The Affiliated Hospital of Kunming University of Science and Technology, Department of Orthopaedics, The First People's Hospital of Yunnan Province, 157 Jinbi Road, Kunming, Yunnan Province, China.
Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, 104 Guanghua Street, Kunming, Yunnan Province, China.
Spine J. 2024 Sep;24(9):1625-1634. doi: 10.1016/j.spinee.2024.04.016. Epub 2024 Apr 26.
Osteoporosis, a metabolic bone disorder, markedly elevates fracture risks, with vertebral compression fractures being predominant. Antiosteoporotic treatments for patients with osteoporotic vertebral compression fractures (OVCF) lessen both the occurrence of subsequent fractures and associated pain. Thus, diagnosing osteoporosis in OVCF patients is vital.
The aim of this study was to develop a predictive radiographic model using T1 sequence MRI images to accurately determine whether patients with lumbar spine compression fractures also have osteoporosis.
Retrospective cohort study.
Patients over 45 years of age diagnosed with a fresh lumbar compression fracture.
Diagnostic accuracy of the model (area under the ROC curve).
The study retrospectively collected clinical and imaging data (MRI and DEXA) from hospitalized lumbar compression fracture patients (L1-L4) aged 45 years or older between January 2021 and June 2023. Using the pyradiomics package in Python, features from the lumbar compression fracture vertebral region of interest (ROI) were extracted. Downscaling of the extracted features was performed using the Mann-Whitney U test and the least absolute shrinkage selection operator (LASSO) algorithm. Subsequently, six machine learning models (Naive Bayes, Support Vector Machine [SVM], Decision Tree, Random Forest, Extreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LightGBM]) were employed to train and validate these features in predicting osteoporosis comorbidity in OVCF patients.
A total of 128 participants, 79 in the osteoporotic group and 49 in the nonosteoporotic group, met the study's inclusion and exclusion criteria. From the T1 sequence MRI images, 1906 imaging features were extracted in both groups. Utilizing the Mann-Whitney U test, 365 radiologic features were selected out of the initial 1,906. Ultimately, the lasso algorithm identified 14 significant radiological features. These features, incorporated into six conventional machine learning algorithms, demonstrated successful prediction of osteoporosis in the validation set. The NaiveBayes model yielded an area under the receiver operating characteristic curve (AUC) of 0.84, sensitivity of 0.87, specificity of 0.70, and accuracy of 0.81.
A NaiveBayes machine learning algorithm can predict osteoporosis in OVCF patients using t1-sequence MRI images of lumbar compression fractures. This approach aims to obviate the necessity for further osteoporosis assessments, diminish patient exposure to radiation, and bolster the clinical care of patients with OVCF.
骨质疏松症是一种代谢性骨病，会显著增加骨折风险，其中以椎体压缩性骨折为主。对骨质疏松性椎体压缩性骨折（OVCF）患者进行抗骨质疏松治疗，可以降低后续骨折和相关疼痛的发生。因此，诊断 OVCF 患者的骨质疏松症至关重要。
本研究旨在通过 T1 序列 MRI 图像建立预测性放射学模型，准确判断腰椎压缩性骨折患者是否同时患有骨质疏松症。
回顾性队列研究。
45 岁以上因新鲜腰椎压缩性骨折而住院的患者。
模型的诊断准确性（ROC 曲线下面积）。
本研究回顾性收集了 2021 年 1 月至 2023 年 6 月期间因腰椎压缩性骨折（L1-L4）住院的 45 岁及以上患者的临床和影像学数据（MRI 和 DEXA）。使用 Python 中的 pyradiomics 包，从感兴趣的腰椎压缩性骨折区域（ROI）中提取特征。使用 Mann-Whitney U 检验和最小绝对收缩选择算子（LASSO）算法对提取的特征进行降维。随后，使用六种机器学习模型（朴素贝叶斯、支持向量机[SVM]、决策树、随机森林、极端梯度提升[XGBoost]和轻梯度提升机[LightGBM]）对这些特征进行训练和验证，以预测 OVCF 患者骨质疏松症的合并症。
共有 128 名患者符合研究的纳入和排除标准，其中 79 名患者为骨质疏松组，49 名患者为非骨质疏松组。从 T1 序列 MRI 图像中，两组共提取了 1906 个影像学特征。使用 Mann-Whitney U 检验，从最初的 1906 个特征中选择了 365 个放射学特征。最终，lasso 算法确定了 14 个有意义的放射学特征。将这些特征纳入六种常规机器学习算法中，在验证集中成功预测了骨质疏松症。朴素贝叶斯模型的受试者工作特征曲线（ROC）下面积（AUC）为 0.84，灵敏度为 0.87，特异性为 0.70，准确性为 0.81。
使用 T1 序列 MRI 图像可以通过朴素贝叶斯机器学习算法预测 OVCF 患者的骨质疏松症。这种方法旨在避免进一步的骨质疏松评估，减少患者的辐射暴露，并加强 OVCF 患者的临床护理。
