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Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram

Authors :
Yang Jiang
Wei Zhang
Shihao Huang
Qing Huang
Haoyi Ye
Yurong Zeng
Xin Hua
Jinhui Cai
Zhifeng Liu
Qingyu Liu
Source :
Diagnostics, Vol 13, Iss 22, p 3459 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

Details

Language :
English
ISSN :
20754418
Volume :
13
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.13096ed575bd490b93914664d03f833b
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics13223459