Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures.

ABSTRACT: Intraoperative frozen section analysis can be used to improve the accuracy of tumour margin estimation during cancer resection surgery through rapid processing and pathological assessment of excised tissue. Its applicability is limited in some cases due to the additional risks associated with prolonged surgery, largely from the time-consuming staining procedure. Our work uses a measurable property of bulk tissue to bypass the staining process: as tumour cells proliferate, they influence the surrounding extra-cellular matrix, and the resulting change in elastic modulus provides a signature of the underlying pathology. In this work we accurately localise atomic force microscopy measurements of human liver tissue samples and train a generative adversarial network to infer elastic modulus from

SUBMITTER: Neary-Zajiczek L 

PROVIDER: S-EPMC8577366 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature