Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

Abstract

IntroductionTo establish a nomogram for predicting axillary lymph node (ALN) involvement in patients with early-stage invasive breast cancer (BC) based on magnetic resonance imaging (MRI) features and clinicopathological characteristics. Materials and MethodsPatients with confirmed early-stage invasive BC between 03/2016 and 05/2017 were retrospectively reviewed at the National Cancer Center/Cancer Hospital. Risk factors for ALN metastasis (ALNM) were identified by univariable and multivariable logistic regression analysis. The independent risk factors were used to create a nomogram. ResultsThis study included 214 early-stage invasive BC patients, including 57 (26.6%) with positive ALNs. Tumor location (OR = 4.019, 95% CI: 1.304 –12.383, P = .015), tumor size (OR = 3.702, 95%CI: 1.517 –9.034, P = .004), multifocality (OR = 3.534, 95%CI: 1.249 –9.995, P = .017), MR-reported suspicious ALN (OR = 9.829, 95%CI: 4.132 –23.384, P <0.001), apparent diffusion coefficient (ADC) value (OR = 0.367, 95%CI: 0.158 –0.852, P = .020), and lymphovascular invasion (LVI) (OR = 3.530, 95%CI: 1.483 –8.400, P = .004) were identified as independent risk factors associated with ALNM. A nomogram was created for predicting the probability of ALNM by using these risk factors. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual ALNM rate. The area under the curve was 0.88 (95% CI: 0.83 –0.93). The nomogram had a bootstrapped-concordance index of 0.88 and was well-calibrated. ConclusionThe nomogram based on MRI and clinicopathologic features might be a useful tool for predicting ALNM in early-stage invasive BC and could help clinical decision-making.