higher angiogenesis may facilitate the spread of cancer cells, and thus is more likely to show lymph node metastasis and earlier recurrence. We found that LN(+) group had a stronger washout and faster kep than the LN(–) cohort, suggesting that invasive breast cancers with higher angiogenesis are more likely to have positive nodes. Our results were consistent with that of Tuncbilek et al. [6], which showed correlation of lymph node metastasis and washout slope of the primary tumor.
In six recurrent patients, three showed positive and three showed negative nodes. Three patients with negative lymph nodes developed early recurrence within 4 years. The uncommon clinical presentation was more likely due to substandard treatment, or the primary tumor expressing highly aggressive biomarkers or genetic markers. The development in oncogene profiling in the last decade has slowly been established as clinical tools. For example, Oncotype DX® on the basis of 21 genes has been shown to predict 10-year distant recurrence in patients with estrogen receptor-positive, axillary lymph node-negative breast cancer [10, 11]. The wide spread use of these tools is expected to provide additional prognostic predictors other than the traditional tumor staging and node status to determine the optimal management for each patient. In addition to help deciding which node-negative patients should receive chemotherapy and who can be spared, these genomic assays may predict the response to chemotherapy and endocrine therapy and help to select the optimal treatment [12, 13].
In summary, although the angiogenic activity of breast cancer evaluated by DCE-MRI was significantly higher in node-positive compared with node-negative patients, it is not sensitive to accurately predict the node status. In our cohort of 62 subjects, the angiogenic activity could not predict which patient would have early recurrence. Further development of genetic profile, biomarkers, and imaging markers may be combined to evaluate each specific cancer from different aspects and to develop the most accurate prognostic predictors [14]. Such predictors will further impact on selection of optimal management plan, contributing to ‘personalized medicine’ for each individual patient.
funding
National Institutes of Health/National Cancer Institute (R01 CA90437 and R21 CA121568 [GenBank] ); California California Breast Cancer Research Program (9WB-0020).
1 Tu and Yuan Center for Functional Onco-Imaging, University of California, Irvine, CA, USA
2 Department of Radiology, China Medical University Hospital, Taichung, Taiwan
* (E-mail: jeonhc{at}uci.edu)
References
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.[CrossRef][Web of Science][Medline]
2. 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.
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.[CrossRef]
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. Invasive breast cancer: correlation of dynamic MR features with prognostic factors. Eur Radiol (2003) 13:2425–2435.[CrossRef][Web of Science][Medline]
6. Tuncbilek N, Karakas HM, Okten OO. Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers. Eur J Radiol (2005) 53(2):199–205.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
8. American College of Radiology. Breast Imaging Reporting and Data System Atlas (BI-RADS atlas) (2003) Reston, VA: American College of Radiology.
9. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging (1997) 7:91–101.[Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
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.[CrossRef][Web of Science][Medline]
13. Morris SR, Carey LA. Gene expression profiling in breast cancer. Curr Opin Oncol (2007) 19(6):547–551.[Web of Science][Medline]
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.
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