Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Department of Neurosurgery, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Neurol Sci. 2022 Jul;43(7):4363-4372. doi: 10.1007/s10072-022-05954-8. Epub 2022 Feb 24.
To develop and validate a radiomic prediction model using initial noncontrast computed tomography (CT) at admission to predict in-hospital mortality in patients with traumatic brain injury (TBI).
A total of 379 TBI patients from three cohorts were categorized into training, internal validation, and external validation sets. After filtering the unstable features with the minimum redundancy maximum relevance approach, the CT-based radiomics signature was selected by using the least absolute shrinkage and selection operator (LASSO) approach. A personalized predictive nomogram incorporating the radiomic signature and clinical features was developed using a multivariate logistic model to predict in-hospital mortality in patients with TBI. The calibration, discrimination, and clinical usefulness of the radiomics signature and nomogram were evaluated.
The radiomic signature consisting of 12 features had areas under the curve (AUCs) of 0.734, 0.716, and 0.706 in the prediction of in-hospital mortality in the internal and two external validation cohorts. The personalized predictive nomogram integrating the radiomic and clinical features demonstrated significant calibration and discrimination with AUCs of 0.843, 0.811, and 0.834 in the internal and two external validation cohorts. Based on decision curve analysis (DCA), both the radiomic features and nomogram were found to be clinically significant and useful.
This predictive nomogram incorporating the CT-based radiomic signature and clinical features had maximum accuracy and played an optimized role in the early prediction of in-hospital mortality. The results of this study provide vital insights for the early warning of death in TBI patients.
利用入院时的初始非对比计算机断层扫描（CT）开发和验证一种放射组学预测模型，以预测创伤性脑损伤（TBI）患者的院内死亡率。
将来自三个队列的 379 名 TBI 患者分为训练集、内部验证集和外部验证集。使用最小冗余最大相关性方法过滤不稳定特征后，使用最小绝对值收缩和选择算子（LASSO）方法选择基于 CT 的放射组学特征。使用多变量逻辑模型结合放射组学特征和临床特征开发个性化预测列线图，以预测 TBI 患者的院内死亡率。评估放射组学特征和列线图的校准、判别和临床实用性。
由 12 个特征组成的放射组学特征在内部和两个外部验证队列中预测院内死亡率的曲线下面积（AUC）分别为 0.734、0.716 和 0.706。整合放射组学和临床特征的个性化预测列线图在内部和两个外部验证队列中具有显著的校准和判别能力，AUC 分别为 0.843、0.811 和 0.834。基于决策曲线分析（DCA），放射组学特征和列线图均具有临床意义和实用性。
该列线图结合基于 CT 的放射组学特征和临床特征，具有最高的准确性，并在早期预测院内死亡率方面发挥了优化作用。本研究结果为 TBI 患者死亡的早期预警提供了重要的见解。
