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Background <p>Pyogenic spondylitis (PS) and tuberculous spondylitis (TS) present with significant clinical overlap, posing a major diagnostic challenge. We aimed to develop and validate an imaging-based nomogram integrating CT and MRI features to accurately differentiate PS from TS.</p> Method <p>We conducted a multicenter retrospective study including 539 patients with spinal infections (251 PS, 288 TS) diagnosed between June 2021 and May 2025. Patients were divided into training (n = 427) and external validation (n = 112) cohorts. Imaging features were screened using univariate logistic regression. The least absolute shrinkage and selection operator (LASSO) regression was then applied to select the optimal predictive feature subset and mitigate overfitting. A multivariate logistic regression model based on these features constructed the nomogram. We evaluated diagnostic performance using the area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Internal validation employed 500 bootstrap resamples; external validation used an independent cohort.</p> Results <p>The training cohort comprised 187 (43.8%) PS and 240 (56.2%) TS patients; the external validation cohort had 64 (57.1%) PS and 48 (42.9%) TS patients. LASSO regression identified five key predictors: vertebral involvement pattern (continuous vs. skip/non-continuous), vertebral body T2-weighted signal intensity (hyperintense vs. heterogeneous), MRI abscess wall characteristics (thick/irregular vs. thin/smooth), CT bone destruction type (osteolytic vs. fragmentary), and CT sagittal bone destruction degree (&lt; 1/3 vs. &gt; 2/3). The AUCs of the nomograms for the training and external validation cohorts were 0.908 (95% confidence interval: 0.880—0.936) and 0.899 (95% confidence interval: 0.842—0.955), respectively. Calibration curves showed the optimal concordance between predicted results and the actual observations. DCA indicated that the substantial clinical net benefit across threshold probabilities.</p> Conclusion <p>The developed nomogram is capable of accurately distinguishing between PS and TS, thereby aiding clinicians in making informed decisions promptly upon obtaining relevant data.</p>

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Development and validation of a nomogram for differential diagnosis of pyogenic spondylitis and tuberculous spondylitis in China: a multicenter retrospective study

  • 期刊论文

  • 发表日期：

    2026年5月5日

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  • Liang Xu,
  • Enuo Dai,
  • Lulu Shi,
  • Yongrui Yang,
  • Wenkai Ruan,
  • Jianlong Li,
  • Rongpan Dang,
  • Huigang An,
  • Wentao Zhao,
  • Yingxin Zhao,
  • Zhaofei Li,
  • Chenggui Zhang,
  • Chenguang Jia,
  • Zhongji Wang,
  • Qile Gao,
  • Ningkui Niu,
  • Shangsheng Xu,
  • Rui Bao,
  • Zhigang Huang,
  • Zhaopeng Li,
  • Xiaogang Guan,
  • Shengping Hu,
  • Hongdong Tan

摘要

Background

Pyogenic spondylitis (PS) and tuberculous spondylitis (TS) present with significant clinical overlap, posing a major diagnostic challenge. We aimed to develop and validate an imaging-based nomogram integrating CT and MRI features to accurately differentiate PS from TS.

Method

We conducted a multicenter retrospective study including 539 patients with spinal infections (251 PS, 288 TS) diagnosed between June 2021 and May 2025. Patients were divided into training (n = 427) and external validation (n = 112) cohorts. Imaging features were screened using univariate logistic regression. The least absolute shrinkage and selection operator (LASSO) regression was then applied to select the optimal predictive feature subset and mitigate overfitting. A multivariate logistic regression model based on these features constructed the nomogram. We evaluated diagnostic performance using the area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Internal validation employed 500 bootstrap resamples; external validation used an independent cohort.

Results

The training cohort comprised 187 (43.8%) PS and 240 (56.2%) TS patients; the external validation cohort had 64 (57.1%) PS and 48 (42.9%) TS patients. LASSO regression identified five key predictors: vertebral involvement pattern (continuous vs. skip/non-continuous), vertebral body T2-weighted signal intensity (hyperintense vs. heterogeneous), MRI abscess wall characteristics (thick/irregular vs. thin/smooth), CT bone destruction type (osteolytic vs. fragmentary), and CT sagittal bone destruction degree (< 1/3 vs. > 2/3). The AUCs of the nomograms for the training and external validation cohorts were 0.908 (95% confidence interval: 0.880—0.936) and 0.899 (95% confidence interval: 0.842—0.955), respectively. Calibration curves showed the optimal concordance between predicted results and the actual observations. DCA indicated that the substantial clinical net benefit across threshold probabilities.

Conclusion

The developed nomogram is capable of accurately distinguishing between PS and TS, thereby aiding clinicians in making informed decisions promptly upon obtaining relevant data.