Revisiting the modern toolkit to optimize breast conservation surgery

For patients with early breast cancer, breast conservation therapy results in higher patient satisfaction with equivalent or improved long-term survival compared to mastectomy (1,2). The risk of positive margins after lumpectomy, however, remains a problem. Reoperations after initial breast conservation surgery (BCS) often exceed 20%, compromising aesthetic results, driving up healthcare costs, and prolonging time to adjuvant therapy for tens of thousands of women each year. In the current study of more than 520,000 women identified from the National Cancer Database (NCDB); Landercasper et al., report an overall reoperation rate of 16.1 % after BCS (3). More than 1,200 facilities were included in their analysis, of which, only 1 in 4 facilities had a reoperation rate under 10%, the European quality benchmark (4). On multivariate analysis, Facility ID was the variable most significantly associated with reoperation. Notably, there was a four-fold variation in reoperation rates between facilities at the 10th and 90th percentile, a difference not explained by facility volume, lumpectomy rate or case mix (3). With their elegant use of the NCDB, Landercasper et al., have demonstrated the true scale of this problem for the first time. Their results confirm those from McCahill et al. in 2012, who showed significant variation in postlumpectomy reoperation rates between 4 affiliated institutions (5). This earlier work found the hospital facility itself to be the most important determinant for reoperation, regardless of margin status after initial BCS. Yet, in both studies, the root cause for interfacility variation could not be delineated from the available data, suggesting important variables were left unmeasured. Retrospective studies using large aggregate datasets with standardized variable fields are not sufficiently detailed to answer disease-specific research questions, often limiting a study’s conclusions. For example, breast density was recently shown to have a significant impact on reoperation rates after lumpectomy (6), but this variable was not included in the studies by Landercasper or McCahill. Likewise, individual differences in decision-making and operative technique among breast surgeons, can cause significant variability in reoperation rates within a given facility (7), yet these factors are not captured in the aforementioned datasets. Educating individual breast surgeons for improvement is arguably the most effective way to decrease both the overall rate of reoperations after lumpectomy, and the wide interfacility variation. Importantly, there are several methods for a surgeon to improve on this benchmark (8). Here , we revisit some of the tools to help minimize rates of reoperation after BCS.