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

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.

Educational Case: Brain abscess

“…Inflammatory brain lesions and tumors may have a similar pattern of ring enhancement around a non-enhancing core of cystic components or necrosis. 8 Glioblastoma tends to be more spherical than brain abscess, which can help differentiating these conditions. 9 Texture analysis of ring-enhancing lesions on MRI based on machine learning approaches are computer-aided diagnostic tools that can assist radiologists in differentiating inflammatory brain lesions from tumors.…”

“… 9 Texture analysis of ring-enhancing lesions on MRI based on machine learning approaches are computer-aided diagnostic tools that can assist radiologists in differentiating inflammatory brain lesions from tumors. 8 …”

A Survey of Brain Tumor Segmentation and Classification Algorithms

“…Furthermore, these tissues often have indistinguishable intensity features in structural MRI sequences, such as T1-w, T2-w, FLAIR. For instance, the difficulty in differentiating between the core tumor and associated inflammation was discussed [ 19 ]. In addition to that, Alves et al [ 19 ] demonstrated the difficulty in differentiating tumors using signal intensities alone.…”

“…For instance, the difficulty in differentiating between the core tumor and associated inflammation was discussed [ 19 ]. In addition to that, Alves et al [ 19 ] demonstrated the difficulty in differentiating tumors using signal intensities alone. They demonstrated using a case where two patients were diagnosed with two different brain tumor types due to both tumors have similar intensity features and both are surrounded by extensive edema.…”

Recognition of brain tumors in MRI images using texture analysis

“…The features used during texture analysis are mathematical parameters calculated from the distribution of pixels, which characterize the texture type in the image. The main image processing disciplines in which texture analysis techniques are used are classification, segmentation, and synthesis of dissimilar features including normal tissue, inflammatory regions and tumors ( Pietikäinen et al, 2000 , Alves et al, 2020 ).…”