[Value of a nomogram model based on IDEAL-IQ for predicting early bone mass loss]

Area of Science:

• Radiology
• Biomedical Imaging
• Osteoporosis Research

Background:

• Early detection of bone loss is crucial for preventing fractures.
• Dual-energy X-ray absorptiometry (DXA) is the current standard for assessing bone mineral density (BMD).
• Novel imaging techniques are needed to improve early diagnosis and risk stratification.

Purpose of the Study:

• To evaluate the diagnostic performance of a nomogram model for predicting early lumbar vertebrae bone loss.
• To assess the utility of bone marrow fat fraction (FF) measured by IDEAL-IQ MRI in predicting bone loss.

Main Methods:

• Fifty-nine participants underwent DXA and lumbar MRI using IDEAL-IQ.
• Bone marrow FF was measured in L1-4 vertebrae.
• Subjects were categorized into normal bone mass and osteopenia groups based on DXA.
• A nomogram model was developed using FF, age, and BMI, and its diagnostic efficiency was assessed using ROC curve and decision curve analysis.

Main Results:

• Vertebral bone marrow FF was significantly lower in the normal bone mass group compared to the osteopenia group.
• The FF value showed an AUC of 0.797 for differentiating normal bone mass from osteopenia.
• The nomogram model incorporating FF, age, and BMI achieved an AUC of 0.954, demonstrating superior predictive power compared to FF alone.

Conclusions:

• MRI-based IDEAL-IQ can accurately measure vertebral bone marrow fat content.
• The developed nomogram model effectively predicts early bone mass loss.
• This visual nomogram offers a promising tool for early osteoporosis detection and management.