Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-9964
Authors: | Kriegsmann, Mark Kriegsmann, Katharina Steinbuss, Georg Zgorzelski, Christiane Albrecht, Thomas Heinrich, Stefan Farkas, Stefan Roth, Wilfried Dang, Hien Hausen, Anne Gaida, Matthias M. |
Title: | Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis |
Online publication date: | 24-Jan-2024 |
Year of first publication: | 2023 |
Language: | english |
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: | 610 Medizin 610 Medical sciences |
Institution: | Johannes Gutenberg-Universität Mainz |
Department: | FB 04 Medizin |
Place: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-9964 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | Clinical and translational medicine 13 7 |
Pages or article number: | e1299 |
Publisher: | Wiley |
Publisher place: | Hoboken, NJ |
Issue date: | 2023 |
ISSN: | 2001-1326 |
Publisher URL: | https://doi.org/10.1002/ctm2.1299 |
Publisher DOI: | 10.1002/ctm2.1299 |
Appears in collections: | DFG-491381577-G |
Files in This Item:
File | Description | Size | Format | ||
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implementation_of_deep_learni-20240123154450414.pdf | 2.08 MB | Adobe PDF | View/Open |