Cancer tissues differ from normal tissues in a myriad of ways including cellular changes in size, morphology, and type/density of membrane and cytosolic proteins, which will accordingly change the refractive index of cells. There are also important differences in tissue organization, perhaps most notably the increase and morphologic changes in capillary vessels and extracellular matrix organization. Our laboratory has recently developed a novel technology and early prototype that can be used to detect some of these physiologic changes by collecting the light scattering intensities over a range of scattering angles from animal and human colonic tissues. To classify tumors, we employed multivariate analysis tools including Principal Component Analysis and Support Vector Machines to help discriminate between healthy and cancerous tissue samples. The preliminary results revealed a consistent and relatively accurate classification of tissues. These findings show promise for the noninvasive, label free, low cost, and rapid (under a minute) detection of surface tumors.