Stenman, Sebastian; Bétrisey, Sylvain; Vainio, Paula; Huvila, Jutta; Lundin, Mikael; Linder, Nina; Schmitt, Anja; Perren, Aurel; Dettmer, Matthias S; Haglund, Caj; Arola, Johanna; Lundin, Johan (2024). External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study. Journal of pathology informatics, 15(100366) Elsevier 10.1016/j.jpi.2024.100366
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The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Service Sector > Institute of Pathology 04 Faculty of Medicine > Service Sector > Institute of Pathology > Clinical Pathology |
UniBE Contributor: |
Bétrisey, Sylvain, Schmitt Kurrer, Anja, Perren, Aurel, Dettmer, Matthias |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
2229-5089 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
04 Mar 2024 09:51 |
Last Modified: |
05 Mar 2024 06:13 |
Publisher DOI: |
10.1016/j.jpi.2024.100366 |
PubMed ID: |
38425542 |
Uncontrolled Keywords: |
Artificial intelligence Deep learning Digital pathology Papillary thyroid carcinoma |
BORIS DOI: |
10.48350/193683 |
URI: |
https://boris.unibe.ch/id/eprint/193683 |
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