Prostate cancer comprises the second most common cancer in men. One of the most powerful and established prognostic indicators of adenocarcinoma of the prostate is the Gleason score, a subjective assessment of the pattern of tumor growth and extent of glandular differentiation in H&E stained histology slides. Despite being the most dominant prostate grading method in use, the Gleason score suffers from high variability between grading pathologists, and due to its 2D nature, fails to effectively capture potentially prognostic information contained in 3D glandular growth patterns. We have previously demonstrated that persistent homology, a subset of topological data analysis (TDA), is effective in generating a quantitative morphological descriptor capable of differentiating Gleason grade in 2D. By capturing glands as loops in 2D, and voids in 3D, persistent homology lends itself naturally to the assessment of 3D glandular growth patterns while maintaining a correspondence to their 2D analogue. Dual-view inverted selective plane illumination microscopy (diSPIM) with a fluorescent H&E analogue was leveraged for volumetric imaging of optically-cleared prostate biopsies. The two orthogonal views of the diSPIM system yielded isotropic resolution in all dimensions, facilitating reconstruction of tissue histology in 3D for quantitative morphological assessment by persistent homology. The use of a nuclei specific hematoxylin analog (DRAQ5), in addition to the isotropic resolution of the system, enabled accurate 3D nuclei segmentation, thereby facilitating application of persistent homology to the corresponding nuclei 3D point clouds. Through TDA a quantitative, reproducible descriptor for 3D prostate cancer morphology will be demonstrated.