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 ... -  《-》

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.

To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.

Aghaei F ，Tan M ，Hollingsworth AB ，Zheng B ... -  《-》

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 ... -  《-》

Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients.

To enhance the accurate prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer patients by using a quantitative analysis of dynamic enhancement magnetic resonance imaging (DCE-MRI). A dataset of 57 cancer patients with breast DCE-MR images acquired before NAC was used. Among them, 47 patients were Responders, and 10 patients were non-Responders based on the RECIST criteria. The breast regions were segmented on the MR images, and a total of 158 radiomic features were computed to represent the morphologic, dynamic, and the texture of the tumors as well as the background parenchymal features. The optimal subset of features was selected using evolutionary based Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-out cross-validation (LOOCV) method to classify Responder and non-Responder cases. The area under a receiver operating characteristic curve (AUC) was computed to assess the classifier performance. An additional independent dataset with 46 patients was also included to validate the results. The evolutionary algorithm (EA)-based method identified optimal subsets comprising 12 image features that were fit for classification for the main cohort. Following the same feature selection procedure, the independent validation dataset produced 11 image features, 7 of which were identical to those from the main cohort. The classifier based on the features yield a LOOCV AUC of 0.910 and 0.874 for the main and the reproducibility study cohort, respectively. If the optimal features in the main cohort were utilized to test performance on the reproducibility cohort, the classifier generated an AUC of 0.713. While the features developed in the reproducibility cohort were applied to test the main cohort, the classifier achieved an AUC of 0.683. The AUC of the averaged receiver operating characteristic (ROC) curve for the two data cohort was 0.703. This study demonstrated that quantitative analyses of radiomic features from pretreatment breast DCE-MRI data could be used as valuable image markers that are associated with tumor response to NAC.

Fan M ，Wu G ，Cheng H ，Zhang J ，Shao G ，Li L ... -  《-》

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps.

Machireddy A ，Thibault G ，Tudorica A ，Afzal A ，Mishal M ，Kemmer K ，Naik A ，Troxell M ，Goranson E ，Oh K ，Roy N ，Jafarian N ，Holtorf M ，Huang W ，Song X ... -  《-》