Oral cavity cancer (OCC) is a type of cancer of the lip, tongue, salivary glands and other sites in the mouth (buccal or oral cavity) and is the sixth leading cause of cancer worldwide. Patients with OCC are treated based on a staging system: low-stage patients typically receive less aggressive therapy compared to high-stage patients. Unfortunately, low-stage patients are sometimes at risk for locoregional recurrence. Recently, a semi-quantitative risk scoring system has been developed to assess the locoregional recurrence risk for low-stage patients. This risk scoring system is based on tissue characteristics determined on 2D histopathology images under a microscope. This modality limits the appreciation of the 3D architecture of the tumor and its associated morphological features. This thesis aims to visualize 3D models of the tumor-host interface reconstructed from serially-sectioned histopathology slides and quantify their clinically validated morphological features to predict locoregional recurrence after treatment. The 3D models are developed and quantified for 6 patient cases using readily available tools. This pilot study provides a framework for an automated diagnostic technique for 3D visualization and morphological analysis of tumor biology which is traditionally done using 2D analysis.