An Anatomically Guided and Optimization-Refined Radiomics Framework for Opportunistic Osteoporosis Assessment from Lumbar Spine MRI

Information from the abstract

Background/Objectives: Osteoporosis is a major contributor to vertebral compression fractures (VCFs) and other skeletal complications, yet quantitative bone mineral density (BMD) assessment using dual-energy X-ray absorptiometry (DEXA) is not routinely available in many spine surgery workflows. This study proposes an anatomically guided and optimization-refined radiomics framework for opportunistic osteoporosis assessment from routine lumbar spine magnetic resonance imaging (MRI). Methods: The proposed pipeline employs a hierarchical template-matching strategy to automatically localize the L1–L4 vertebral region, followed by an optimization-based refinement procedure that adapts vertebral regions of interest (ROIs) using intensity, texture, boundary, and geometric constraints. Anatomically consistent ROIs are subsequently used for extraction of handcrafted radiomic descriptors, including statistical, textural, gradient-based, frequency-domain, and shape-related features. The extracted features were evaluated using conventional support vector classification (SVC) and a NeurodynamicSVMRBFTanh classification framework for osteoporosis-related classification. Results: Experimental results demonstrated robust and anatomically consistent vertebral localization across heterogeneous lumbar MRI acquisitions. The NeurodynamicSVMRBFTanh framework achieved the best screening-oriented performance, yielding 85.2% classification accuracy and 100.0% sensitivity on an independent test set. In addition, exploratory BMD regression analysis demonstrated the feasibility of estimating DEXA-derived BMD directly from MRI-derived radiomic features, achieving mean absolute percentage errors of approximately 15–20% across lumbar vertebral levels. Conclusions: These findings suggest that anatomically guided vertebral radiomics extracted from routine lumbar spine MRI contain clinically meaningful information associated with osteoporosis-related bone quality changes and may provide a practical tool for automated opportunistic osteoporosis assessment in settings where DEXA measurements are unavailable.

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Related topics: Radiomics and Machine Learning in Medical Imaging · Medical Imaging and Analysis · Bone health and osteoporosis research