AMINN: Autoencoder-based Multiple Instance Neural Network for Outcome Prediction of Multifocal Liver Metastases

• URL: http://arxiv.org/abs/2012.06875v1
• Date: Sat, 12 Dec 2020 17:52:14 GMT
• Title: AMINN: Autoencoder-based Multiple Instance Neural Network for Outcome Prediction of Multifocal Liver Metastases
• Authors: Jianan Chen, Helen M. C. Cheung, Laurent Milot, Anne L. Martel
• Abstract summary: Multifocality occurs frequently in colorectal cancer liver metastases. Most existing biomarkers do not take the imaging features of all multifocal lesions into account. We present an end-to-end autoencoder-based multiple instance neural network (AMINN) for the prediction of survival outcomes.
• Score: 1.7294318054149134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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