  Colorectal cancer is one of the most common and lethal cancers and colorectal
cancer liver metastases (CRLM) is the major cause of death in patients with
colorectal cancer. Multifocality occurs frequently in CRLM, but is relatively
unexplored in CRLM outcome prediction. Most existing clinical and imaging
biomarkers do not take the imaging features of all multifocal lesions into
account. In this paper, we present an end-to-end autoencoder-based multiple
instance neural network (AMINN) for the prediction of survival outcomes in
multifocal CRLM patients using radiomic features extracted from
contrast-enhanced MRIs. Specifically, we jointly train an autoencoder to
reconstruct input features and a multiple instance network to make predictions
by aggregating information from all tumour lesions of a patient. In addition,
we incorporate a two-step normalization technique to improve the training of
deep neural networks, built on the observation that the distributions of
radiomic features are almost always severely skewed. Experimental results
empirically validated our hypothesis that incorporating imaging features of all
lesions improves outcome prediction for multifocal cancer. The proposed ADMINN
framework achieved an area under the ROC curve (AUC) of 0.70, which is 19.5%
higher than baseline methods. We built a risk score based on the outputs of our
network and compared it to other clinical and imaging biomarkers. Our risk
score is the only one that achieved statistical significance in univariate and
multivariate cox proportional hazard modeling in our cohort of multifocal CRLM
patients. The effectiveness of incorporating all lesions and applying two-step
normalization is demonstrated by a series of ablation studies. Our code will be
released after the peer-review process.
