Pre-operative Survival Prediction of Diffuse Glioma Patients with Joint Tumor Subtyping

Pre-operative survival prediction for diffuse glioma patients is desired for personalized treatment. Clinical findings show that tumor types are highly correlated with the prognosis of diffuse glioma. However, the tumor types are unavailable before craniotomy and cannot be used in pre-operative survival prediction. In this paper, we propose a new deep learning based pre-operative survival prediction method. Besides the common survival prediction backbone, a tumor subtyping network is integrated to provide tumor-type-related features. Moreover, a novel ordinal manifold mixup is presented to enhance the training of the tumor subtyping network. Unlike the original manifold mixup, which neglects the feature distribution, the proposed method forces the feature distribution of different tumor types in the order of risk grade, by which consistency between the augmented features and labels can be strengthened. We evaluate our method on both in-house and public datasets comprising 1936 patients and demonstrate up to a 10% improvement in concordance-index compared with the state-of-the-art methods. Ablation study further confirms the effectiveness of the proposed tumor subtyping network and the ordinal manifold mixup. Codes are available at https://github.com/ginobilinie/osPred.