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