External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study

• Main Authors: Sebastian Stenman (Author), Sylvain Bétrisey (Author), Paula Vainio (Author), Jutta Huvila (Author), Mikael Lundin (Author), Nina Linder (Author), Anja Schmitt (Author), Aurel Perren (Author), Matthias S. Dettmer (Author), Caj Haglund (Author), Johanna Arola (Author), Johan Lundin (Author)
• Format: Book
• Published: Elsevier, 2024-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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 Description

2153-3539
10.1016/j.jpi.2024.100366