Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors.

Chunhua Wang,Hongbing Luo,Jing Ren,Ruirui Meng,Siyun Liu,Guohui Xu,Xiaoyu Chen,Peng Zhou,Yuanyuan Liu,Min Wang

doi:10.3389/fonc.2021.754843

Abstract

PurposeTo develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients.MethodsThis study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong’s test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the “Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis” (TRIPOD) statement.ResultsThe AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717–0.849) and 0.680 (95% CI, 0.604–0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737–0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783–0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05).ConclusionFGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.

Highlights

Breast cancer is the most common cancer with heterogeneous features and leading cause of cancer death in females worldwide [1]
We developed a nomogram by combining tumor and fibrograndular tissue (FGT) signatures and histopathological progesterone receptor (PR) expression status to predict Sentinel lymph node (SLN) status and achieved a higher area under curve (AUC) of 0.839, with a high negative predictive value (NPV) of 0.853, suggesting benefits for identification of patients with positive SLN, which was consistent with a previous study that combined radiomic features and clinical factors, which were predictive for positive and negative SLN [16] and may help breast cancer patients to avoid unnecessary SLNB and the corresponding complications
Regarding the prediction of SLN status, the AUC of Magnetic resonance imaging (MRI) features was 0.80, suggesting that the dynamic contrast-enhanced (DCE)-MRI can play an important role in the prediction of SLN status

Summary

Introduction

Breast cancer is the most common cancer with heterogeneous features and leading cause of cancer death in females worldwide [1]. The low apparent diffusion coefficient (ADC) value and rim enhancement of tumor in patients with breast cancer were associated with lymph node metastasis [6, 7]. Heterogeneous and rim enhancement, and peritumoral–tumoral ADC ratio were independent predictors for SLN metastasis in breast cancer [8]. In addition to conventional feature analysis, radiomics as a novel tool applied in medicine is able to extract high-throughput quantitative features from medical images obtained by noninvasive technique using mathematical algorithm [9,10,11], which has been used to predict malignancy [12], molecular subtypes [13], pathological complete response to neoadjuvant chemotherapy [14], and ALN or SLN metastasis in breast cancer [15, 16]

Objectives

Methods

Results

Conclusion