Vacuum-Assisted Breast Biopsy After Neoadjuvant Systemic Treatment for Reliable Exclusion of Residual Cancer in Breast Cancer Patients

Abstract

Background About 40 % of women with breast cancer achieve a pathologic complete response in the breast after neoadjuvant systemic treatment (NST). To identify these women, vacuum-assisted biopsy (VAB) was evaluated to facilitate risk-adaptive surgery. In confirmatory trials, the rates of missed residual cancer [false-negative rates (FNRs)] were unacceptably high (> 10%). This analysis aimed to improve the ability of VAB to exclude residual cancer in the breast reliably by identifying key characteristics of false-negative cases. Methods Uni- and multivariable logistic regressions were performed using data of a prospective multicenter trial (n = 398) to identify patient and VAB characteristics associated with false-negative cases (no residual cancer in the VAB but in the surgical specimen). Based on these findings FNR was exploratively re-calculated. Results In the multivariable analysis, a false-negative VAB result was significantly associated with accompanying ductal carcinoma in situ (DCIS) in the initial diagnostic biopsy [odds ratio (OR), 3.94; p < 0.001], multicentric disease on imaging before NST (OR, 2.74; p = 0.066), and age (OR, 1.03; p = 0.034). Exclusion of women with DCIS or multicentric disease (n = 114) and classication of VABs that did not remove the clip marker as uncertain representative VABs decreased the FNR to 2.9% (3/104). Conclusion For patients without accompanying DCIS or multicentric disease, performing a distinct representative VAB (i.e., removing a well-placed clip marker) after NST suggests that VAB might reliably exclude residual cancer in the breast without surgery. This evidence will inform the design of future trials evaluating risk-adaptive surgery for exceptional responders to NST.

Minimally Invasive Breast Biopsy After Neoadjuvant Systemic Treatment to Identify Breast Cancer Patients with Residual Disease for Extended Neoadjuvant Treatment: A New Concept

“… 21 The low sensitivity (high rate of missed residual cancer) of VAB alone is in line with our previous research, which showed that for the reliable exclusion of residual cancer, VAB should be combined with imaging and specific patient selection criteria. 9 , 22 Recent studies showed that an ‘intelligent VAB’, a machine learning algorithm analyzing VAB variables alongside clinical and patient information, can reliably exclude residual cancer after NAST; these patients without residual disease might be spared breast and axillary surgery. 10 , 23 Thus, future trials may evaluate oncologic outcomes of response assessment to NAST via VAB within three patient groups (Fig.…”

Artificial intelligence to de-escalate loco-regional breast cancer treatment

“…False-negative rates (FNR, rate of missed cancer in the biopsy compared to the surgical specimen) were higher than anticipated with 18–50% [ [31] , [32] , [33] , [34] ]. Subgroup analyses showed that the biopsies mainly miss small, heterogenous responding tumor foci and suggested lower FNRs when specific patient selection criteria were applied (tumor size after NAST <2 cm, at least six biopsy samples, biopsy sample representative of tumor bed, and triple-negative or HER2+ breast cancer) [ 31 , 32 , 35 ]. Based on these results, a multi-modal machine learning algorithm using patient, tumor, imaging, and biopsy variables (“intelligent vacuum-assisted biopsy (VAB)”) was developed.…”

De-escalating Surgery Among Patients with HER2 + and Triple Negative Breast Cancer

“…On recent further analysis of the same data, age and presence of DCIS were found to be associated with the FNR. In a selected sub-cohort of patients with unicentric disease, not associated with DCIS and a representative VAB, the FNR was 2.9% [ 23 ].…”