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Updated: Dec 3, 2025

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
Digital Versus Optical Diagnosis of Follicular Patterned Thyroid Lesions
Ayat Aloqaily1,2, Antonio Polonia3,4, Sofia Campelos3
1Institute of Molecular Pathology and Immunology, Ipatimup Diagnostics, University of Porto (IPATIMUP)/i3S, Rua Júlio Amaral de Carvalho 45, 4200-135, Porto, Portugal. Ayat.aloqaily@gmail.com.
Objectives:
To study the concordance between pathologists in the diagnosis of follicular patterned thyroid lesions using both digital and conventional optical settings.
Material And Methods:
Five pathologists reviewed 50 hematoxylin and eosin-stained slides of follicular patterned thyroid lesions using both digital (the D-Sight 2.0 scanner and navigator viewer) and conventional optical instruments with washout interval time.
Results:
The mean concordance rate with the ground truth (GT) was similar between conventional optical and digital observation (83.2 and 85.2%, respectively). The most frequent reason for diagnostic discordance with GT on both systems was the evaluation of nuclear features (69.1% for conventional optical observation and 59.4% for digital observation). The intraobserver diagnostic concordance mean was 86.8%. Time for digital observation (mean time per case = 2.9 ± 0.8 min) was higher than that for conventional optical observation (mean time per case = 2.0 ± 0.7 min). Interobserver correlation of measurements was higher in the digital observation than the conventional optical observation.
Conclusion:
Conventional optical and digital observation settings showed a comparable accuracy for the diagnosis of follicular patterned thyroid nodules, as well as substantial intraobserver agreement and a significant improvement in the reproducibility of the measurements that support the use of digital diagnosis in thyroid pathology. The origins underlying the variability of the diagnosis were the same in both conventional optical microscopy and digital pathology systems.

