Introduction
ALNM.Breast cancer, cancer, cancers, breast cancer
CANCERS, BREAST CANCER, CANCER, REGRESSIONS, BREAST
Xiaomin Wang and Xiaoping Yi contributed equally to this work.Models for predicting axillary lymph node metastasis (ALNM) in breast cancer patients are lacking. We aimed to develop an efficient model to accurately predict ALNM. Three hundred fifty-five breast cancer patients were recruited and randomly divided into the training and validation sets. Univariate and multivariate logistic regressions were applied to identify predictors of ALNM. We developed nomograms based on these variables to predict ALNM. The performance of the nomograms was tested using the receiver operating characteristic curve and calibration curve, and a decision curve analysis was performed to assess the clinical utility of the prediction models. The nomograms that included clinical N stage (cN), pathological grade (pathGrade), and hemoglobin accurately predicted ALNM in the training and validation sets (area under the curve [AUC] 0.80 and 0.80, respectively). We then explored the importance of the cN and pathGrade signatures used in the integrated model and developed new nomograms by removing the two variables. The results suggested that the combine-pathGrade nomogram also accurately predicted ALNM in the training and validation sets (AUC 0.78 and 0.78, respectively), but the combine-cN nomogram did not (AUC 0.64 and 0.60, in the training and validation sets, respectively). We described a cN-based ALNM prediction model in breast cancer patients, presenting a novel efficient clinical decision nomogram for predicting ALNM.Breast cancer is one of the most commonly diagnosed cancers worldwide, with over 2 million new cases in 2020 based on the GLOBOCAN [Traditional noninvasive methods to confirm the ALN status are mainly preoperative imaging examinations, such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). However, these approaches may lead to some ALNM patients being missed due to low sensitivity [In the current study, we aimed to build and validate a noninvasive predictive model to accurately predict ALNM in breast cancer patients. We collected patients’ demographics and laboratory tests and extracted clinical N stage (cN) signature, which was assessed by the Breast Surgery multidisciplinary team. Then, we used clinical factors and cN to generate nomograms for preoperative prediction of ALN status in breast cancer patients to optimize decision making for personalized cancer treatment.