Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer

Abstract

Background This study was aimed to establish the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors. Methods A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a training group and a validation group. Univariate and multivariate ordered logistic regression were used to determine the predictive ability of each variable. A nomogram was performed based on the factors selected from logistic regression results. Receiver operating characteristic curve (ROC) analysis, calibration plots and decision curve analysis (DCA) were used to evaluate the discriminative ability and accuracy of the models. Results Logistic regression analysis demonstrated that CEA, CA125, CA153, tumor size, vascular-invasion, calcification, and tumor grade were independent prognostic factors for positive ALNs. Integrating all the predictive factors, a nomogram was successfully developed and validated. The C-indexes of the nomogram for prediction of no ALN metastasis, positive ALN, and four and more ALN metastasis were 0.826, 0.706, and 0.855 in training group and 0.836, 0.731, and 0.897 in validation group. Furthermore, calibration plots and DCA demonstrated a satisfactory performance of our nomogram. Conclusion We successfully construct and validate the nomogram to predict patients’ axillary node status by using patients’ clinicopathological and tumor characteristic factors.

Efficacy and safety of pyrotinib combined with albumin‐bound paclitaxel as first‐line treatment for HER2‐positive metastatic breast cancer in patients previously treated with adjuvant and/or neoadjuvant trastuzumab therapy: The stage 1 results of a single‐arm, phase 2 prospective clinical trial

“… 26 , 27 , 28 It participates in the inhibition of cell differentiation and apoptosis, 29 and plays an important role in tumour metastasis. 30 A significant association was found between elevated CEA levels and tumour size, lymph node metastasis, and advanced‐stage breast cancer. 31 , 32 , 33 Studies have found that CEA levels were affected by drugs such as trastuzumab and paclitaxel.…”

“…CEACAM5, also known as carcinoembryonic antigen (CEA), has been reported to be overexpressed in several cancers, especially colon cancer 26–28 . It participates in the inhibition of cell differentiation and apoptosis, 29 and plays an important role in tumour metastasis 30 . A significant association was found between elevated CEA levels and tumour size, lymph node metastasis, and advanced‐stage breast cancer 31–33 .…”

The Ongoing Necessity of Sentinel Lymph Node Biopsy for cT1–2N0 Breast Cancer Patients

“…The relationship between VI and positive lymph nodes in early breast cancer has also been explored. Geng et al [30] showed that VI was an independent prognostic factor for positive axillary lymph nodes in patients undergoing SNLB, including pathological T3 stage, clinical N0 stage, and some N1 stage patients. In addition, Liu et al [31] showed that VI was an independent predictor of non-SLN metastasis in T1-2 breast cancer patients with only one SLN metastasis.…”

Development and Validation of Prediction Models for Sentinel Lymph Node Status Indicating Postmastectomy Radiotherapy in Breast Cancer: a Population-Based Study of 18 185 Women

“…A non-invasive tool for predicting SLN status could be beneficial in preoperatively identifying patients at risk of postoperative complications associated with the requirement for PMRT following IBR. Although there are several nomograms for predicting axillary node status 18–20 , most of them include variables that are not routinely available in a preoperative setting. Moreover, predictive tools to identify patients with cN0 breast cancer at high risk of macro-SLNM indicating the need for PMRT, are still lacking.…”

“…Although there are several nomograms for predicting axillary node status [18][19][20] , most of them include variables that are not routinely available in a preoperative setting. Moreover, predictive tools to identify patients with cN0 breast cancer at high risk of macro-SLNM indicating the need for PMRT, are still lacking.…”