Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears

• Published in: Journal of pathology informatics Vol. 9; no. 1; p. 43
• Main Authors: Sanyal, Parikshit, Mukherjee, Tanushri, Barui, Sanghita, Das, Avinash, Gangopadhyay, Prabaha
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2018; Wolters Kluwer India Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Arrays; Artificial intelligence; Artificial neural networks; Automation; Cellular biology; Classification; Cytology; Datasets; Diagnostic systems; fine-needle aspiration cytology; Image classification; Learning theory; Lesions; Machine learning; Medical imaging; Microphotographs; neural network; Neural networks; Object recognition; Original; Programming languages; Software; thyroid; Thyroid cancer; Thyroid gland; Training; cytology; fine-needle aspiration cytology; image classification; thyroid; Artificial intelligence; neural network
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/jpi.jpi_43_18

Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology. Aim: The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. Subjects and Methods: An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40). Results: The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback. Conclusion: With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.