Artificial intelligence applied to the clinical and histopathological diagnosis of Head and Neck Cancer

• Grant number: 21/14585-7
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: October 01, 2022
• End date: September 30, 2025
• Field of knowledge: Health Sciences - Dentistry
• Principal Investigator: Luiz Paulo Kowalski
• Grantee: Anna Luiza Damaceno Araujo
• Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

The digitization of histological slides favors the migration of diagnostic practice to a fully digital environment. In this context, computer-aided diagnosis (CAD), with a focus on quantitative analysis, opens paths for diagnosis through artificial intelligence (AI) driven by technological development that favors strategies based on neural networks that uses images in this process. This study aims to develop and evaluate Deep Learning (DL) models to support the diagnosis of C&P diseases through digital histological analysis and clinical images. The research will be carried out at the Faculty of Medicine of the University of São Paulo - FMUSP (São Paulo, São Paulo, Brazil) and the sample will be obtained from different centers with the collaboration and support of Brazilian and foreign institutions. The algorithms will be tunned and implemented by professionals in the area of Biomedical Engineering at the Institute of Science and Technology of the Federal University of São Paulo, São José dos Campos Unit (ICT -UNIFESP) and the Institute of Mathematics and Computer Sciences at the University of São Paulo, São Carlos Unit (ICMC-USP). A sample will be selected, retrospectively, by surveying lesions compatible with histological diagnosis within the following categories: (1) oral potentially malignant disorders, (2) squamous cell carcinoma, (3) salivary gland tumors and (4) lymphomas. The deep learning neural networks used in this context will be AlexNet, ResNet, DenseNet, Inception, Xception and MobileNet. Eventually, the best architecture will be selected. The performance of each approach used will be calculated using parameters of accuracy, sensitivity, specificity, F1 score coefficient, ROC curve graphs and confusion matrices. The prognostic significance will be investigated using Kaplan-Meier curves and Cox summit analyses, performing univariate and multivariate analyses of digital, clinical and pathological parameters. (AU)

Scientific publications

(8)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ARAUJO, ANNA LUIZA DAMACENO; DE SOUZA, EDUARDO SANTOS CARLOS; FAUSTINO, ISABEL SCHAUSLTZ PEREIRA; SALDIVIA-SIRACUSA, CRISTINA; BRITO-SARRACINO, TAMIRES; ADUDURATE LOPES, MARCIO; VARGAS, PABLO AGUSTIN; PEARSON, ALEXANDER T.; KOWALSKI, LUIZ PAULO; DE CARVALHO, ANDRE CARLOS PONCE DE LEON FERREIRA; et al. Clinicians' perception of oral potentially malignant disorders: a pitfall for image annotation in supervised learning. ORAL SURGERY ORAL MEDICINE ORAL PATHOLOGY ORAL RADIOLOGY, v. 136, n. 3, p. 7-pg., 2023-09-01. (22/07276-0, 21/14585-7, 09/53839-2)

SALDIVIA-SIRACUSA, CRISTINA; ARAUJO, ANNA LUIZA DAMACENO; ARBOLEDA, LADY PAOLA ARISTIZABAL; ABRANTES, THAMIRIS; PINTO, MARIANA BITU RAMOS; MENDONCA, NATHALIA; CORDERO-TORRES, KARINA; GILLIGAN, GERARDO; PIEMONTE, EDUARDO; PANICO, RENE; et al. Insights into incipient oral squamous cell carcinoma: a comprehensive south-american study. MEDICINA ORAL PATOLOGIA ORAL Y CIRUGIA BUCAL, v. 29, n. 4, p. 9-pg., 2024-07-01. (22/13069-8, 21/14585-7)

DE SOUZA, LUCAS LACERDA; FONSECA, FELIPE PAIVA; ARAUJO, ANNA LUIZA DAMACENO; LOPES, MARCIO AJUDARTE; VARGAS, PABLO AGUSTIN; KHURRAM, SYED ALI; KOWALSKI, LUIZ PAULO; DOS SANTOS, HARIM TAVARES; WARNAKULASURIYA, SAMAN; DOLEZAL, JAMES; et al. Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review. JOURNAL OF ORAL PATHOLOGY & MEDICINE, v. 52, n. 3, p. 9-pg., 2023-03-08. (21/14585-7, 22/03123-5)

ARAUJO, ANNA LUIZA DAMACENO; SANTOS-SILVA, ALAN ROGER; KOWALSKI, LUIZ PAULO. Diagnostic Accuracy of Liquid Biopsy for Oral Potentially Malignant Disorders and Head and Neck Cancer: an Overview of Systematic Reviews. CURRENT ONCOLOGY REPORTS, v. 25, n. 4, p. 14-pg., 2023-02-15. (21/14585-7)

ARAUJO, ANNA LUIZA DAMACENO; MORAES, MATHEUS CARDOSO; PEREZ-DI-OLIVEIRA, MARIA EDUARDA; DA SILVA, VIVIANE MARIANO; SALDIVIA-SIRACUSA, CRISTINA; PEDROSO, CAIQUE MARIANO; LOPES, MARCIO AJUDARTE; VARGAS, PABLO AGUSTIN; KOCHANNY, SARA; PEARSON, ALEXANDER; et al. Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis. Oral Oncology, v. 140, p. 15-pg., 2023-04-04. (22/07276-0, 21/14585-7, 09/53839-2)

ARAUJO, ANNA LUIZA DAMACENO; DA SILVA, VIVIANE MARIANO; MORAES, MATHEUS CARDOSO; DE AMORIM, HENRIQUE ALVES; FONSECA, FELIPE PAIVA; ST'ANA, MARIA SISSA PEREIRA; MESQUITA, RICARDO ALVES; MARIZ, BRUNO AUGUSTO LINHARES ALMEIDA; PONTES, HELDER ANTONIO REBELO; DE SOUZA, LUCAS LACERDA; et al. The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal. JOURNAL OF ORAL PATHOLOGY & MEDICINE, v. N/A, p. 8-pg., 2023-09-15. (21/14585-7, 09/53839-2)

DAMACENO ARAUJO, ANNA LUIZA; DA SILVA, VIVIANE MARIANO; KUDO, MAIRA SUZUKA; CARLOS DE SOUZA, EDUARDO SANTOS; SALDIVIA-SIRACUSA, CRISTINA; GIRALDO-ROLDAN, DANIELA; LOPES, MARCIO AJUDARTE; VARGAS, PABLO AGUSTIN; KHURRAM, SYED ALI; PEARSON, ALEXANDER T.; et al. Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach. JOURNAL OF ORAL PATHOLOGY & MEDICINE, v. 52, n. 2, p. 10-pg., 2023-01-04. (09/53839-2, 21/14585-7)

GIRALDO-ROLDAN, DANIELA; DOS SANTOS, GIOVANNA CALABRESE; ARAUJO, ANNA LUIZA DAMACENO; NAKAMURA, THAIS CERQUEIRA REIS; PULIDO-DIAZ, KATYA; LOPES, MARCIO AJUDARTE; SANTOS-SILVA, ALAN ROGER; KOWALSKI, LUIZ PAULO; MORAES, MATHEUS CARDOSO; VARGAS, PABLO AGUSTIN. Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images. Head and Neck Pathology, v. 18, n. 1, p. 9-pg., 2024-10-28. (09/53839-2, 21/14585-7)