Evidence map›Paper›PMID 41307715›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Developing and validating a combined model with CT-based paraspinal muscle radiomics and clinical features to predict residual low back pain after percutaneous kyphoplasty.

Ji-Le Jiang, Ruixuan Yu, Yanbin Zhang, Tenghui Ge, Bin Xiao, Da He, Xiaohui Tao

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ji-Le Jiang *Department of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Ruixuan Yu *Department of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Yanbin ZhangDepartment of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Tenghui GeDepartment of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Bin XiaoDepartment of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Da HeDepartment of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China.
Xiaohui TaoDepartment of Spine Surgery, Beijing Jishuitan Hospital, Capital Medical University, National Centre for Orthopaedics, Beijing, China. m13325106814@163.com.

Funding

Capital's Funds for Health Improvement and Research CFH-Brain 2024-1-2041
6 · The paper itself

Abstract

purposeTo develop and validate a CT-based radiomics model for predicting long-term residual low back pain (RLBP) in patients with osteoporotic lumbar vertebral compression fractures (LVCFs) following percutaneous kyphoplasty (PKP).

methodsThis study enrolled 385 patients with LVCFs who underwent PKP. Patients were randomly allocated to a training cohort (n = 269) and a testing cohort (n = 116). Radiomic features were extracted from preoperative CT scans of the paraspinal muscles. Candidate predictors, including demographics (age, gender, BMI), clinical factors (Oswestry Disability Index [ODI], smoking status, fracture history), and radiological measures (mean CT value, muscle area, fat infiltration fraction [FIF]), were analyzed. Three models were developed: a clinical model derived from multivariable logistic regression, a radiomics model constructed from the Rad-score, and a combined model incorporating both. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).

resultsFour independent clinical predictors were identified: ODI score, mean CT value, FIF, and multi-level fractures. The combined model showed the best performance, with an AUC of 0.905 in the training cohort and 0.891 in the testing cohort. In the testing cohort, the AUC of the combined model was significantly higher than that of the clinical model (0.891 vs. 0.787, P = 0.044) and showed a trend toward superiority over the radiomics model (0.891 vs. 0.828, P = 0.066). Calibration curves and DCA confirmed the combined model's good calibration and clinical utility. A nomogram was created to facilitate clinical use.

conclusionA combined model incorporating clinical risk factors and paraspinal muscle radiomics features can accurately and robustly predict the risk of long-term RLBP after PKP. The model shows promise as a noninvasive preoperative tool to aid in individualized risk stratification and clinical decision-making.

Indexed as

Fractures, CompressionKyphoplastyLow Back PainLumbar VertebraeOsteoporotic FracturesParaspinal MusclesSpinal FracturesTomography, X-Ray ComputedAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiomicsLumbar vertebral compression fractureParaspinal musclesPercutaneous kyphoplastyPredictive modelRadiomicsResidual low back pain

Identifiers

PMID41307715

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.