Evaluation of machine learning models for the classification of breast cancer hormone receptors using micro-FTIR images

Autor(es)

del Valle, Matheus ; dos Santos, Moises Oliveira ; dos Santos, Sofia Nascimento ; Bernardes, Emerson Soares ; Zezell, Denise Maria ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2021 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE (SBFOTON IOPC); v. N/A, p. 3-pg., 2021-01-01.

Resumo

The breast cancer is the most incident cancer in women. Evaluation of hormone receptors expression plays an important role to outline treatment strategies. FTIR spectroscopy imaging may be employed as an additional technique, providing extra information to help physicians. In this work, estrogen and progesterone receptors expression were evaluated using tumors biopsies from human cell lines inoculated in mice. FTIR images were collect from histological sections, and six machine learning models were applied and assessed. Xtreme gradient boost and Linear Discriminant Analysis presented the best accuracies results, indicating to be potential models for breast cancer classification tasks. (AU)

• Processo FAPESP: 05/51689-2 - Centro de Pesquisa em Óptica e Fotônica - UNICAMP (CEPOF-UNICAMP)
• Beneficiário: Hugo Luis Fragnito
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 17/50332-0 - Capacitação científica, tecnológica e em infraestrutura em radiofármacos, radiações e empreendedorismo a serviço da saúde (PDIp)
• Beneficiário: Marcelo Linardi
• Modalidade de apoio: Auxílio à Pesquisa - Programa Modernização de Institutos Estaduais de Pesquisa