期刊
BREAST CANCER RESEARCH AND TREATMENT
卷 83, 期 1, 页码 67-76出版社
KLUWER ACADEMIC PUBL
DOI: 10.1023/B:BREA.0000010700.11092.f4
关键词
antineoplastic agents combined; breast neoplasms; histopathological response; image processing; magnetic resonance imaging; neoadjuvant chemotherapy
类别
Purpose. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows analysis of both tumor volume and contrast enhancement pattern using a single tool. We sought to investigate whether DCE-MRI could be used to predict histological response in patients undergoing primary chemotherapy (PCT) for breast cancer. Patients and methods. Thirty patients with breast cancer, clinical diameter > 3 cm or stage III A/B, received anthracycline and taxane based PCT. DCE-MRI was performed at the baseline, after two cycles and after four cycles of PCT, before surgery. Histological response was assessed using a five-point scheme. Grade 4 ( small cluster of dispersed residual cancer cells) and grade 5 ( no residual viable cancer cell) were defined as a major histopathological response (MHR). Results. Univariate analysis showed that a > 65% reduction in the tumor volume and a reduction in the early enhancement ratio (ECU) after two cycles of PCT were associated with a MHR. Multivariate analysis revealed that tumor volume reduction after two cycles of PCT was independently associated with a MHR ( odds ratio [ OR] 39.968, 95% confidence interval [CI] 3.438 - 464.962, p < 0.01). ECU reduction was still associated with a MHR (OR 2.50, 95% CI 0.263 - 23.775), but it did not retain statistical significance ( p = 0.42). Combining tumor volume and ECU reduction after two cycles of PCT yielded a 93% diagnostic accuracy in identifying tumors achieving a pathological complete response (pCR) ( histopathological grade 5). Conclusions. DCE-MRI allows prediction of the effect of neoadjuvant chemotherapy in breast cancer. Although in our study tumor volume reduction after two cycles had the strongest predictive value, DCE-MRI has the potential to provide functional parameters that could be integrated to optimize neoadjuvant chemotherapy strategies.
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