  Quantitative medical image computing (radiomics) has been widely applied to
build prediction models from medical images. However, overfitting is a
significant issue in conventional radiomics, where a large number of radiomic
features are directly used to train and test models that predict genotypes or
clinical outcomes. In order to tackle this problem, we propose an unsupervised
learning pipeline composed of an autoencoder for representation learning of
radiomic features and a Gaussian mixture model based on minimum message length
criterion for clustering. By incorporating probabilistic modeling, disease
heterogeneity has been taken into account. The performance of the proposed
pipeline was evaluated on an institutional MRI cohort of 108 patients with
colorectal cancer liver metastases. Our approach is capable of automatically
selecting the optimal number of clusters and assigns patients into clusters
(imaging subtypes) with significantly different survival rates. Our method
outperforms other unsupervised clustering methods that have been used for
radiomics analysis and has comparable performance to a state-of-the-art imaging
biomarker.
