Análise de textura: uma potencial ferramenta para diferenciar tumores cerebrais primários e metástase cerebral solitária
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Data
2022-09-08
Autores
Souza, Sérgio Augusto Santana de [UNESP]
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Editor
Universidade Estadual Paulista (Unesp)
Resumo
Tumores cerebrais são considerados um grave problema de saúde pública e sua diferenciação constitui um grande desafio diagnóstico. Geralmente, o diagnóstico definitivo só pode ser alcançado a partir de uma biópsia estereotáxica do cérebro, o que acarreta certo risco de mortalidade. Assim, é necessário o desenvolvimento de estratégias alternativas para a diminuição de procedimentos invasivos. Nesse aspecto, o aprendizado de máquinas (AM) aplicado a imagens biomédicas por meio de análise de texturas (AT) tem sido amplamente utilizado na medicina diagnóstica para classificação ou diferenciação de lesões. Essa pesquisa teve como objetivo a criação de um método de extração de texturas e aprendizado de máquina aplicado a neuroimagens para diferenciação de tumores cerebrais. O banco de dados foi constituído a partir de exames de Ressonância Magnética (RM) de encéfalo de pacientes do Hospital das Clínicas de Botucatu. O bando de dados foi dividido em dois grupos, tumores primários (n = 38) e metástase cerebral solitária (n = 58). As sequências utilizadas foram: imagem ponderada em difusão (DWI), sequência de recuperação de inversão atenuada por fluido (FLAIR), imagem ponderada em T1 com e sem contraste endovenoso, imagem ponderada em T2. Radiologistas com mais de 15 anos de experiência realizaram na região tumoral, segmentações manuais nas regiões de interesse (ROI) de 10 x 10 pixels. Para cada ROI, 40 parâmetros de textura foram extraídos através do software Matlab® (v 2017) e aplicados a cinco métodos de AM diferentes: naive bayes, support vector machine (SVM), stochastic gradient descente (SGD), random forest, e tree. Os métodos de AM classificaram os grupos com boa diferenciação de até 97,5% da área sob as características do operador receptor (ROC) usando o SVM na sequência DWI e 94,9% de precisão usando o gradiente descendente estocástico na sequência ponderada em T1. O método proposto apresentou uma classificação confiável para a investigação de lesões tumorais e trouxe contribuições originais à comunidade cientifica com grande potencial de suporte ao diagnóstico clínico diferencial.
Brain tumors are considered a serious public health problem and their differentiation constitutes a major diagnostic challenge. Generally, a definitive diagnosis can only be reached from a stereotaxic brain biopsy, which carries a certain risk of mortality. Thus, it is necessary to develop alternative strategies to reduce invasive procedures. In this aspect, machine learning (ML) applied to biomedical images through texture analysis (TA) has been widely used in diagnostic medicine for classification or differentiation of lesions. In this way, we propose a texture extraction and machine learning method applied to neuroimaging to differentiate brain tumors. The database consisted of images from routine exams at “Hospital das Clínicas de Botucatu”. We used magnetic resonance imaging (MRI) scans of the brain. The dataset was divided into two groups, primary tumors (n=38) and solitary brain metastasis (n=58). The sequences used were: diffusion-weighted image (DWI), fluid-attenuated inversion recovery (FLAIR), T1-weighted, T1-weighted SE gadolinium-enhanced, and T2-weighted images. Radiologists with more than 15 years of experience performed, in tumoral regions, manual segmentation of regions of interest (ROI) of 10 x 10 tumor pixels. For each ROI, 40 texture parameters were extracted using Matlab® R2020b (license: 40962010) and applied to five different AM methods: naive bayes, support vector machine (SVM), stochastic gradient descent (SGD), random forest, and tree. The ML methods classified the groups with good differentiation of up to 97.5% of the area under the characteristics of the receiver operator (ROC). using the SVM on the DWI sequence and 94.9% accuracy using the stochastic gradient descent on the T1-weighted sequence. The proposed method presented a reliable classification for the investigation of tumoral lesions and brought original contributions to the scientific community with great potential to aid in the differential clinical diagnosis.
Brain tumors are considered a serious public health problem and their differentiation constitutes a major diagnostic challenge. Generally, a definitive diagnosis can only be reached from a stereotaxic brain biopsy, which carries a certain risk of mortality. Thus, it is necessary to develop alternative strategies to reduce invasive procedures. In this aspect, machine learning (ML) applied to biomedical images through texture analysis (TA) has been widely used in diagnostic medicine for classification or differentiation of lesions. In this way, we propose a texture extraction and machine learning method applied to neuroimaging to differentiate brain tumors. The database consisted of images from routine exams at “Hospital das Clínicas de Botucatu”. We used magnetic resonance imaging (MRI) scans of the brain. The dataset was divided into two groups, primary tumors (n=38) and solitary brain metastasis (n=58). The sequences used were: diffusion-weighted image (DWI), fluid-attenuated inversion recovery (FLAIR), T1-weighted, T1-weighted SE gadolinium-enhanced, and T2-weighted images. Radiologists with more than 15 years of experience performed, in tumoral regions, manual segmentation of regions of interest (ROI) of 10 x 10 tumor pixels. For each ROI, 40 texture parameters were extracted using Matlab® R2020b (license: 40962010) and applied to five different AM methods: naive bayes, support vector machine (SVM), stochastic gradient descent (SGD), random forest, and tree. The ML methods classified the groups with good differentiation of up to 97.5% of the area under the characteristics of the receiver operator (ROC). using the SVM on the DWI sequence and 94.9% accuracy using the stochastic gradient descent on the T1-weighted sequence. The proposed method presented a reliable classification for the investigation of tumoral lesions and brought original contributions to the scientific community with great potential to aid in the differential clinical diagnosis.
Descrição
Palavras-chave
Tumores cerebrais primários, Metástase solitária cerebral, Parâmetros de textura, Aprendizado de máquina, Primary brain tumors