Two-stage biologically interpretable neural-network models for liver cancer prognosis prediction using histopathology and transcriptomic data

Abstract

PurposePathological images are easily accessible data with the potential as prognostic biomarkers. Moreover, integration of heterogeneous data types from multi-modality, such as pathological image and gene expression data, is invaluable to help predicting cancer patient survival. However, the analytical challenges are significant. Experimental DesignHere we take the hepatocellular carcinoma (HCC) pathological image features extracted by CellProfiler, and apply them as the input for Cox-nnet, a neural network-based prognosis. We compare this model with conventional Cox-PH model, CoxBoost, Random Survival Forests and DeepSurv, using C-index and log ranked p-values on HCC testing samples. Further, to integrate pathological image and gene expression data of the same patients, we innovatively construct a two-stage Cox-nnet model, and compare it with another complex neural network model PAGE-Net. Resultspathological image based prognosis prediction using Cox-nnet is significantly more accurate than Cox-PH and random survival forests models, and comparable with CoxBoost and DeepSurv methods. Additionally, the two-stage Cox-nnet complex model combining histopathology image and transcriptomics RNA-Seq data achieves better prognosis prediction, with a median C-index of 0.75 and log-rank p-value of 6e-7 in the testing datasets. The results are much more accurate than PAGE-Net, a CNN based complex model (median C-index of 0.68 and log-rank p-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is much more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are modestly correlated with gene expression. Genes having correlations to top imaging features have known associations with HCC patient survival and morphogenesis of liver tissue. ConclusionThis work provides two-stage Cox-nnet, a new class of biologically relevant and relatively interpretable models, to integrate multi-modal and multiple types of data for survival prediction.