DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.

Breast tumor heterogeneity is related to risk factors that lead to worse prognosis, yet such heterogeneity has not been well studied. To 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). Retrospective study. Seventy-seven breast cancer patients with ER-positive breast cancer were investigated, of whom 51 had low Ki-67 expression. T1 -weighted 3.0T DCE-MR images. Each 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. Univariate 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. In 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). Texture analysis of intratumoral heterogeneity has the potential to serve as a valuable clinical marker to enhance the prediction of breast cancer prognosis. 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

Fan M ，Cheng H ，Zhang P ，Gao X ，Zhang J ，Shao G ，Li L ... -  《-》

Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy.

To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.

Wu J ，Gong G ，Cui Y ，Li R ... -  《-》

Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of Dynamic Contrast-Enhanced MRI for Predicting Histological Characteristics of Breast Cancer.

Breast tumor heterogeneity is associated with histological characteristics. However, pharmacokinetic (PK) heterogeneity within tumor might merit further exploration. To enhance the predictive power of molecular subtypes, Ki-67, and tumor grade by analyzing PK heterogeneity within tumor based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Retrospective. Two hundred and eight biopsy-proven breast cancer patients, randomly divided into a training cohort (N = 144) and a testing cohort (N = 64). T1 -weighted DCE-MRI at 3.0 T. A convex analysis of mixtures-compartmental modeling decomposition method was used to estimate the PK parameter (i.e., the volume transfer constant Ktrans ) in tumor subregions with distinct physiological kinetic patterns, including fast-flow kinetics, slow-flow kinetics, and plasma input. Radiomic features based on the PK parameter were calculated from each tumor subregion. The training cohort was used to build random forest classifiers based on the optimal features determined by the 5-fold cross-validation method. The performance was assessed on the testing cohort using the area under the receiver operating characteristic curve (AUC). The AUCs derived from the tumor subregion-based PK parameter were compared with those of the original images of the entire tumor using the DeLong test. A P-value of <0.05 was considered statistically significant. The tumor subregion-based PK parameter, which yielded the highest AUCs of 0.8782, 0.7568, 0.7019, 0.7963, 0.8080, and 0.7375 for luminal A, luminal B, basal-like, human epidermal growth factor receptor 2, Ki-67, and tumor grade, respectively, obtained better diagnostic performance than the original images in the entire tumor (highest AUCs = 0.8612, 0.6191, 0.5593, 0.7704, 0.7494, and 0.6261, respectively). In particular, statistically significant improvement in the diagnostic performance was obtained for luminal B. Radiomic analysis of PK heterogeneity within tumor can enhance the predictive performance of radiomic models compared with that of the entire tumor. 4 TECHNICAL EFFICACY STAGE: 3.

Zhang L ，Fan M ，Wang S ，Xu M ，Li L ... -  《-》

Differences in tumour heterogeneity based on dynamic contrast-enhanced MRI between tumour and peritumoural stroma for predicting Ki-67 status of invasive ductal carcinoma.

To evaluate and compare the heterogeneity of intratumour and peritumour areas in the prediction of Ki-67 of invasive ductal carcinoma (IDC) and the predictive accuracy of different contrast frames based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This study included 88 patients with histologically confirmed IDC with 57 patients with high Ki-67 status and 31 patients with low Ki-67 status. All patients underwent DCE-MRI before surgery. A grey-level co-occurrence matrix (GLCM) was performed on slice-matched images from six frames by drawing the region of the interest (ROI) on the inner and outer regions of the tumours. The correlations between texture characteristics and Ki-67 status of lesions were analysed, using the Mann-Whitney test and receiver operating characteristic curve analysis. In the high-Ki-67 group, the entropy was significantly higher than that of the low-Ki-67 group (p<0.001). The entropy obtained, based on the tumour boundary as a band-like area inside and outside at the first post-contrast series, revealed the highest receiver operating characteristic (AUC = 0.765). In the multivariate analysis, a higher entropy value (>4.305; p<0.001) remained independently associated with a high-Ki-67 status after adjustment for menopausal status, tumour size, histologic grade, oestrogen receptor (ER) status, and progesterone receptor (PR) status. The other parameters did not show significant differences between the high- and low-Ki-67 groups. Heterogeneity analysis based on DCE-MRI could discriminate between high- and low-Ki-67 status. Texture characteristics from the band-like region inside and outside the tumour boundary could predict the Ki-67 status and showed higher accuracy.

He X ，Zhou J ，Ye S ，Cheng Q ，Miao H ，Xu N ，Li J ，Pan Z ，Cheng J ，Wang M ... -  《-》

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer.

Zhang L ，Shen M ，Zhang D ，He X ，Du Q ，Liu N ，Huang X ... -  《-》