Abstract
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.
Highlights
Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients
All 63 extracted features were analyzed with the statistical methods information gain ratio and Gini Index to select the five best-ranked features
Recall and area under the curve (AUC) the data between our two patient groups. Those five best-ranked were different in each Magnetic resonance imaging (MRI) sequence, wavelet entropy (EntropyWv) and the mean intensity of pixels remained constant in all five groups
Summary
Neuroimaging strategies are essential to locate, to elucidate the etiology, and to the follow up of brain disease patients. 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. Inflammatory lesions and tumors are common brain diseases that present a similar pattern of a cerebral ring enhancing lesion on MRI, leading to misdiagnosis on neuroimaging [1]. They may produce severe complications, disability, and economic burden. Neuroimaging modalities including magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) provide localization, determination of etiology, and the follow up of these diseases [4, 5]
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