• Zhuangsheng Liu, Bao Feng, Changlin Li, Yehang Chen, Qinxian Chen, Xiaoping Li, Jianhua Guan, Xiangmeng Chen, Enming Cui, Ronggang Li, Zhi Li, and Wansheng Long.
• Department of Radiology, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China.
• J Magn Reson Imaging. 2019 Sep 1; 50 (3): 847-857.

BackgroundLymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection.PurposeTo explore the use of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)-based radiomics for preoperative prediction of LVI in invasive breast cancer.Study TypeProspective.PopulationNinety training cohort patients (22 LVI-positive and 68 LVI-negative) and 59 validation cohort patients (22 LVI-positive and 37 LVI-negative) were enrolled.Field Strength/Sequence1.5 T and 3.0 T, T1 -weighted DCE-MRI.AssessmentAxillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE-MRI. A radiomics signature was constructed in the training cohort with 10-fold cross-validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort.Statistical TestsMann-Whitney U-test, chi-square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA).ResultsTwo radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone.Data ConclusionThe DCE-MRI-based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery.Level Of Evidence1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847-857.© 2019 International Society for Magnetic Resonance in Medicine.

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