Abstract A profound remodeling of the extracellular matrix (ECM) occurs in human ovarian cancer but it unknown how this affects tumor growth, where this understanding could lead to better diagnostics and therapeutic approaches. To this end, we utilized collagen-specific Second Harmonic Generation (SHG) imaging microscopy and optical scattering measurements to probe structural differences in the extracellular matrix of normal stroma, benign tumors, endometrioid tumors, and low and high-grade serous (LGS and HGS) tumors. The SHG and scattering metrics are sensitive to the organization of collagen on the sub-micron size. We found these sub-resolution determinations are consistent with the dualistic classification of type I and II serous tumors. However, type I endometrioid tumors have strongly differing ECM architecture than the serous malignancies. Moreover, our analyses are further consistent with LGS and benign tumors having similar etiology. Further, the SHG metrics and optical scattering measurements were then used to form a linear discriminant model to classify the tissues, and we obtained high accuracy (~90%) between the tissue types, and this delineation is superior to current clinical performance. To also quantify these alterations we implemented a new form of 3D texture analysis to classify the collagen morphologies in these tissues. We developed a tailored set of 3D filters which extract textural features in each tissue class and we achieved 83-91% accuracies for the six classes. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. We further investigate the role of these ECM alterations by using multiphoton excited (MPE) polymerization to fabricate biomimetic models to investigate operative cell-matrix interactions in invasion/metastasis. This process is akin to 3D printing except is performed at much higher resolution and with the proteins that comprise the native ECM. We specifically use this technique to create collagen scaffolds with complex, 3D submicron morphology as ovarian stromal models. The scaffold designs are derived directly from “blueprints” based on the SHG images of normal, high risk, benign tumors, and malignant ovarian tissues. The models are seeded with different cancer cell lines and this allows decoupling of the roles of cell characteristics (metastatic potential) and ECM structure and composition (normal vs cancer) on adhesion/migration dynamics. We found the malignant stroma structure promoted enhanced migration persistence and cell proliferation and also cytoskeletal alignment. Moreover, the method allows varying fiber properties such as fiber diameters and characteristic frequency as well as overall alignment. While alignment has been well studied, we found that the migration dynamics are highly dependent upon the morphological properties of the fibers themselves. These models cannot be synthesized by other conventional fabrication methods and we suggest the MPE image-based fabrication method will enable a variety of studies in cancer biology. This work is currently by a new grant from NCI R01 CA206561-01. Citation Format: Visar Ajeti, Manish Patankar, Kevin Eliceiri, and Paul J. Campagnola. QUANTITATIVE ASSESSMENT OF THE ROLE OF COLLAGEN ALTERATIONS IN OVARIAN CANCER [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr TMEM-015.