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

Neary-Zajiczek, L; Essmann, C; Rau, A; Bano, S; Clancy, N; Jansen, M; Heptinstall, L; ... Stoyanov, D; + view all (2021) Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures. Nanoscale Advances 10.1039/d1na00527h. (In press). Green open access

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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 low-resolution images of unstained tissue sections. Pathology is predicted through unsupervised clustering of parameters characterizing the distributions of inferred values, achieving 89% accuracy for all samples based on the nominal assessment (n = 28), and 95% for samples that have been validated by two independent pathologists through post hoc staining (n = 20). Our results demonstrate that this technique could increase the feasibility of intraoperative frozen section analysis for use during resection surgery and improve patient outcomes.

Type: Article
Title: Stain-free identification of tissue pathology using a generative adversarial network to infer nanomechanical signatures
Open access status: An open access version is available from UCL Discovery
DOI: 10.1039/d1na00527h
Publisher version: https://doi.org/10.1039/D1NA00527H
Language: English
Additional information: © Royal Society of Chemistry 2021. This article is Open Access (http://creativecommons.org/licenses/by/3.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Surgical Biotechnology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10135399
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