1. External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study
- Author
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Sebastian Stenman, Sylvain Bétrisey, Paula Vainio, Jutta Huvila, Mikael Lundin, Nina Linder, Anja Schmitt, Aurel Perren, Matthias S. Dettmer, Caj Haglund, Johanna Arola, and Johan Lundin
- Subjects
Papillary thyroid carcinoma ,Artificial intelligence ,Digital pathology ,Deep learning ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Pathology ,RB1-214 - Abstract
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.
- Published
- 2024
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