Can spatial heterogeneity in whole slide images (WSIs) be automatically learned to predict patient survival outcomes? Spatial heterogeneity within the tissue and tumour microenvironments is increasingly recognised as a critical indicator of cancer prognosis. However, most existing methods do not explicitly model spatial heterogeneity, and those that do typically require segmentation of cellular or tissue structures followed by the use of hand-crafted spatial metrics such as the Morisita-Horn index or colocalisation indices. These approaches are not learnable, and rely on manually defined features and relationships. In this work, we propose a novel neural network-based framework that learns a differentiable variant of Moran’s Index, a classical measure of spatial autocorrelation, to automatically quantify spatial heterogeneity from point cloud representations of whole slide image patches. Guided by survival information in training, the proposed method adaptively learns which image-derived features correlate spatially, and how proximity influences their interaction, enabling the discovery of prognostic spatial patterns directly from patch-level features. Applied to the TCGA breast cancer dataset, the proposed method achieves a test concordance index of 0.662 ± 0.060, outperforming several competitive baselines while providing an interpretable quantification of spatial heterogeneity in WSIs. This approach opens new directions for interpretable, spatially informed prognostic modeling and biomarker discovery in computational pathology.