Effect of taxane‐based neoadjuvant chemotherapy on fibroglandular tissue volume and percent breast density in the contralateral normal breast evaluated by 3T MR

Abstract

The aim of this study was to evaluate the change of breast density in the normal breast of patients receiving neoadjuvant chemotherapy (NAC). Forty-four breast cancer patients were studied. MRI acquisition was performed before treatment (baseline), and 4 and 12 weeks after treatment. A computer algorithm-based program was used to segment breast tissue and calculate breast volume (BV), fibroglandular tissue volume (FV) and percent density (PD) (the ratio of FV over BV x100%). The reduction of FV and PD after treatment was compared to baseline using paired t-tests with a Bonferroni-Holm correction. The association of density reduction with age was analyzed. FV and PD after NAC showed significant decreases compared to the baseline. FV was 110.0ml (67.2, 189.8) (geometric mean (interquartile range)) at baseline, 104.3ml (66.6, 164.4) after 4 weeks (p< 0.0001), and 94.7ml (60.2, 144.4) after 12 weeks (comparison to baseline, p<0.0001; comparison to 4 weeks, p=0.016). PD was 11.2% (6.4, 22.4) at baseline, 10.6% (6.6, 20.3) after 4 weeks (p< 0.0001), and 9.7% (6.2, 17.9) after 12 weeks (comparison to baseline, p=0.0001; comparison to 4 weeks, p =0.018). Younger patients tended to show a higher density reduction, but overall correlation with age was only moderate (r=0.28 for FV, p=0.07 and r=0.52 for PD, p=0.0003). Our study showed that breast density measured from MR images acquired at 3T MR can be accurately quantified using a robust computer-aided algorithm based on nonparametric nonuniformity normalization (N3) and an adaptive fuzzy C-means algorithm. Similar to doxorubicin and cyclophosphamide regimens, the taxane-based NAC regimen also caused density atrophy in the normal breast and showed reduction in FV and PD. The effect of breast density reduction was age-related and duration-related.

Significant association of combination of OCT4, NANOG, and SOX2 gene polymorphisms in susceptibility and response to treatment in North Indian breast cancer patients

“…Recurrence of the disease is one of the major reasons of breast cancer mortality. Previous studies have extensively studied the role of hormone receptors, HER2/neu, and lymph node status in breast cancer [10,14,30,39,50]. However, recent literature has demonstrated the role of embryonic cancer stem cells (ESCs) in tumorigenesis and response to treatment in different types of tumors [17].…”

“…LIN28 is one such marker [61]. This RNA-binding protein was initially identified as an important regulator of developmental timing in Caenorhabditis elegans [10]. Overexpression of LIN28 is known to promote cancer cell proliferation by inhibiting the biogenesis of microRNA let-7 [37,60].…”

Signaling Pathways in the Development of Infantile Hemangioma

“…Possible reasons of breast cancer mortality are tumor dormancy after treatment followed by local, regional or distant recurrence. Several factors as hormone receptors, HER2 status, proliferation fraction predicting outcome in preoperative setting were extensively examined in previous studies (Chen et al 2013;Denkert et al 2010;Lips et al 2013;Payne et al 2008;Teleki et al 2013;van Nes et al 2012;Varga et al 2005;Yoshioka et al 2013). The role of transcription factors (TF) in breast cancer prognosis has been the subject of some previous studies (Ablett et al 2012;Cimino-Mathews et al 2013;Giordano et al 2012;Guo et al 2012;Mego et al 2012).…”

Impact of positional difference on the measurement of breast density using MRI

“…The value of k was chosen based on our past experience analyzing many cases. 6,15,16,21,23,[25][26][27][28][29] For extremely fatty breasts, the number of clusters may need to be adjusted. However, for Asian women with relatively dense breasts, k = 6 (three for dense and three for fatty tissues) has worked very well.…”

“…It has been shown that quantitative 3D MR-based analysis of breast density has the potential to provide an imaging biomarker for assessing cancer risk 22,24 or to predict therapeutic efficacy. 15,16,25 In order to serve as a reliable imaging biomarker, factors affecting the reproducibility in density measurement should be considered. Our group has devoted a substantial research effort to develop and refine a comprehensive 3D MR-based density method.…”