scholarly journals The transcription factor KLF4 as an independent predictive marker for pathologic complete remission in breast cancer neoadjuvant chemotherapy: a case–control study

2014 ◽  
pp. 1963 ◽  
Author(s):  
Min Jun Dong ◽  
Wang Lin Bo ◽  
Zhi Nong Jiang ◽  
Mei Jin ◽  
Wen Xian Hu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factor ◽  
Neoadjuvant Chemotherapy ◽  
Complete Remission ◽  
Predictive Marker ◽  
Pathologic Complete Remission ◽  
Control Study

Association between mammographic breast density and achievement of a pathologic complete remission (pCR) after neoadjuvant chemotherapy (NACT) for early breast cancer (EBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12611-e12611
Author(s):  
Cornelia Kolberg-Liedtke ◽  
Mohamed Shaheen ◽  
Oliver Hoffmann ◽  
Ann-Kathrin Bittner ◽  
Sarah Wetzig ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Complete Remission ◽  
Early Breast Cancer ◽  
Breast Density ◽  
Breast Imaging ◽  
Tumor Biology ◽  
Small Sample ◽  
Mammographic Breast Density ◽  
Pathologic Complete Remission

e12611 Background: Neoadjuvant chemotherapy (NACT) is indicated in early breast cancer (EBC) with an unfavorable tumor biology. Achievement of pathologic complete remission (pCR) after NACT is indicating an improved prognosis. An association between pCR and mammographic breast density as defined by BIRADS (Breast Imaging Reporting and Data System) could be demonstrated. However, the definition of mammographic breast density by the American College of Radiology (ACR) is widely used worldwide and data regarding an association of breast density by this definition and pCR after NACT are missing. Methods: We conducted a retrospective analysis among patients who had received neoadjuvant chemotherapy (NACT) for EBC and had available data regarding mammographic breast density as defined by ACR before therapy, pCR, age, estrogen and progesterone receptor (ER, PR) status, HER2neu status and grading were included. An association between mammographic breast density (ACR) and pCR was analyzed. Results: 185 patients were included in this analysis, 35.7% of whom achieved a pCR. Mammographic breast density was ACR 1 in 15.1%, ACR 2 in 41.6%, ACR3 in 38.4% and ACR 4 in 4.9% of the patients. A negative correlation (Spearman-Rho) between mammographic breast density and pCR (correlation coefficient (CC) -0.240) was highly statistically significant (p = 0.001). The association of decreasing pCR rates with increasing mammographic breast density (pCR rates by ACR 1: 53.6%, ACR 2: 41.6, ACR 3: 25.4% and 11.1 %) was statistically significant (Chi-Square, p = 0.013). These results were independent of age, ER status, PR status, HER2neu status and grading. Conclusions: In our analysis higher mammographic breast density as defined by ACR was significantly correlated with a lower chance of achieving a pCR after NACT. Although this result has to be interpreted with caution due to the small sample size and the retrospective character of our investigation, it is completely in line with other investigations using other definitions of mammographic breast density. The pathophysiological cause of this association should be further elucidated to reveal potential mechanisms of treatment resistance.

Shear Wave Elastography Combined with Molecular Subtype in Early Predicting Response to Neoadjuvant Chemotherapy for Breast Cancer: A Prospective Case-control Study

2021 ◽  
Author(s):  
Jia-Xin Huang ◽  
Shi-Yang Lin ◽  
Yan Ou ◽  
Cai-Gou Shi ◽  
Yuan Zhong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Case Control Study ◽  
Molecular Subtype ◽  
Shear Wave Elastography ◽  
Predictive Performance ◽  
Case Control ◽  
Pathological Response ◽  
Control Study ◽  
Prospective Case Control Study

Abstract Background: This study was designed to investigate the performance of quantitative shear wave elastography (SWE) and the accuracy of SWE with the molecular subtype for early prediction of pathological response of breast cancer to neoadjuvant chemotherapy (NAC).Methods: In this prospective case-control study, 102 patients were screened from September 2016 to August 2020. Characteristics of conventional ultrasonography (US), SWE and contrast-enhanced magnetic resonance imaging (CE-MRI), were recorded, and the changes were compared to the pre-NAC baseline data. The pathological response was classified according to the Miller Payne grading system. Multivariate logistic regression was used to develop a predictive model for the response to NAC. Results: Significant differences related to changes in SWE characteristics of breast lesions between between pathological response groups were observed earlier than size on the conventional US and MRI images. According to the multivariate predictive model, the best parameter for predicting the pathological response after the first cycle of NAC was the molecular subtype of the tumor [area under the curve (AUC) = 0.83] with low sensitivity (66.04%). Better predictive performance was achieved when △AE/B and the molecular subtype were applied in combination after the second cycle of NAC (AUC = 0.92) with a higher sensitivity (86.79%). The predictive performance of molecular subtype combined with △AE/B and △SWVmean after the third and fourth cycles of NAC were improved (AUC = 0.94).Conclusion: The SWE can be an early predictor of the pathological response to NAC for breast cancer. The combination of SWE and the molecular subtype may be a preferred method for the clinical evaluation of NAC to facilitate the personalization of treatment regimens for breast cancer.

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