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

Autor: Sousa-Neto SS; Departmento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil., Nakamura TCR; Institute of Science and Technology, Federal University of São Paulo (ICT-UNIFESP), São José dos Campos, São Paulo, Brazil., Giraldo-Roldan D; Departmento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil., Dos Santos GC; Institute of Science and Technology, Federal University of São Paulo (ICT-UNIFESP), São José dos Campos, São Paulo, Brazil., Fonseca FP; Department of Oral Surgery and Pathology, School of Dentistry, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil., de Cáceres CVBL; Department of Oral Surgery and Pathology, School of Dentistry, Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil., Rangel ALCA; Universidade Estadual Do Oeste Do Paraná (UNIOESTE), Cascavel, Paraná, Brazil., Martins MD; Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande Do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Martins MAT; Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande Do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Gabriel AF; Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande Do Sul, Porto Alegre, Rio Grande do Sul, Brazil., Zanella VG; Department of Head and Neck Surgery, Santa Rita Hospital, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre, Rio Grande do Sul, Brazil., Santos-Silva AR; Departmento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil., Lopes MA; Departmento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil., Kowalski LP; Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, Brazil.; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, Brazil., Araújo ALD; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, Brazil., Moraes MC; Institute of Science and Technology, Federal University of São Paulo (ICT-UNIFESP), São José dos Campos, São Paulo, Brazil., Vargas PA; Departmento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
Jazyk: angličtina
Zdroj: Head & neck [Head Neck] 2024 Oct 27. Date of Electronic Publication: 2024 Oct 27.
DOI: 10.1002/hed.27971
Abstrakt: Aims: To develop a model capable of distinguishing carcinoma ex-pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture.
Methods and Results: 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).
Conclusions: 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.
(© 2024 Wiley Periodicals LLC.)
Databáze: MEDLINE
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