Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex-Pleomorphic Adenoma

• Published in: Head & neck 2024-10
• Main Authors: Sousa-Neto, Sebastião Silvério, Nakamura, Thaís Cerqueira Reis, Giraldo-Roldan, Daniela, Dos Santos, Giovanna Calabrese, Fonseca, Felipe Paiva, de Cáceres, Cinthia Verónica Bardález López, Rangel, Ana Lúcia Carrinho Ayroza, Martins, Manoela Domingues, Martins, Marco Antonio Trevizani, Gabriel, Amanda De Farias, Zanella, Virgilio Gonzales, Santos-Silva, Alan Roger, Lopes, Marcio Ajudarte, Kowalski, Luiz Paulo, Araújo, Anna Luíza Damaceno, Moraes, Matheus Cardoso, Vargas, Pablo Agustin
• Format: Article
• Language: English
• ISSN: 1043-3074; 1097-0347; 1097-0347
• DOI: 10.1002/hed.27971

To develop a model capable of distinguishing carcinoma ex-pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture. A cohort of 83 Brazilian patients, divided into carcinoma ex-pleomorphic adenoma (n = 42) and pleomorphic adenoma (n = 41), was used for training a convolutional neural network. The whole-slide images were annotated and fragmented into 743 869 (carcinoma ex-pleomorphic adenomas) and 211 714 (pleomorphic adenomas) patches, measuring 224 × 224 pixels. Training (80%), validation (10%), and test (10%) subsets were established. The Residual Neural Network (ResNet)-50 was chosen for its recognition and classification capabilities. The training and validation graphs, and parameters derived from the confusion matrix, were evaluated. The loss curve recorded 0.63, and the accuracy reached 0.93. Evaluated parameters included specificity (0.88), sensitivity (0.94), precision (0.96), F1 score (0.95), and area under the curve (0.97). The study underscores the potential of ResNet-50 in the microscopic diagnosis of carcinoma ex-pleomorphic adenoma. The developed model demonstrated strong learning potential, but exhibited partial limitations in generalization, as indicated by the validation curve. In summary, the study established a promising baseline despite limitations in model generalization. This indicates the need to refine methodologies, investigate new models, incorporate larger datasets, and encourage inter-institutional collaboration for comprehensive studies in salivary gland tumors.