Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea.
Department of Trauma Surgery, Gachon University Gil Medical Center, Gachon University, Incheon, Republic of Korea.
PLoS One. 2024 May 30;19(5):e0304350. doi: 10.1371/journal.pone.0304350. eCollection 2024.
Depending on the degree of fracture, pelvic fracture can be accompanied by vascular damage, and in severe cases, it may progress to hemorrhagic shock. Pelvic radiography can quickly diagnose pelvic fractures, and the Association for Osteosynthesis Foundation and Orthopedic Trauma Association (AO/OTA) classification system is useful for evaluating pelvic fracture instability. This study aimed to develop a radiomics-based machine-learning algorithm to quickly diagnose fractures on pelvic X-ray and classify their instability. data used were pelvic anteroposterior radiographs of 990 adults over 18 years of age diagnosed with pelvic fractures, and 200 normal subjects. A total of 93 features were extracted based on radiomics:18 first-order, 24 GLCM, 16 GLRLM, 16 GLSZM, 5 NGTDM, and 14 GLDM features. To improve the performance of machine learning, the feature selection methods RFE, SFS, LASSO, and Ridge were used, and the machine learning models used LR, SVM, RF, XGB, MLP, KNN, and LGBM. Performance measurement was evaluated by area under the curve (AUC) by analyzing the receiver operating characteristic curve. The machine learning model was trained based on the selected features using four feature-selection methods. When the RFE feature selection method was used, the average AUC was higher than that of the other methods. Among them, the combination with the machine learning model SVM showed the best performance, with an average AUC of 0.75±0.06. By obtaining a feature-importance graph for the combination of RFE and SVM, it is possible to identify features with high importance. The AO/OTA classification of normal pelvic rings and pelvic fractures on pelvic AP radiographs using a radiomics-based machine learning model showed the highest AUC when using the SVM classification combination. Further research on the radiomic features of each part of the pelvic bone constituting the pelvic ring is needed.
根据骨折程度的不同，骨盆骨折可伴有血管损伤，严重者可进展为出血性休克。骨盆 X 线摄影可快速诊断骨盆骨折，而骨折协会和骨科创伤协会（AO/OTA）分类系统有助于评估骨盆骨折的不稳定性。本研究旨在开发一种基于放射组学的机器学习算法，以便快速诊断骨盆 X 线片上的骨折并对其不稳定性进行分类。数据来自 990 名 18 岁以上骨盆骨折患者和 200 名正常受试者的骨盆前后位 X 线片。共提取了 93 个基于放射组学的特征：18 个一阶特征、24 个 GLCM 特征、16 个 GLRLM 特征、16 个 GLSZM 特征、5 个 NGTDM 特征和 14 个 GLDM 特征。为了提高机器学习的性能，使用 RFE、SFS、LASSO 和 Ridge 等特征选择方法，以及 LR、SVM、RF、XGB、MLP、KNN 和 LGBM 等机器学习模型。通过分析受试者工作特征曲线来评估性能，以曲线下面积（AUC）作为评估指标。基于所选特征，使用四种特征选择方法训练机器学习模型。当使用 RFE 特征选择方法时，平均 AUC 高于其他方法。其中，与 SVM 机器学习模型结合的效果最佳，平均 AUC 为 0.75±0.06。通过获得 RFE 和 SVM 结合的特征重要性图，可以识别出具有高重要性的特征。使用基于放射组学的机器学习模型对骨盆前后位 X 线片上的正常骨盆环和骨盆骨折进行 AO/OTA 分类，当使用 SVM 分类组合时，AUC 最高。需要进一步研究构成骨盆环的骨盆骨各部分的放射组学特征。
