Autoren:	Kriegsmann, Mark Kriegsmann, Katharina Steinbuss, Georg Zgorzelski, Christiane Albrecht, Thomas Heinrich, Stefan Farkas, Stefan Roth, Wilfried Dang, Hien Hausen, Anne Gaida, Matthias M.
Titel:	Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis
Online-Publikationsdatum:	24-Jan-2024
Erscheinungsdatum:	2023
Sprache des Dokuments:	Englisch
Zusammenfassung/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.
DDC-Sachgruppe:	610 Medizin 610 Medical sciences
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 04 Medizin
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-9964
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart:	Scientific article
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Clinical and translational medicine 13 7
Seitenzahl oder Artikelnummer:	e1299
Verlag:	Wiley
Verlagsort:	Hoboken, NJ
Erscheinungsdatum:	2023
ISSN:	2001-1326
URL der Originalveröffentlichung:	https://doi.org/10.1002/ctm2.1299
DOI der Originalveröffentlichung:	10.1002/ctm2.1299
Enthalten in den Sammlungen:	DFG-491381577-G