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We proposed a novel convolutional restricted Boltzmann machine CRBM-basedradiomic method for predicting pathologic complete response (pCR) toneoadjuvant chemotherapy treatment (NACT) in breast cancer. The method consistsof extracting semantic features from CRBM network, and pCR prediction. It wasevaluated on the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)data of 57 patients and using the area under the receiver operatingcharacteristic curve (AUC). Traditional radiomics features and the semanticfeatures learned from CRBM network were extracted from the images acquiredbefore and after the administration of NACT. After the feature selection, thesupport vector machine (SVM), logistic regression (LR) and random forest (RF)were trained to predict the pCR status. Compared to traditional radiomicmethods, the proposed CRBM-based radiomic method yielded an AUC of 0.92 for theprediction with the images acquired before and after NACT, and an AUC of 0.87for the pretreatment prediction, which was increased by about 38%. The resultsshowed that the CRBM-based radiomic method provided a potential means foraccurately predicting the pCR to NACT in breast cancer before the treatment,which is very useful for making more appropriate and personalized treatmentregimens.

Convolutional Restricted Boltzmann Machine Based-Radiomics for  Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in  Breast Cancer