Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution

Abstract

Prediction of high grade ovarian cancer on proteomic data is a clinical challenge. Besides, it offers the potential for earlier intervention to increase overall survival, as well as guides the prophylactic ovarian removal to avoid unnecessary early menopause. In this work, we propose a model that learns how to detect ovarian cancer on images from uterine liquid proteomic data. The contributions of this work are two-fold. First, we propose an original method to use proteomic data without direct matching with the existing protein libraries as in the traditional method. The gray-scale peptide image generated by our method contains almost all information from mass spectrometry. Second, we pioneer in analyzing the uterine liquid proteomic data with deep convolutional neural networks. Specifically, we design a feature extractor consisting of stacked asymmetric convolutional layers, which could pay more attention to multiple compounds in different retention times and isotopes in similar mass/charge than symmetric convolutions. Another novelty is trying to find the patches contributing more in improving both sensitivity and specificity. In addition, we add an auxiliary classifier module near the end of training to push useful gradients into the lower layers and to improve the convergence during training. Compared with traditional proteome analysis, experimental results demonstrate the effectiveness and superiority of our model in high grade ovarian cancer prediction.