KINET: A Non-Invasive Method For Predicting Ki67 Index Of Glioma

In this paper, a multimodal magnetic resonance imaging (MRI) and heterogeneous metadata (including age, gender) dataset containing263 patients was established. Based on this dataset, a new multimodal deep neural network (KiNet) was proposed, aiming to effectively predict the Ki67 index in gliomas in a non-invasive way by fusing multimodal MRI features and metadata. We adopted a five-fold crossvalidation approach to verify the performance of the network. KiNet achieved results with an AUC of 0.79 and a kappa coefficient of 0.47. The proposed approach’s outperformance indicated the feasibility of predicting the Ki67 index in gliomas in a non-invasive way.