ArticleThe journal of venomous animals and toxins including tropical diseases2020
Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?
Article in The journal of venomous animals and toxins including tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 30 citations in OpenAlex.
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- Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.Quantitative imaging in medicine and surgery · 2022Article
- Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation.Journal of clinical medicine · 2022Article
- Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective.Frontiers in oncology · 2022Review
- Educational case: Brain abscess.Academic pathology · 2022Article
- A Survey of Brain Tumor Segmentation and Classification Algorithms.Journal of imaging · 2021Article
- Recognition of brain tumors in MRI images using texture analysis.Saudi journal of biological sciences · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundNeuroimaging 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.
methodsIn 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.
resultsThe 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).
conclusionThe 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.
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