Inflammatory lesions and brain tumors: Is it possible to differentiate them based on texture features in magnetic resonance imaging?

Alves, Allan Felipe Fattori [UNESP]; de Arruda Miranda, José Ricardo [UNESP]; Reis, Fabiano; de Souza, Sergio Augusto Santana [UNESP]; Alves, Luciana Luchesi Rodrigues [UNESP]; de Moura Feitoza, Laisson; de Souza de Castro, José Thiago; de Pina, Diana Rodrigues [UNESP]

Inflammatory lesions and brain tumors: Is it possible to differentiate them based on texture features in magnetic resonance imaging?

Arquivos

S1678-91992020000100328.pdf (1.92 MB)

Data

2020-01-01

Autores

Alves, Allan Felipe Fattori

de Arruda Miranda, José Ricardo

Reis, Fabiano

de Souza, Sergio Augusto Santana

Alves, Luciana Luchesi Rodrigues

de Moura Feitoza, Laisson

de Souza de Castro, José Thiago

de Pina, Diana Rodrigues

Tipo

Artigo

Direito de acesso

Acesso aberto

Resumo

Background: Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. Magnetic resonance imaging (MRI) provides good cerebral soft-tissue contrast detection and diagnostic sensitivity. Inflammatory lesions and tumors are common brain diseases that may present a similar pattern of a cerebral ring enhancing lesion on MRI, and non-enhancing core (which may reflect cystic components or necrosis) leading to misdiagnosis. Texture analysis (TA) and machine learning approaches are computer-aided diagnostic tools that can be used to assist radiologists in such decisions. Methods: In this study, we combined texture features with machine learning (ML) methods aiming to differentiate brain tumors from inflammatory lesions in magnetic resonance imaging. Retrospective examination of 67 patients, with a pattern of a cerebral ring enhancing lesion, 30 with inflammatory, and 37 with tumoral lesions were selected. Three different MRI sequences and textural features were extracted using gray level co-occurrence matrix and gray level run length. All diagnoses were confirmed by histopathology, laboratorial analysis or MRI. Results: The features extracted were processed for the application of ML methods that performed the classification. T1-weighted images proved to be the best sequence for classification, in which the differentiation between inflammatory and tumoral lesions presented high accuracy (0.827), area under ROC curve (0.906), precision (0.837), and recall (0.912). Conclusion: The algorithm obtained textures capable of differentiating brain tumors from inflammatory lesions, on T1-weghted images without contrast medium using the Random Forest machine learning classifier.