Diagnosis-Guided Multi-modal Feature Selection for Prognosis Prediction of Lung Squamous Cell Carcinoma

Abstract

The existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can hold great promise for survival analysis of cancers. However, direct combination of multi-modal data may bring irrelevant or redundant features that will harm the prognosis performance. Therefore, it has become a challenge to select informative features from the derived heterogeneous data for survival analysis. Most existing feature selection methods only utilized the collected multi-modal data and survival information to identify a subset of relevant features, which neglect to use the diagnosis information to guide the feature selection process. In fact, the diagnosis information (e.g., TNM stage) indicates the extent of the disease severity that are highly correlated with the patients’ survival. Accordingly, we propose a diagnosis-guided multi-modal feature selection method (DGM2FS) for prognosis prediction. Specifically, we make use of the task relationship learning framework to automatically discover the relations between the diagnosis and prognosis tasks, through which we can identify important survival-associated image and eigengenes features with the help of diagnosis information. In addition, we also consider the association between the multi-modal data and use a regularization term to capture the correlation between the image and eigengene data. Experimental results on a lung squamous cell carcinoma dataset imply that incorporating diagnosis information can help identify meaningful survival-associated features, by which we can achieve better prognosis prediction performance than the conventional methods.

This work was supported in part by the Indiana University Precision Health Initiative to ZH and KH, the National Natural Science Foundation of China (61876082, 61861130366, 61703301) to DZ, and Shenzhen Peacock Plan (KQTD2016053112051497) to JC.