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Study design <p>Retrospective cohort study.</p> Purpose <p>To develop and validate the Local Fragility Index (LFI), a novel CT-derived measure that integrates the lowest vertebral Hounsfield Unit (HU) (Min HU), inter-level variability (SD), and cranio-caudal slope to quantify vertebral heterogeneity and improve osteoporosis risk assessment.</p> Methods <p>We retrospectively analyzed 172 patients who underwent spinal surgery. Severe adult spinal deformity (Cobb angle &gt; 30°) was excluded to ensure measurement consistency. HU values from L1–L4 were obtained on preoperative CT. Min HU was inverted (− Min HU) to emphasize fragility, SD was calculated across levels, and the cranio-caudal slope of HU values was estimated by linear regression across L1–L4. Each parameter was standardized using Z-scores, and LFI was defined as z(− Min HU) + z(SD) + z(− Slope). The composite score was then further standardized to generate a normalized index (LFI_z), which was used for all statistical analyses. Osteoporosis was defined as the lowest DXA T-score ≤ − 2.5. Diagnostic performance was assessed by ROC analysis, logistic regression, decision curve analysis (DCA), and category-free net reclassification improvement (cfNRI).</p> Results <p>Min HU was significantly lower in osteoporotic patients (<i>p</i> &lt; 0.001) and correlated with the lowest DXA T-score (<i>r</i> = 0.52). LFI_z showed comparable discrimination to Mean HU (AUC 0.664 vs. 0.690), while their combination achieved higher accuracy (AUC 0.701). In adjusted models, LFI_z remained an independent predictor of osteoporosis (OR 1.9 per SD, <i>p</i> &lt; 0.01), with improved reclassification (cfNRI + 0.19, 95% CI 0.05–0.32). The Radiomics-lite + Clinical model (HU quantiles, variability metrics, and demographics) provided the best performance (AUC 0.824) and the greatest net clinical benefit across 10–40% thresholds.</p> Conclusions <p>The LFI captures vertebral heterogeneity and complements mean HU, thereby enhancing osteoporosis risk stratification. Building on this framework, a simplified Radiomics-lite + Clinical model further improved discriminative performance and decision-analytic utility, offering a scalable approach for CT-based assessment in patients undergoing spine surgery. This approach may directly inform surgical planning (e.g., screw augmentation, extended fusion levels) and perioperative management of osteoporosis. As this outcome-agnostic, hypothesis-generating analysis was benchmarked to DXA rather than clinical endpoints, prospective validation against fractures and instrumentation-related complications is needed before clinical use.</p>

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Local fragility index and Radiomics-lite modeling of CT-Derived Hounsfield units for osteoporosis risk assessment in spine surgery patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Akihiko Hiyama,
  • Daisuke Sakai,
  • Hiroyuki Katoh,
  • Masato Sato,
  • Masahiko Watanabe

摘要

Study design

Retrospective cohort study.

Purpose

To develop and validate the Local Fragility Index (LFI), a novel CT-derived measure that integrates the lowest vertebral Hounsfield Unit (HU) (Min HU), inter-level variability (SD), and cranio-caudal slope to quantify vertebral heterogeneity and improve osteoporosis risk assessment.

Methods

We retrospectively analyzed 172 patients who underwent spinal surgery. Severe adult spinal deformity (Cobb angle > 30°) was excluded to ensure measurement consistency. HU values from L1–L4 were obtained on preoperative CT. Min HU was inverted (− Min HU) to emphasize fragility, SD was calculated across levels, and the cranio-caudal slope of HU values was estimated by linear regression across L1–L4. Each parameter was standardized using Z-scores, and LFI was defined as z(− Min HU) + z(SD) + z(− Slope). The composite score was then further standardized to generate a normalized index (LFI_z), which was used for all statistical analyses. Osteoporosis was defined as the lowest DXA T-score ≤ − 2.5. Diagnostic performance was assessed by ROC analysis, logistic regression, decision curve analysis (DCA), and category-free net reclassification improvement (cfNRI).

Results

Min HU was significantly lower in osteoporotic patients (p < 0.001) and correlated with the lowest DXA T-score (r = 0.52). LFI_z showed comparable discrimination to Mean HU (AUC 0.664 vs. 0.690), while their combination achieved higher accuracy (AUC 0.701). In adjusted models, LFI_z remained an independent predictor of osteoporosis (OR 1.9 per SD, p < 0.01), with improved reclassification (cfNRI + 0.19, 95% CI 0.05–0.32). The Radiomics-lite + Clinical model (HU quantiles, variability metrics, and demographics) provided the best performance (AUC 0.824) and the greatest net clinical benefit across 10–40% thresholds.

Conclusions

The LFI captures vertebral heterogeneity and complements mean HU, thereby enhancing osteoporosis risk stratification. Building on this framework, a simplified Radiomics-lite + Clinical model further improved discriminative performance and decision-analytic utility, offering a scalable approach for CT-based assessment in patients undergoing spine surgery. This approach may directly inform surgical planning (e.g., screw augmentation, extended fusion levels) and perioperative management of osteoporosis. As this outcome-agnostic, hypothesis-generating analysis was benchmarked to DXA rather than clinical endpoints, prospective validation against fractures and instrumentation-related complications is needed before clinical use.