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Nakamura, Thais Cerqueira Reis ; Sousa-Neto, Sebastiao Silverio ; Dos Santos, Giovanna Calabrese ; Giraldo-Roldan, Daniela ; Rangel, Ana Lucia Carrinho Ayroza ; Martins, Manoela Domingues ; Martins, Marco Antonio Trevizani ; Lopes, Marcio Ajudarte ; Kowalski, Luiz Paulo ; Santos-Silva, Alan Roger ; Araujo, Anna Luiza Damaceno ; Vargas, Pablo Agustin ; Moraes, Matheus Cardoso

• Tipo de documento: Artigo Científico
• Fonte: Head and Neck Pathology; v. 19, n. 1, p. 14-pg., 2025-11-10.

Resumo

ObjectiveThe study aimed to compare multiple convolutional neural networks architectures for their classification performance in distinguishing salivary gland tumors, pleomorphic adenoma and carcinoma ex pleomorphic adenoma, using whole-slide images.MethodsA cross-sectional study using 107 hematoxylin and eosin stained whole-slide images from 83 patients diagnosed with pleomorphic adenoma (n = 41) and carcinoma ex pleomorphic adenoma (n = 42) was conducted. Eight convolutional neural networks models (ResNet50, InceptionV3, VGG16, Xception, MobileNet, DenseNet121, EfficientNetB0, and EfficientNetV2B0) were applied, trained, and evaluated. A total of 955,583 patches (224 x 224 pixels) were generated and not-randomly divided into training (80%), validation (10%), and testing (10%) subsets. Performance and generalization were assessed through analysis of training and validation accuracy and loss curves. Testing phase evaluation included multiple metrics-such as precision, sensitivity, specificity, and others.ResultsResNet50 achieved the highest performance in 7 out of 9 metrics. DenseNet121 also delivered strong results, surpassing ResNet50 in specificity (94% vs. 93%) while matching its balanced accuracy (93%), precision (98%), and area under the receiver operating characteristic curve (0.97). Both exhibited comparable performance in loss (0.63 vs. 0.65), precision (98% vs. 98%), sensitivity (94% vs. 92%), and F1 score (0.96 vs. 0.95), demonstrating near-equivalent diagnostic capability.ConclusionThis study demonstrates strong potential of convolutional neural networks for classifying salivary gland tumors, with ResNet50 and DenseNet121 showing notable performance. Future work should focus on expanding datasets, improving generalization, exploring ensemble methods, and incorporating interpretability to enhance clinical relevance with clinical and radiographic data. (AU)

• Processo FAPESP: 23/13797-6 - Estratégias de quimioterapia direcionada para a eliminação das celulas-tronco tumorais em pacientes jovens com carcinoma espinocelular oral
• Beneficiário: Sebastião Silvério de Sousa Neto
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 21/14585-7 - Inteligência artificial aplicada ao diagnóstico clínico e histopatológico do Câncer de Cabeça e Pescoço
• Beneficiário: Anna Luiza Damaceno Araujo
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 09/53839-2 - Criação do Laboratório de Patologia Digital através do uso do escaneador de lâminas histológicas (Aperio® Scanscope CS)
• Beneficiário: Oslei Paes de Almeida
• Modalidade de apoio: Auxílio à Pesquisa - Programa Equipamentos Multiusuários