Feasibility study of ResNet‐50 in the distinction of intraoral neural tumors using histopathological images

Author:

dos Santos Giovanna Calabrese1ORCID,Araújo Anna Luíza Damaceno2ORCID,de Amorim Henrique Alves1ORCID,Giraldo‐Roldán Daniela3ORCID,de Sousa‐Neto Sebastião Silvério3ORCID,Vargas Pablo Agustin3ORCID,Kowalski Luiz Paulo24ORCID,Santos‐Silva Alan Roger3ORCID,Lopes Marcio Ajudarte3ORCID,Moraes Matheus Cardoso1ORCID

Affiliation:

1. Institute of Science and Technology, Federal University of São Paulo (ICT‐UNIFESP) São Paulo Brazil

2. Head and Neck Surgery Department University of São Paulo Medical School São Paulo Brazil

3. Departamento de Diagnóstico Oral, Faculdade de Odontologia de Piracicaba Universidade Estadual de Campinas (FOP‐UNICAMP) Piracicaba São Paulo Brazil

4. Department of Head and Neck Surgery and Otorhinolaryngology A.C. Camargo Cancer Center São Paulo Brazil

Abstract

AbstractBackgroundNeural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for the histopathological classification of the three most prevalent benign neural tumor types: neurofibroma, perineurioma, and schwannoma.MethodsA model was developed, trained, and evaluated for classification using the ResNet‐50 architecture, with a database of 30 whole‐slide images stained in hematoxylin and eosin (106, 782 patches were generated from and divided among the training, validation, and testing subsets, with strategies to avoid data leakage).ResultsThe model achieved an accuracy of 70% (64% normalized), and showed satisfactory results for differentiating two of the three classes, reaching approximately 97% and 77% as true positives for neurofibroma and schwannoma classes, respectively, and only 7% for perineurioma class. The AUROC curves for neurofibroma and schwannoma classes was 0.83%, and 0.74% for perineurioma. However, the specificity rate for the perineurioma class was greater (83%) than in the other two classes (neurofibroma with 61%, and schwannoma with 60%).ConclusionThis investigation demonstrated significant potential for proficient performance with a limitation regarding the perineurioma class (the limited feature variability observed contributed to a lower performance).

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

UNICAMP DEVELOPMENT FOUNDATION

Publisher

Wiley

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