External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study
: 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
Publisher: Elsevier
: 2024
: Journal of Pathology Informatics
: Journal of Pathology Informatics
: 100366
: 15
: 2153-3539
: 2153-3539
DOI: https://doi.org/10.1016/j.jpi.2024.100366(external)
: https://doi.org/10.1016/j.jpi.2024.100366(external)
: https://research.utu.fi/converis/portal/detail/Publication/381223889(external)
The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TCV 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.