Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions

Autor: Andrea de Barros, Alex Rovira, Annalaura Salerno, Roberto Cannella, Cristina Auger, G. Caruana, Giuseppe Salvaggio, Lucas M. Pessini
Přispěvatelé: Caruana G., Pessini L.M., Cannella R., Salvaggio G., de Barros A., Salerno A., Auger C., Rovira À.
Rok vydání: 2020
Předmět:
Adult
Male
medicine.medical_specialty
Adolescent
Contrast Media
030218 nuclear medicine & medical imaging
Lesion
03 medical and health sciences
Young Adult
0302 clinical medicine
Multiple Sclerosis
Relapsing-Remitting
Image Processing
Computer-Assisted
Medicine
Humans
Multiple sclerosi
Radiology
Nuclear Medicine and imaging
Diagnosis
Computer-Assisted
Least-Squares Analysis
Neuroradiology
Aged
Retrospective Studies
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Multiple sclerosis
Ultrasound
Reproducibility of Results
Magnetic resonance imaging
General Medicine
Middle Aged
medicine.disease
Magnetic Resonance Imaging
Regression
Logistic models
Contrast agent
ROC Curve
030220 oncology & carcinogenesis
Area Under Curve
Susceptibility weighted imaging
Acute Disease
Chronic Disease
Regression Analysis
Female
Radiology
medicine.symptom
Settore MED/36 - Diagnostica Per Immagini E Radioterapia
business
Zdroj: European radiology. 30(11)
ISSN: 1432-1084
Popis: To evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions. We analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions. Multiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617–0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models. Texture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies. • Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions • The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies
Databáze: OpenAIRE
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