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

Author(s)

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

• Document type: Journal article
• Source: 2021 SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE (SBFOTON IOPC); v. N/A, p. 3-pg., 2021-01-01.

Abstract

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)

• FAPESP's process: 05/51689-2 - Optics and Photonics Research Center at UNICAMP
• Grantee: Hugo Luis Fragnito
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 17/50332-0 - Scientific, technological and infrastructure qualification in radiopharmaceuticals, radiation and entrepreneurship for health purposes (PDIp)
• Grantee: Marcelo Linardi
• Support Opportunities: Research Grants - State Research Institutes Modernization Program