New Explainable Deep CNN Design for Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy.

To reduce breast tumor size before surgery, Neoadjuvant Chemotherapy (NAC) is applied systematically to patients with local breast cancer. However, with the existing clinical protocols, it is not yet possible to have an early prediction of the effect of chemotherapy on a breast tumor. Predicting the response to chemotherapy could reduce toxicity and delay effective treatment. Computational analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) through Deep Convolution Neural Network (CNN) has proved a significant performance in classifying responders and no responder's patients. This study intends to present a new explainable Deep Learning (DL) model predicting the breast cancer response to chemotherapy based on multiple MRI inputs. In this study, a cohort of 42 breast cancer patients who underwent chemotherapy was used to train and validate the proposed DL model. This dataset was provided by the Jules Bordet institute of radiology in Brussels, Belgium. 14 external subjects were used to validate the DL model to classify responding or non-responding patients on temporal DCE-MRI volumes. The model performance was assessed by the Area Under the receiver operating characteristic Curve (AUC), accuracy, and features map visualization according to pathological complete response (Ground truth). The developed deep learning architecture was able to predict the responding breast tumors to chemotherapy treatment in the external validation dataset with an AUC of 0.93 using parallel learning MRI images acquired at different moments. The visual results showed that the most important extracted features from non-responding tumors are in the peripheral and external tumor regions. The model proposed in this study is more efficient compared to those proposed in the literature. Even with a limited training dataset size, the developed multi-input CNN model using DCE-MR images acquired before and following the first chemotherapy was able to predict responding and non-responding tumors with higher accuracy. Thanks to the visualization of the extracted characteristics by the DL model on the responding and non-responding tumors, the latter could be used henceforth in clinical analysis after its evaluation based on more extra data.

Adoui ME ，Drisis S ，Benjelloun M 《Current Medical Imaging》

Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.

El Adoui M ，Drisis S ，Benjelloun M 《-》

Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.

The aim of the study was to develop a deep learning (DL) algorithm to evaluate the pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer. A total of 302 breast cancer patients in this retrospective study were randomly divided into a training set (n = 244) and a validation set (n = 58). Tumor regions were manually delineated on each slice by two expert radiologists on enhanced T1-weighted images. Pathological results were used as ground truth. Deep learning network contained five repetitions of convolution and max-pooling layers and ended with three dense layers. The pre-NAC model and post-NAC model inputted six phases of pre-NAC and post-NAC images, respectively. The combined model used 12 channels from six phases of pre-NAC and six phases of post-NAC images. All models above included three indexes of molecular type as one additional input channel. The training set contained 137 non-pCR and 107 pCR participants. The validation set contained 33 non-pCR and 25 pCR participants. The area under the receiver operating characteristic (ROC) curve (AUC) of three models was 0.553 for pre-NAC, 0.968 for post-NAC and 0.970 for the combined data, respectively. A significant difference was found in AUC between using pre-NAC data alone and combined data (P < 0.001). The positive predictive value of the combined model was greater than that of the post-NAC model (100% vs. 82.8%, P = 0.033). This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data. The model performed better than using pre-NAC data only, and also performed better than using post-NAC data only. Significant findings of the study. It achieved an AUC of 0.968 for pCR prediction. It showed a significantly greater AUC than using pre-NAC data only. What this study adds This study established a deep learning model to predict PCR status after neoadjuvant therapy by combining pre-NAC and post-NAC MRI data.

Qu YH ，Zhu HT ，Cao K ，Li XT ，Ye M ，Sun YS ... -  《-》

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 ... -  《-》

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 ... -  《-》