Texture analysis: A potential tool to differentiate primary brain tumors and solitary brain metastasis

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorInstitut Universitaire de Technologie-
Autor(es): dc.creatorSouza, S. A.S.-
Autor(es): dc.creatorGuassu, R. A.C.-
Autor(es): dc.creatorAlves, A. F.F.-
Autor(es): dc.creatorAlvarez, M.-
Autor(es): dc.creatorPitanga, L. C.C.-
Autor(es): dc.creatorReis, F.-
Autor(es): dc.creatorVacavant, A.-
Autor(es): dc.creatorMiranda, J. R.A.-
Autor(es): dc.creatorFilho, J. C. S. Trindade-
Autor(es): dc.creatorPina, D. R.-
Data de aceite: dc.date.accessioned2025-08-21T17:57:30Z-
Data de disponibilização: dc.date.available2025-08-21T17:57:30Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11042-023-17139-2-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303079-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303079-
Descrição: dc.descriptionWe propose a machine learning (ML) approach applied to texture features to differentiate primary brain tumors and solitary brain metastasis. Magnetic resonance imaging (MRI) exams of 96 patients were divided into primary tumors (38) and solitary brain metastasis (58). MRI sequences used: diffusion-weighted image (DWI), fluid-attenuated inversion recovery, T1-weighted, T1-weighted SE gadolinium-enhanced, and T2-weighted images. Regions of interest (ROIs) of 10 × 10 pixels were positioned within the tumors. For each ROI, 40 texture features were extracted and applied to five different ML methods: naive bayes, support vector machine (SVM), stochastic gradient descent, random forest, and tree. The ML methods classified the groups with good differentiation of up to 97.5% of the area under the receiver operator characteristics (ROC) for SVM as the best classifier, especially in the DWI sequence. The method has a reliable classification for the investigation of tumor lesions.-
Descrição: dc.descriptionDepartment of Biophysics and Pharmacology São Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus São Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionDepartment of Radiology School of Medical Sciences University of Campinas-
Descrição: dc.descriptionInstitut Universitaire de Technologie-
Descrição: dc.descriptionBotucatu Medical School São Paulo State University-
Descrição: dc.descriptionDepartment of Tropical Diseases and Imaging Diagnosis São Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionDepartment of Biophysics and Pharmacology São Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus São Paulo State University Julio de Mesquita Filho-
Descrição: dc.descriptionBotucatu Medical School São Paulo State University-
Descrição: dc.descriptionDepartment of Tropical Diseases and Imaging Diagnosis São Paulo State University Julio de Mesquita Filho-
Formato: dc.format39523-39535-
Idioma: dc.languageen-
Relação: dc.relationMultimedia Tools and Applications-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectPrimary brain tumors-
Palavras-chave: dc.subjectSolitary brain metastasis-
Palavras-chave: dc.subjectTexture analysis-
Título: dc.titleTexture analysis: A potential tool to differentiate primary brain tumors and solitary brain metastasis-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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