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.

Abstract: INTRODUCTION: Rhinosporidiosis is a chronic infection of the mucous membrane and is caused by Rhinosporidium seeberi, an aquatic mesomycetozoan. The mode of infection is probably transepithelial penetration. The large number of rivers and lakes and the strong presence of riparian populations in the State of Maranhão are strong predisposing factors for rhinosporidiosis. METHODS: A 5-year retrospective study was conducted in a tertiary medical center situated in Maranhão, Northeast Brazil. Twenty-five Maranhense patients diagnosed with rhinosporidiosis were analyzed. RESULTS: Most of the patients were children, adolescents and young adults (age range: 7-24 years, mean age: 14 years). The majority of the participants were male (84%), brown (76%), and students (92%). All lesions involved the entire nasal cavity and presented with a vascular polypoid mass. All patients were treated by surgical excision of the lesions. CONCLUSIONS: Rhinosporidiosis affects younger age groups, especially students from the countryside and the outskirts of urban areas. This study will aid and guide physicians in diagnosing and treating this infection in endemic areas.