Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Christos Fragopoulos, Abraham Pouliakis, Christos Meristoudis, Emmanouil Mastorakis, Niki Margari, Nicolaos Chroniaris, Nektarios Koufopoulos, Alexander G. Delides, Nicolaos Machairas, Vasileia Ntomi, Konstantinos Nastos, Ioannis G. Panayiotides (+3 others)
2020 Journal of Thyroid Research  
Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these
more » ... , nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.
doi:10.1155/2020/5464787 pmid:33299540 pmcid:PMC7707952 fatcat:yngeq4pdsjclnfl7xcda3lhhue