Inflammatory lesions and brain tumors: is it possible to differentiate them based on texture features in magnetic resonance imaging?
| Autor: | Allan Felipe Fattori Alves, José Thiago de Souza de Castro, José Ricardo de Arruda Miranda, Laisson de Moura Feitoza, Fabiano Reis, Luciana Luchesi Rodrigues Alves, Sergio Augusto Santana de Souza, Diana Rodrigues de Pina |
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| Přispěvatelé: | Universidade Estadual Paulista (Unesp), Universidade Estadual de Campinas (UNICAMP) |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty 030231 tropical medicine Central nervous system RC955-962 Toxicology 03 medical and health sciences 0302 clinical medicine Magnetic resonance imaging Neuroimaging Image processing Arctic medicine. Tropical medicine RA1190-1270 medicine Medical imaging Enhancing Lesion Medical diagnosis Inflammation Tumor 030102 biochemistry & molecular biology medicine.diagnostic_test business.industry Research Contrast medium Infectious Diseases medicine.anatomical_structure QL1-991 Toxicology. Poisons Animal Science and Zoology Parasitology Histopathology Radiology business Zoology |
| Zdroj: | The Journal of Venomous Animals and Toxins Including Tropical Diseases Journal of Venomous Animals and Toxins including Tropical Diseases Journal of Venomous Animals and Toxins including Tropical Diseases, Volume: 26, Article number: e20200011, Published: 04 SEP 2020 Journal of Venomous Animals and Toxins including Tropical Diseases v.26 2020 The Journal of venomous animals and toxins including tropical diseases Universidade Estadual Paulista (UNESP) instacron:UNESP Scopus Repositório Institucional da UNESP |
| ISSN: | 1678-9199 |
| Popis: | Made available in DSpace on 2021-06-25T10:35:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01. Added 1 bitstream(s) on 2021-07-15T15:22:47Z : No. of bitstreams: 1 S1678-91992020000100328.pdf: 2012220 bytes, checksum: 16ebe6eacc222cb008a8d097b95a7aa1 (MD5) American Federation for Aging Research 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. Department of Physics and Biophysics Botucatu Biosciences Institute São Paulo State University (UNESP) Department of Radiology School of Medical Sciences University of Campinas (Unicamp) Department of Tropical Disease and Imaging Diagnosis Botucatu Medical School São Paulo State University (UNESP) Department of Physics and Biophysics Botucatu Biosciences Institute São Paulo State University (UNESP) Department of Tropical Disease and Imaging Diagnosis Botucatu Medical School São Paulo State University (UNESP) |
| Databáze: | OpenAIRE |
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