MRI-Based Radiomics for Preoperative Prediction of Lymphovascular Invasion in Patients With Invasive Breast Cancer

Abstract

ObjectivePreoperative identification of lymphovascular invasion (LVI) in patients with invasive breast cancer is challenging due to absence of reliable biomarkers or tools in clinical settings. We aimed to establish and validate multiparametric magnetic resonance imaging (MRI)-based radiomic models to predict the risk of lymphovascular invasion (LVI) in patients with invasive breast cancer.MethodsThis retrospective study included a total of 175 patients with confirmed invasive breast cancer who had known LVI status and preoperative MRI from two tertiary centers. The patients from center 1 was randomly divided into a training set (n=99) and a validation set (n = 26), while the patients from center 2 was used as a test set (n=50). A total of 1409 radiomic features were extracted from the T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), respectively. A three-step feature selection including SelectKBest, interclass correlation coefficients (ICC), and least absolute shrinkage and selection operator (LASSO) was performed to identify the features most associated with LVI. Subsequently, a Support Vector Machine (SVM) classifier was trained to develop single-layer radiomic models and fusion radiomic models. Model performance was evaluated and compared by the area under the curve (AUC), sensitivity, and specificity.ResultsBased on one feature of wavelet-HLH_gldm_GrayLevelVariance, the ADC radiomic model achieved an AUC of 0.87 (95% confidence interval [CI]: 0.80–0.94) in the training set, 0.87 (0.70-1.00) in the validation set, and 0.77 (95%CI: 0.64-0.86) in the test set. However, the combination of radiomic features derived from other MR sequences failed to yield incremental value.ConclusionsADC-based radiomic model demonstrated a favorable performance in predicting LVI prior to surgery in patients with invasive breast cancer. Such model holds the potential for improving clinical decision-making regarding treatment for breast cancer.

Editorial for “Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer”

“…The LVI radiomic model in this study is distinguished from prior models through the inclusion of the peritumoral region. Hence, the seven peritumoral features chosen is an important result that supports the continued inclusion of this region in future model development 3,5–8 . It may also be impactful to include T2‐weighted features in future models, as peritumoral edema on T2‐weighted images is associated with poor prognosis in breast cancer 9 .…”

“…Hence, the seven peritumoral features chosen is an important result that supports the continued inclusion of this region in future model development. 3,[5][6][7][8] It may also be impactful to include T2-weighted features in future models, as peritumoral edema on T2-weighted images is associated with poor prognosis in breast cancer. 9 Further, in a previous investigation of a multicontrast MRI radiomic model for LVI status, seven of the 12 features selected were from T2-weighted images.…”

Predictive value of dynamic contrast-enhanced-MRI-based machine learning model for lymphovascular invasion status in node-negative invasive breast cancer

“…Zhang et al [15] identi ed that the proposed nomogram, incorporating MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients. One recent study used SVM classi er to establish a prediction model of LVI based on ADC radiomic signature, and its AUC was 0.77 in the test set [14]. Liu et al [13] con rmed that the DCE-MRI based radiomics model using multivariate logistic regression method could signi cantly improve the performance for discriminating LVI-positive from LVI-negative lesions.…”

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE‐MRI Radiomics Nomogram

“…Intraclass correlation coefficients (ICCs) were calculated to evaluate the reliability and reproducibility of features by using 60 randomly chosen MR images. Radiomics features with ICC > 0.75 (excellent stability) were used for feature extraction [ 20 ]. Second, all feature lines were standardized by the z score standardization method, and the correlation between features was calculated by the Spearman correlation coefficient.…”