• Ming Fan, Hu Cheng, Peng Zhang, Xin Gao, Juan Zhang, Guoliang Shao, and Lihua Li.
• Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.
• J Magn Reson Imaging. 2018 Jul 1; 48 (1): 237-247.

BackgroundBreast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied.PurposeTo predict the Ki-67 status of estrogen receptor (ER)-positive breast cancer patients via analysis of tumor heterogeneity with subgroup identification based on patterns of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Study TypeRetrospective study.PopulationSeventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression.Field Strength/SequenceT1 -weighted 3.0T DCE-MR images.AssessmentEach tumor was partitioned into multiple subregions using three methods based on patterns of dynamic enhancement: 1) time to peak (TTP), 2) peak enhancement rate (PER), and 3) kinetic pattern clustering (KPC). In each tumor subregion, 18 texture features were computed.Statistical TestingUnivariate and multivariate logistic regression analyses were performed using a leave-one-out-based cross-validation (LOOCV) method. The partitioning results were compared with the same feature extraction methods across the whole tumor.ResultsIn the univariate analysis, the best-performing feature was the texture statistic of sum variance in the tumor subregion with early TTP for differentiating between patients with high and low Ki-67 expression (area under the receiver operating characteristic curves, AUC = 0.748). Multivariate analysis showed that features from the tumor subregion associated with early TTP yielded the highest performance (AUC = 0.807) among the subregions for predicting the Ki-67 status. Among all regions, the tumor area with high PER at a precontrast MR image achieved the highest performance (AUC = 0.722), while the subregion that exhibited the highest overall enhancement rate based on KPC had an AUC of 0.731. These three models based on intratumoral texture analysis significantly (P < 0.01) outperformed the model using features from the whole tumor (AUC = 0.59).Data ConclusionTexture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis.Level Of Evidence4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.© 2017 International Society for Magnetic Resonance in Medicine.

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