Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Department of Radiology, Huizhou Central People's Hospital, Huizhou, China.
BMC Musculoskelet Disord. 2023 Jun 9;24(1):472. doi: 10.1186/s12891-023-06557-w.
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.
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.
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 < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model.
The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.
准确预测骨质疏松性椎体压缩性骨折（OVCFs）患者接受椎体增强（VA）后即将发生的新椎体骨折（NVFs）具有挑战性，目前尚无有效的方法。本研究旨在探讨一种基于放射组学特征和临床因素的机器学习模型，以预测椎体增强术后新的椎体骨折。
共纳入 235 例接受 VA 治疗的 OVCFs 患者，这些患者来自两个独立的机构，分为三组：训练集（n=138）、内部验证集（n=59）和外部验证集（n=38）。在训练集中，从 T1 加权 MRI 图像上的 L1 或相邻椎体（T12 或 L2）计算放射组学特征，并使用最小绝对值收缩和选择算子算法（LASSO）构建放射组学特征。使用随机生存森林（RSF）算法或 COX 比例风险（CPH）分析将预测性放射组学特征和临床因素拟合到两个最终预测模型中。使用独立的内部和外部验证集验证预测模型。
两个预测模型均整合了放射组学特征和椎体内裂隙（IVC）。与 CPH 模型相比，RSF 模型具有更高的 C 指数（0.763、0.773 和 0.731）和时间依赖性 AUC（2 年）（0.855、0.907 和 0.839，p<0.001 均），在训练集、内部验证集和外部验证集中更具预测性。与 CPH 模型相比，RSF 模型具有更好的校准度、更大的净效益（通过决策曲线分析确定）和更低的预测误差（时间依赖性 Brier 评分分别为 0.156、0.151 和 0.146）。
整合的 RSF 模型具有预测椎体增强术后即将发生的 NVFs 的潜力，这将有助于术后随访和治疗。
