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Risk stratification of kidney cancers based on survival at diagnosis could enable more informed treatment decisions toward improved patient survival. Given the limited prognostic ability of clinical evaluation in segregating long-term vs short-term survivors, we sought to develop prognostic models which could exploit diagnostic CT scans for overall survival prediction in kidney cancers. This requires overcoming challenges related to model interpretability (such that the model best utilizes the most relevant locations within or around the kidney) as well as model generalizability (to ensure optimal model performance despite limited cohort sizes). In this work, we present the Spatial Attention Wavelon Network (SpAWN), which leverages a novel pre-training spatial attention operation to guide localization of convolutional responses together with wavelon activation functions to overcome known issues with vanishing/exploding gradients that occur in limited cohorts with class imbalance. SpAWN was evaluated for prognosticating survival on two large-scale publicly available cohorts of over 400 CT scans from kidney cancer patients, with comprehensive ablation studies to confirm the utility of attention maps as well as wavelon activation functions. A kidney exterior focused SpAWN model (with wavelon activations) demonstrated the best overall validation performance in segregating low- and high-risk patients in a hold-out cohort of N=223 CT scans (c-index=0.58, p=0.03), and was significantly improved compared to any alternative strategy. Integrating spatial attention with wavelon activations represents a novel interpretable and robust approach for prognosticating overall survival in kidney cancers via CT scans.
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