Context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in renal...

The invasiveness prediction in renal cell carcinoma (RCC) is of significant importance for the decision of clinical surgical plans and the patients' prognosis. Currently, besides invasive pathological assessment, it mainly relies on observation through computed tomography (CT) imaging. However, limitations of human vision and qualitative descriptions restrict the accuracy of the diagnosis of renal sinus invasion (RSI). Recently, artificial intelligence approaches have shown promising prospects in cancer diagnosis. Due to the complex imaging characteristics of invasiveness, prediction models that only focus on tumor regions are inadequate, requiring comprehensive evaluation of intratumoral heterogeneity, peritumoral information, and the kidney in which the tumor resides. Therefore, in this study, we propose a context-aware heterogeneous graph neural network for multi-level description and invasiveness prediction in RCC. The superiority of the proposed model lies in its ability to integrate imaging features at multi-level, and to learn disturbance invariant features through a data-driven diffusion perturbation strategy. To evaluate the effectiveness and generalization of our model, we conduct extensive experiments on a multi-center dataset (including CT scan images of 437 patients) to compare our model with a series of state-of-the-art (SOTA) classification models. The experimental results show the superiority of our model for RSI classification (AUC=0.88). Additionally, we also perform a comparative study with clinical experts, and the proposed method is significantly better than existing assessment methods and clinical experts (p<0.05). In general, our work provides an effective assessment tool for automated diagnosis of RSI in RCC and also offers new insights for constructing more precise tumor prediction models.