Preoperative Nomogram for Predicting Sentinel Lymph Node Metastasis Risk in Breast Cancer: A Potential Application on Omitting Sentinel Lymph Node Biopsy

Abstract

BackgroundSentinel lymph node (SLN) biopsy is feasible for breast cancer (BC) patients with clinically negative axillary lymph nodes; however, complications develop in some patients after surgery, although SLN metastasis is rarely found. Previous predictive models contained parameters that relied on postoperative data, thus limiting their application in the preoperative setting. Therefore, it is necessary to find a new model for preoperative risk prediction for SLN metastasis to help clinicians facilitate individualized clinical decisions.Materials and MethodsBC patients who underwent SLN biopsy in two different institutions were included in the training and validation cohorts. Demographic characteristics, preoperative tumor pathological features, and ultrasound findings were evaluated. Multivariate logistic regression was used to develop the nomogram. The discrimination, accuracy, and clinical usefulness of the nomogram were assessed using Harrell’s C-statistic and ROC analysis, the calibration curve, and the decision curve analysis, respectively.ResultsA total of 624 patients who met the inclusion criteria were enrolled, including 444 in the training cohort and 180 in the validation cohort. Young age, high BMI, high Ki67, large tumor size, indistinct tumor margins, calcifications, and an aspect ratio ≥1 were independent predictive factors for SLN metastasis of BC. Incorporating these parameters, the nomogram achieved a robust predictive performance with a C-index and accuracy of 0.92 and 0.85, and 0.82 and 0.80 in the training and validation cohorts, respectively. The calibration curves also fit well, and the decision curve analysis revealed that the nomogram was clinically useful.ConclusionsWe established a nomogram to preoperatively predict the risk of SLN metastasis in BC patients, providing a non-invasive approach in clinical practice and serving as a potential tool to identify BC patients who may omit unnecessary SLN biopsy.

The NILS Study Protocol: A Retrospective Validation Study of an Artificial Neural Network Based Preoperative Decision-Making Tool for Noninvasive Lymph Node Staging in Women with Primary Breast Cancer (ISRCTN14341750)

“…Many nomograms and ANN models have been developed for predicting nodal metastasis, or the lack thereof, with [28][29][30][31][32][33][34][35][36] or without imaging [37][38][39][40]. The discrimination of clinical nomograms including clinicopathological data only is equivalent to the predictions made by the NILS prediction model (area under the curve (AUC) 0.73-0.75 [39,40]), whereas those incorporating radiomic features present an AUC slightly above 0.90 [30][31][32]34].…”

“…Many nomograms and ANN models have been developed for predicting nodal metastasis, or the lack thereof, with [28][29][30][31][32][33][34][35][36] or without imaging [37][38][39][40]. The discrimination of clinical nomograms including clinicopathological data only is equivalent to the predictions made by the NILS prediction model (area under the curve (AUC) 0.73-0.75 [39,40]), whereas those incorporating radiomic features present an AUC slightly above 0.90 [30][31][32]34]. However, the ANN model that this study aims to validate is a promising tool in the clinic because the input variables are routinely available, no extra imaging besides clinical work-up (i.e., mammography and axillary ultrasound) is necessary, and the web interface is user-friendly.…”

The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer

“…Previous studies reported that tumor size was an independent prognostic factor of SLN [ 32 ]. Tumor size was proportional to axillary lymph node metastasis, and each 0.1 cm higher in tumor size resulted in 4.29 times more likely to have SLN metastasis in breast cancer [ 33 ]. Our study showed that the lesion size was the largest for the number of metastatic ALNs of > 3, followed by the number of metastatic ALNs of 1–2, and then by no metastasis, similar to previous studies.…”

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

“…Hu et al. concluded that a significant association between young age, high BMI, high Ki67 and large tumour size was an independent predictor for ALNM ( 25 ). However, the accuracy of the above studies remains uncertain due to insufficient population size.…”

“…Zeng et al found that grade, estrogen receptor (ER) status and human epidermal growth factor receptor 2 (Her-2) status were significantly correlated with ALNM (24). Hu et al concluded that a significant association between young age, high BMI, high Ki67 and large tumour size was an independent predictor for ALNM (25). However, the accuracy of the above studies remains uncertain due to insufficient population size.…”