Head and neck pathology · 2025
Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.
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Nakamura Thaís Cerqueira Reis, Sousa-Neto Sebastião Silvério, Dos Santos Giovanna Calabrese, Giraldo-Roldán Daniela, Rangel Ana Lúcia Carrinho Ayroza, Martins Manoela Domingues, Martins Marco Antonio Trevizani, Lopes Marcio Ajudarte, Kowalski Luiz Paulo, Santos-Silva Alan Roger, Araújo Anna Luíza Damaceno, Vargas Pablo Agustin, Moraes Matheus Cardoso
Аннотация
OBJECTIVE: The 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.
METHODS: A 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 × 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.
RESULTS: ResNet50 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.
CONCLUSION: This 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.
METHODS: A 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 × 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.
RESULTS: ResNet50 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.
CONCLUSION: This 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.