A Study of Thyroid Fine Needle Aspiration of Follicular Adenoma in the “Atypia of Undetermined Significance” Bethesda Category Using Digital Image Analysis

BACKGROUND: Originally designed for computerized image analysis, ThinPrep is underutilized in that role outside gynecological cytology. It can be used to address the inter/intra-observer variability in the evaluation of thyroid fine needle aspiration (TFNA) biopsy and help pathologists to gain additional insight into thyroid cytomorphology. METHODS: We designed and validated a feature engineering and supervised machine learning-based digital image analysis method using ImageJ and Python scikit-learn . The method was trained and validated from 400 low power (100x) and 400 high power (400x) images generated from 40 TFNA cases. RESULT: The area under the curve (AUC) for receiver operating characteristics (ROC) is 0.75 (0.74–0.82) for model based from low-power images and 0.74 (0.69–0.79) for the model based from high-power images. Cytomorphologic features were synthesized using feature engineering and when performed in isolation, they achieved AUC of 0.71 (0.64–0.77) for chromatin, 0.70 (0.64–0.73) for cellularity, 0.65 (0.60–0.69) for cytoarchitecture, 0.57 (0.51–0.61) for nuclear size, and 0.63 (0.57–0.68) for nuclear shape. CONCLUSION: Our study proves that ThinPrep is an excellent preparation method for digital image analysis of thyroid cytomorphology. It can be used to quantitatively harvest morphologic information for diagnostic purpose.