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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
663
Deep-Learning-Based Screening and Ancillary Testing for Thyroid Cytopathology
David Dov1, Danielle Elliott Range2, Jonathan Cohen3
1I-Medata AI Center, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel; Department of Pathology, Duke University Medical Center, Durham, North Carolina.
The American Journal of Pathology
|August 23, 2023
Summary
A new deep-learning algorithm accurately analyzes thyroid fine-needle aspiration biopsies (FNABs) from whole-slide images. This AI tool improves diagnostic accuracy, reducing unnecessary surgeries for thyroid cancer.
Area of Science:
- Endocrinology
- Oncology
- Pathology
- Artificial Intelligence in Medicine
Background:
- Thyroid cancer is the most common endocrine malignancy.
- Fine-needle aspiration biopsies (FNABs) are crucial for preoperative risk assessment.
- Indeterminate FNAB results often lead to diagnostic challenges and unnecessary surgeries.
Purpose of the Study:
- To develop and validate a deep-learning algorithm for analyzing thyroid FNAB whole-slide images (WSIs).
- To assess the algorithm's performance in classifying determinate cases and aiding in the disambiguation of indeterminate cases.
Main Methods:
- Development of a deep-learning algorithm for analyzing thyroid FNAB WSIs.
- Testing on the largest reported dataset of thyroid FNAB WSIs.
- Validation using a separate dataset of consecutive FNABs from a full calendar year.
Main Results:
- The algorithm achieved clinical-grade performance, classifying 45.1% of WSIs as benign or malignant.
- It reduced indeterminate cases by reclassifying 21.3% as benign.
- Results demonstrated clinically acceptable margins of error for thyroid FNAB classification.
Conclusions:
- Deep learning applied to thyroid FNAB WSIs offers a powerful tool for improving diagnostic accuracy.
- The algorithm shows potential as an ancillary test to reduce diagnostic uncertainty and unnecessary surgeries.
- This AI-driven approach can enhance the preoperative risk assessment of thyroid nodules.
