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Autor(en) / Beteiligte
Titel
Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis
Ist Teil von
  • Clinical and translational medicine, 2023-07, Vol.13 (7), p.e1299-n/a
Ort / Verlag
United States: John Wiley & Sons, Inc
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
  • 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. Artificial intelligence algorithms allow the separation of anatomical, benign, and malignant structures in histological slides of the liver. Digital image analysis on case level revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38413/43059) and case accuracy of 94% (198/211). A large curated data set of liver histology is provided for further optimization and research. Our algorithm can be applied in surgical liver pathology supporting decision making in establishing the diagnosis.
Sprache
Englisch
Identifikatoren
ISSN: 2001-1326
eISSN: 2001-1326
DOI: 10.1002/ctm2.1299
Titel-ID: cdi_doaj_primary_oai_doaj_org_article_44754a9766534839b9554ca474ae4822

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