BACKGROUND: This study evaluated breast imaging procedures for predicting pathologic complete response (pCR  =  ypT0) after neoadjuvant chemotherapy (NACT) for @entity0 to challenge surgery as a diagnostic procedure after NACT. METHODS: This retrospective, exploratory, monocenter study included 150 invasive @entity0 treated by NACT. The @entity1 received magnetic resonance imaging (MRI), mammography (MGR), and ultrasound (US). The results were classified in three response subgroups according to response evaluation criteria in solid @entity5 . To incorporate specific features of MRI and MGR, an additional category [clinical near complete response (near-cCR)] was defined. @entity5 in imaging and pathology was defined as a positive result. Negative predictive values (NPVs), false-negative rates (FNRs), and false-positive rates (FPRs) of all imaging procedures were analyzed for the whole cohort and for @entity549 ( @entity549 ), @entity1722 -positive ( @entity1722 +), and @entity1722 -negative/hormone-receptor-positive ( @entity1722 -/HR+) cancers, respectively. RESULTS: In 46 cases (31  %), pCR (ypT0) was achieved. Clinical complete response (cCR) and near-cCR showed nearly the same NPVs and FNRs. The NPV was highest with 61  % for near-cCR in MRI and lowest with 44  % for near-cCR in MGR for the whole cohort. The FNRs ranged from 4 to 25  % according to different imaging methods. The MRI performance seemed to be superior, especially in @entity5 (NPV 94  %; FNR 5  %). The lowest FPR was 10  % in MRI, and the highest FPR was 44  % in US. CONCLUSION: Neither MRI nor MGR or US can diagnose a pCR (ypT0) with sufficient accuracy to replace pathologic diagnosis of the surgical excision specimen.

Can Routine Imaging After Neoadjuvant Chemotherapy in XXXX Predict Pathologic Complete Response?

@entity1 :: ('9606', 'Species') :: ['patients'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancers', 'breast cancer'] @entity5 :: ('MESH:D009369', 'Disease') :: ['Residual cancer', 'TN cancers', 'tumors'] @entity549 :: ('MESH:D064726', 'Disease') :: ['triple-negative', 'TN'] @entity1722 :: ('2064', 'Gene') :: ['HER2']
@entity0 :: (MESH:D001943,Disease) :: ['Breast Cancer']
