Dai Jialin, Liu Dan, Li Xia, Liu Yuyao, Wang Fang, Yang Quan
Jialin Dai, Department of Radiology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, P.R. China.
Dan Liu, Department of Radiology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402160, P.R. China.
Pak J Med Sci. 2023 Jul-Aug;39(4):1149-1155. doi: 10.12669/pjms.39.4.7724.
To develop and validate a radiomics-based nomogram model which aimed to predict hematoma expansion (HE) in hypertensive intracerebral hemorrhage (HICH).
Patients with HICH (n=187) were included from October 2017 to March 2022 in the Yongchuan Affiliated Hospital of Chongqing Medical University. Patients were randomly divided into a training set (n=130) and a validation set (n=57) in a ratio of 7:3. The radiomic features were extracted from the regions of interest (including main hematoma, the surrounding small hematoma(s) and perihematomal edema) in the first CT scan images. The variance threshold, SelectKBest and LASSO (least absolute shrinkage and selection operator), features were selected and the radiomics signature was built. Multivariate logistic regression was used to establish a nomogram based on clinical risk factors and the Rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the generalization of the models' performance. The calibration curve and the Hosmer-Lemeshow test were used to assess the calibration of the predictive nomogram. And decision curve analysis (DCA) was used to evaluate the prediction model.
Thirteen radiomics features were selected to construct the radiomics signature, which has a robust association with HE. The radiomics model found that blend sign was a predictive factor of HE. The radiomics model ROC in the training set was 0.89 (95%CI 0.82-0.96) and was 0.82 (95%CI 0.60-0.93) in the validation set. The nomogram model was built using the combined prediction model based on radiomics and blend sign, and worked well in both the training set (ROC: 0.90[95%CI 0.83-0.96]) and the validation set (ROC: 0.88[95%CI 0.71-0.93]).
The radiomic signature based on CT of HICH has high accuracy for predicting HE. The combined prediction model of radiomics and blend sign improves the prediction performance.
开发并验证一种基于放射组学的列线图模型，旨在预测高血压性脑出血（HICH）中的血肿扩大（HE）。
2017年10月至2022年3月期间，重庆医科大学附属永川医院纳入了187例HICH患者。患者按7:3的比例随机分为训练集（n = 130）和验证集（n = 57）。从首次CT扫描图像中的感兴趣区域（包括主要血肿、周围小血肿和血肿周围水肿）提取放射组学特征。选择方差阈值、SelectKBest和LASSO（最小绝对收缩和选择算子）特征并构建放射组学特征。使用多变量逻辑回归基于临床危险因素和Rad评分建立列线图。采用受试者操作特征（ROC）曲线评估模型性能的泛化性。校准曲线和Hosmer-Lemeshow检验用于评估预测列线图的校准情况。并采用决策曲线分析（DCA）评估预测模型。
选择了13个放射组学特征来构建放射组学特征，其与HE具有强相关性。放射组学模型发现混合征是HE的预测因素。训练集中放射组学模型的ROC为0.89（95%CI 0.82 - 0.96），验证集中为0.82（95%CI 0.60 - 0.93）。使用基于放射组学和混合征的联合预测模型构建列线图模型，该模型在训练集（ROC：0.90[95%CI 0.83 - 0.96]）和验证集（ROC：0.88[95%CI 0.71 - 0.93]）中均表现良好。
基于HICH的CT的放射组学特征对预测HE具有较高的准确性。放射组学和混合征的联合预测模型提高了预测性能。
