Traditional histopathology quantifies disease through the study of glass slides, i.e. two-dimensional samples that are representative of the overall process. We hypothesize that 3D reconstruction can enhance our understanding of histopathologic interpretations. To test this hypothesis, we perform a pilot study of the risk model for oral cavity cancer (OCC), which stratifies patients into low-, intermediate-, and high-risk for locoregional disease-free survival. Classification is based on study of hematoxylin and eosin (H and E) stained tissues sampled from the resection specimens. In this model, the Worst Pattern of Invasion (WPOI) is assessed, representing specific architectural features at the interface between cancer and non-cancer tissue. Currently, assessment of WPOI is based on 2D sections of tissue, representing complex 3D structures of tumor growth. We believe that by reconstructing a 3D model of tumor growth and quantifying the tumor-host interface, we can obtain important diagnostic information that is difficult to assess in 2D. Therefore, we introduce a pilot study framework for visualizing tissue architecture and morphology in 3D from serial sections of histopathology. This framework can be used to enhance predictive models for diseases where severity is determined by 3D biological structure. In this work we utilize serial H and E-stained OCC resections obtained from 7 patients exhibiting WPOI-3 (low risk of recurrence) through WPOI-5 (high risk of recurrence). A supervised classifier automatically generates a map of tumor regions on each slide, which are then co-registered using an elastic deformation algorithm. A smooth 3D model of the tumor region is generated from the registered maps, which is suitable for quantitative tumor interface morphology feature extraction. We report our preliminary models created with this system and suggest further enhancements to traditional histology scoring mechanisms that take spatial architecture into consideration.