Jin Liang, Sun Yingli, Ma Zongjing, Li Ming
Shanghai Key Lab of Forensic Medicine, Key Lab of Forensic Science, Ministry of Justice, Academy of Forensic Science, Shanghai 200063, China.
Radiology Department, Huadong Hospital, Affiliated with Fudan University, Shanghai 200040, China.
Bioengineering (Basel). 2022 Dec 21;10(1):8. doi: 10.3390/bioengineering10010008.
This retrospective study aimed to predict the injury time of rib fractures in distinguishing fresh (30 days) or old (90 days) rib fractures. We enrolled 111 patients with chest trauma who had been scanned for rib fractures at our hospital between January 2018 and December 2018 using gemstone spectral imaging (GSI). The volume of interest of each broken end of the rib fractures was segmented using calcium-based material decomposition images derived from the GSI scans. The training and testing sets were randomly assigned in a 7:3 ratio. All cases were divided into groups distinguishing the injury time at 30 and 90 days. We constructed radiomics-based models to predict the injury time of rib fractures. The model performance was assessed by the area under the curve (AUC) obtained by the receiver operating characteristic analysis. We included 54 patients with 259 rib fracture segmentations (34 men; mean age, 52 years ± 12.02; and range, 19-72 years). Nine features were excluded by the least absolute shrinkage and selection operator logistic regression to build the radiomics signature. For distinguishing the injury time at 30 days, the Support Vector Machine (SVM) model and human-model collaboration resulted in an accuracy and AUC of 0.85 and 0.871 and 0.91 and 0.912, respectively, and 0.81 and 0.804 and 0.83 and 0.85, respectively, at 90 days in the testing set. The radiomics-based model displayed good accuracy in differentiating between the injury time of rib fractures at 30 and 90 days, and the human-model collaboration generated more accurate outcomes, which may help to add value to clinical practice and distinguish artificial injury in forensic medicine.
这项回顾性研究旨在通过区分新鲜（30天内）或陈旧（90天以上）肋骨骨折来预测肋骨骨折的损伤时间。我们纳入了111例胸部创伤患者，这些患者于2018年1月至2018年12月期间在我院接受了宝石能谱成像（GSI）扫描以检查肋骨骨折。利用GSI扫描得出的基于钙的物质分解图像对肋骨骨折各断端的感兴趣体积进行分割。训练集和测试集按7:3的比例随机分配。所有病例根据30天和90天的损伤时间分为不同组。我们构建了基于放射组学的模型来预测肋骨骨折的损伤时间。通过接收器操作特征分析获得的曲线下面积（AUC）评估模型性能。我们纳入了54例患者，共259处肋骨骨折分割（34名男性；平均年龄52岁±12.02；年龄范围19 - 72岁）。通过最小绝对收缩和选择算子逻辑回归排除了9个特征以构建放射组学特征。对于区分30天的损伤时间，在测试集中，支持向量机（SVM）模型与人工判断协作的准确率和AUC分别为0.85和0.871以及0.91和0.912，对于90天的损伤时间，准确率和AUC分别为0.81和0.804以及0.83和0.85。基于放射组学的模型在区分30天和90天肋骨骨折的损伤时间方面显示出良好的准确性，并且人工与模型协作产生了更准确的结果，这可能有助于为临床实践增加价值，并在法医学中区分人为损伤。
