Fluorescence and Multiphoton Imaging for Tissue Characterization of a Model of Postmenopausal Ovarian Cancer
AuthorSawyer, Travis W
Koevary, Jennifer W
Howard, Caitlin C
Austin, Olivia J
Rice, Photini F S
Hutchens, Gabrielle V
Chambers, Setsuko K
Connolly, Denise C
Barton, Jennifer K
AffiliationUniv Arizona, James C Wyant Coll Opt Sci
Univ Arizona, Dept Biomed Engn
Univ Arizona, Coll Med
MetadataShow full item record
CitationSawyer, T.W., Koevary, J.W., Howard, C.C., Austin, O.J., Rice, P.F.S., Hutchens, G.V., Chambers, S.K., Connolly, D.C. and Barton, J.K. (2020), Fluorescence and Multiphoton Imaging for Tissue Characterization of a Model of Postmenopausal Ovarian Cancer. Lasers Surg Med. doi:10.1002/lsm.23251
JournalLASERS IN SURGERY AND MEDICINE
Rights© 2020 Wiley Periodicals, Inc.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractBackground and Objectives To determine the efficacy of targeted fluorescent biomarkers and multiphoton imaging to characterize early changes in ovarian tissue with the onset of cancer. Study Design/Materials and Methods A transgenic TgMISIIR-TAg mouse was used as an animal model for ovarian cancer. Mice were injected with fluorescent dyes to bind to the folate receptor alpha, matrix metalloproteinases, and integrins. Half of the mice were treated with 4-vinylcyclohexene diepoxide (VCD) to simulate menopause. Widefield fluorescence imaging (WFI) and multiphoton imaging of the ovaries and oviducts were conducted at 4 and 8 weeks of age. The fluorescence signal magnitude was quantified, and texture features were derived from multiphoton imaging. Linear discriminant analysis was then used to classify mouse groups. Results Imaging features from both fluorescence imaging and multiphoton imaging show significant changes (P < 0.01) with age, VCD treatment, and genotype. The classification model is able to classify different groups to accuracies of 75.53%, 69.53%, and 86.76%, for age, VCD treatment, and genotype, respectively. Building a classification model using features from multiple modalities shows marked improvement over individual modalities. Conclusions This study demonstrates that using WFI with targeted biomarkers, and multiphoton imaging with endogenous contrast shows promise for detecting early changes in ovarian tissue with the onset of cancer. The results indicate that multimodal imaging can provide higher sensitivity for classifying tissue types than using single modalities alone. Lasers Surg. Med. (c) 2020 Wiley Periodicals, Inc.
Note12 month embargo; published online: 20 April 2020
VersionFinal accepted manuscript
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