Dynamic-enhanced MRI predicts metastatic potential of invasive ductal breast cancer View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2002-07

AUTHORS

Takeshi Nagashima, Masato Suzuki, Hiroshi Yagata, Hideyuki Hashimoto, Tomotane Shishikura, Nobuhiro Imanaka, Takuya Ueda, Masaru Miyazaki

ABSTRACT

BACKGROUND: Dynamic magnetic resonance imaging (MRI) has improved the detection of breast malignancies. The method is based on estimating the velocity of contrast enhancement taking into account increased angiogenesis in tumor. Microvessel density correlates with breast carcinoma metastasis. Thus, we hypothesized that contrast enhancement on MRI correlates with metastasis in breast cancer patients. The present study attempts to clarify the quantitative assessment of dynamic data, and examines the correlation between MRI enhancement and breast carcinoma metastasis. METHODS: The subjects consisted of 31 patients with invasive ductal breast cancer. Twenty patients were disease free for five years (group A), and eleven patients suffered from metastatic disease at distant sites concurrently or postoperatively (group B). Dynamic MRI was performed preoperatively using a 1.5T system in all cases. Using the dynamic data, the signal intensity (SI)ratio and SI index were determined and analyzed retrospectively taking into account the presence of distant metastases. RESULTS: The values of the SI ratio were 2.2+/-0.7 in group A and 2.3+/-0.4 in group B, respectively, with no significant difference seen between the groups. The SI index value was significantly higher in group B (28.5+/-32.8) than in group A (10.3+/-5.5, p<0.05). CONCLUSIONS: The current series suggests that the SI index could distinguish patients with high risk of distant metastasis from disease free patients, preoperatively. If a suitable borderline value were established, the quantitative dynamic parameter determined by MRI may be useful for predicting the prognosis of breast cancer patients. More... »

PAGES

226

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02967594

DOI

http://dx.doi.org/10.1007/bf02967594

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030731203

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12185334


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