BI-RADS 3-5 microcalcifications: prediction of lymph node metastasis of breast cancer

Abstract

PurposeTo determine whether the clinicopathological parameters and Breast Imaging Reporting and Data System (BI-RADS) 3–5 microcalcifications differed between lymph node positive (LN (+)) and lymph node negative (LN (−)) invasive ductal carcinoma (IDC).ResultsFor microcalcification-associated breast cancers, seven selected features (age, tumor size, Ki-67 status, lymphovascular invasion, calcification range, calcification diameter and calcification density) were significantly associated with LN status (all P < 0.05). Multivariate logistic regression analysis found that three risk factors (age: older vs. younger OR: 0.973 P = 0.006, tumor size: larger vs. smaller OR: 1.671, P < 0.001 and calcification density: calcifications > 20/cm2 vs. calcifications ≤ 20/cm2 OR: 1.698, P < 0.001) were significant independent predictors. This model had an area under the receiver operating characteristic curve (AUC) of 0.701. The nodal staging (N0 and N1 χ2 = 5.701, P = 0.017; N0 and N2 χ2 = 6.614, P = 0.013) was significantly positively associated with calcification density. The luminal B subtype had the highest risk of LN metastasis. Multivariate analysis demonstrated that calcification > 2 cm in range (OR: 2.209) and larger tumor size (OR: 1.882) were independently predictive of LN metastasis in the luminal B subtype (AUC = 0.667).Materials and MethodsMammographic images of 419 female breast cancer patients were included. Associations between the risk factors and LN status were evaluated using a Chi-square test, ANOVA and binary logistic regression analysis.ConclusionsThis study found that age, tumor size and calcifications density can be conveniently used to facilitate the preoperative prediction of LN metastasis. The luminal B subtype has the highest risk of LN metastasis among the microcalcification-associated breast cancers.

Is sentinel lymph node biopsy without frozen section in early stage breast cancer sufficient in accordance with ACOSOG-Z0011? A retrospective review from King Chulalongkorn Memorial Hospital

“…This is likely due to the fact that tumor sizes in the T2 group were close to the lower limit of 2 centimeters and likewise, the tumors in the T1 group were close to the upper limit of the T stage. This nding is inconsistent with a previous study which found that the rate of nodal metastases increased as the tumor size increased [24][25][26][27]. The percentage of patients with positive SLNs in the histologic grade 2 and 3 groups was higher than that in the histologic grade 1 group.…”

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

“…Given the wide implementation of mammography screening programs, mammographic images are available for all patients and can be used for preoperative diagnostics. Cen et al proposed a model that included age, postoperative pathological tumor size and microcalci cation density on mammographic images, resulting in a model with an AUC of 0.70 [19]. They revealed that a microcalci cation density >20 cm 2 was associated with a positive ALN status, similar to our ndings.…”

“…In this material we observed that decreasing radiologic tumor size was associated with N0 status. Pathological tumor size is a strong predictor for SLN status and often included in prediction models, although it is a postoperative variable [10,19,20]. These and previous results indicate that mammographic tumor size could replace pathological tumor size assessed from the surgical specimen as a predictor for N0 status.…”

“…Prediction models for ALN status using mammographic images have previously been presented, using the presence of microcalci cations, breast density, and radiomic signatures, exclusively or in combination with clinicopathological variables, most of which were postoperatively captured [19][20][21][22]. Several other imaging modalities have been assessed for their nodal predictive value, such as contrast-enhanced computed tomography (CECT), magnetic resonance imaging (MRI) and ultrasound [23][24][25][26][27].…”

Value of digital mammography in predicting lymphovascular invasion of breast cancer

“…Digital mammography is one of the important imaging tools for breast cancer screening and diagnosis. There have been many reports on prediction of axillary lymph node metastasis [3][4][5] and on digital mammography screening [6][7][8][9]. However, there has been no report on predicting LVI of breast cancer based on imaging patterns of digital mammography, except a little literature in which MRI findings were used for prediction [2,[10][11][12].…”