谷歌浏览器插件
订阅小程序
在清言上使用

A prospective cohort study identifying radiologic and tumor related factors of importance for breast conserving surgery after neoadjuvant chemotherapy

K. Gulis,J. Ellbrant,T. Svensjö, I. Skarping, J. Vallon-Christersson,N. Loman,P.O. Bendahl,L. Rydén

European Journal of Surgical Oncology(2023)

引用 0|浏览2
暂无评分
摘要
INTRODUCTION:Neoadjuvant chemotherapy (NAC) is an established treatment option for early breast cancer, potentially downstaging the tumor and increasing the eligibility for breast-conserving surgery (BCS). The primary aim of this study was to assess the rate of BCS after NAC, and the secondary aim was to identify predictors of application of BCS after NAC. MATERIALS AND METHODS:This was an observational prospective cohort study of 226 patients in the SCAN-B (Clinical Trials NCT02306096) neoadjuvant cohort during 2014-2019. Eligibility for BCS was assessed at baseline and after NAC. Uni- and multivariable logistic regression analyses were performed using covariates with clinical relevance and/or those associated with outcome (BCS versus mastectomy), including tumor subtype, by gene expression analysis. RESULTS:The overall BCS rate was 52%, and this rate increased during the study period (from 37% to 52%). Pathological complete response was achieved in 69 patients (30%). Predictors for BCS were smaller tumor size on mammography, visibility on ultrasound, histological subtype other than lobular, benign axillary status, and a diagnosis of triple-negative or HER2-positive subtype, with a similar trend for gene expression subtypes. Mammographic density was negatively related to BCS in a dose-response pattern. In the multivariable logistic regression model, tumor stage at diagnosis and mammographic density showed the strongest association with BCS. CONCLUSION:The rate of BCS after NAC increased during the study period to 52%. With modern treatment options for NAC the potential for tumor response and BCS eligibility might further increase.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要