BEAUT: a radiomic approach to identify potential lumbar fractures in magnetic resonance imaging

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Ramos, Jonathan S. ; Maciel, Jamilly G. ; Cazzolato, Mirela T. ; Traina Jr, Caetano ; Nogueira-Barbosa, Marcello H. ; Traina, Agma J. M. ; Almeida, JR ; Gonzalez, AR ; Shen, L ; Kane, B ; Traina, A ; Soda, P ; Oliveira, JL

• Document type: Journal article
• Source: 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2021-01-01.

Abstract

Bone densitometry (DEXA) is the international reference standard to evaluate Bone Mineral Density (BMD) and diagnose osteoporosis. However, DEXA is far from ideal when used to predict fragility fractures, which are strongly related to morbidity and mortality. According to the literature, spine MRI texture features correlate well with DEXA measurements. For this reason, we conducted an extensive empirical study aimed at assessing fragility fractures secondary to osteoporosis. To perform the evaluations, we developed a radiomic-based approach called BEAUT (BonE Analysis Using Texture). We performed experiments on a meaningful database composed of 47 T2-weighted sagittal sequences from lumbar spine MRI. The patients were diagnosed with osteopenia or osteoporosis according to DEXA (patients with low bone mass). BEAUT achieved an accuracy of 92% and 97% AUC with feature selection to discriminate between patients from groups 'Fractures' and 'No Fractures'. The results support claiming that texture features potentially discriminate subjects with bone mass loss, spotting those at risk of fragility fractures. (AU)

• FAPESP's process: 20/11258-2 - Interoperability and similarity queries on medical databases
• Grantee: Mirela Teixeira Cazzolato
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 17/23780-2 - Content-based retrieval of medical images to aid the clinical decision using radiomics
• Grantee: Jonathan da Silva Ramos
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/04266-9 - Multiparametric analysis of lumbar vertebrae texture on magnetic resonance imaging and correlation with fragility fractures.
• Grantee: Marcello Henrique Nogueira Barbosa
• Support Opportunities: Regular Research Grants