Gutenberg Open Science: Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9964

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DC Field	Value	Language
dc.contributor.author	Kriegsmann, Mark	-
dc.contributor.author	Kriegsmann, Katharina	-
dc.contributor.author	Steinbuss, Georg	-
dc.contributor.author	Zgorzelski, Christiane	-
dc.contributor.author	Albrecht, Thomas	-
dc.contributor.author	Heinrich, Stefan	-
dc.contributor.author	Farkas, Stefan	-
dc.contributor.author	Roth, Wilfried	-
dc.contributor.author	Dang, Hien	-
dc.contributor.author	Hausen, Anne	-
dc.contributor.author	Gaida, Matthias M.	-
dc.date.accessioned	2024-01-24T10:00:07Z	-
dc.date.available	2024-01-24T10:00:07Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9982	-
dc.description.abstract	Introduction Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. Materials and methods In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non-neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. Results Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. Conclusions Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9964	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Clinical and translational medicine	de
jgu.journal.volume	13	de
jgu.journal.issue	7	de
jgu.pages.alternative	e1299	de
jgu.publisher.year	2023	-
jgu.publisher.name	Wiley	de
jgu.publisher.place	Hoboken, NJ	de
jgu.publisher.uri	https://doi.org/10.1002/ctm2.1299	de
jgu.publisher.issn	2001-1326	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1002/ctm2.1299	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-491381577-G

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