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