High-throughput image analysis in the diagnosis of papillary thyroid carcinoma.
BACKGROUND
Thyroid cancer is the most common endocrine cancer, and its incidence has been increasing worldwide in the past decades. The increasing demand in medicine for rapid and accurate diagnosis enabled the application of digital imaging analysis in order to increase workflow efficiency and accurate analyses. The present study aimed to automatically differentiate papillary thyroid carcinoma from normal thyroid cells using high-throughput image analysis.


METHODS
Images of cellular specimens were taken with a digital camera and were subsequently analyzed. Other software was used for machine-learning-based cellular diagnostics.


RESULTS
The two different classes were correctly identified with high sensitivity and specificity.


CONCLUSIONS
The data created offers great potential for an automated diagnosis. Diagn. Cytopathol. 2016;44:574-577. © 2016 Wiley Periodicals, Inc.
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha, ES, Brazil.
Bruno
B
Hosken
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha, ES, Brazil.
Denise
D
Coutinho Endringer
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha, ES, Brazil.
Bianca
B
Prandi Campagnaro
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha, ES, Brazil.
Tadeu
T
Uggere de Andrade
Pharmaceutical Sciences Graduate Program, Vila Velha University, Vila Velha, ES, Brazil.
Dominik
D
Lenz
D001331:Automation; D044963:Biopsy, Fine-Needle; D002277:Carcinoma / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D012984:Software; D013964:Thyroid Neoplasms / Q000473:pathology*
cellular diagnostics; image analysis; machine learning