Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation

Autor: Yang Jiang, Jinhui Cai, Yurong Zeng, Haoyi Ye, Tingqian Yang, Zhifeng Liu, Qingyu Liu
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: BMC Musculoskeletal Disorders, Vol 24, Iss 1, Pp 1-10 (2023)
Druh dokumentu: article
ISSN: 1471-2474
DOI: 10.1186/s12891-023-06557-w
Popis: Abstract Background Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. Methods A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. Results The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p
Databáze: Directory of Open Access Journals
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