A histomorphologic predictive model for axillary lymph node metastasis in preoperative breast cancer core needle biopsy according to intrinsic subtypes.

Abstract

The aim of this study is construction of a pathologic nomogram that can predict axillary lymph node metastasis (LNM) for each intrinsic subtype of breast cancer with regard to histologic characteristics in breast core needle biopsy (CNB) for use in routine practice. A total of 534 CNBs with invasive ductal carcinoma classified into 5 intrinsic subtypes were enrolled. Eighteen clinicopathological characteristics and 8 molecular markers used in CNB were evaluated for construction of the best predictive model of LNM. In addition to conventional parameters including tumor multiplicity (P < .001), tumor size (P < .001), high histologic grade (P = .035), and lymphatic invasion (P = .017), micropapillary structure (P < .001), the presence of small cell-like crush artifact (P = .001), and overexpression of HER2 (P = .090) and p53 (P = .087) were proven to be independent predictive factors for LNM. A combination of 8 statistically independent parameters yielded the strongest predictive performance with an area under the curve of 0.760 for LNM. A combination of 6 independent variables, including tumor number, tumor size, histologic grade, lymphatic invasion, micropapillary structure, and small cell-like crush artifact produced the best predictive performance for LNM in luminal A intrinsic subtype (area under the curve, 0.791). Thus, adding these combinations of clinical and morphologic parameters in preoperative CNB is expected to enhance the accuracy of prediction of LNM in breast cancer, which might serve as another valuable tool in determining optimal surgical strategies for breast cancer patients.