Zhang Ru, Zhu Li, Zhu Zhengqi, Ge Yaqiong, Zhang Zhongxin, Wang Tianle
Department of Radiology, The Second Affiliated Hospital of Nantong University, Nantong, China.
GE Healthcare, Nanjing, China.
Ann Palliat Med. 2020 Sep;9(5):2684-2692. doi: 10.21037/apm-20-1142. Epub 2020 Jul 24.
Saving the ischemic penumbra (IP) is key in treating acute ischemic stroke (AIS). We aim to investigate the value of the apparent diffusion coefficient (ADC) map based radiomics model in the identification of IP in AIS.
This study retrospectively analyzed the data of 241 patients with AIS involving the anterior cerebral circulation who were treated in our hospital within 24 h of stroke onset from January 2014 to October 2019. With the perfusion-weighted imaging (PWI)/diffusion-weighted imaging (DWI) mismatch model as the gold standard to determine whether IP exists, we divided patients into PWI/DWI mismatch (84 cases) and non-PWI/DWI mismatch (157 cases). Following the DWI high signal area, the region of interest (ROI) was drawn to the maximum level of the lesions on the ADC map, and a total of 896 features were extracted. Maximum correlation and minimum redundancy (mRMR) algorithm were applied to select the optimized features subsets, and then the least absolute shrinkage and selection operator (LASSO) were furtherly applied to select the best features to construct radiomics signature in predicting PWI/DWI mismatch. The performance of the model was evaluated using a receiver operating characteristic (ROC) curve. One hundred times internal cross-validation was applied to evaluate the stability of the model. The clinical value of the model was evaluated using decision curve analysis (DCA).
Twenty-one features were finally selected to set up the radiomics model. In the training set, the area under the ROC curve (AUC) was 0.92, and the sensitivity, specificity, and accuracy were 0.93, 0.75, 0.82, respectively. In the validation set, the AUC was 0.90, and the sensitivity, specificity, and accuracy were 0.88, 0.74, 0.80, respectively. The average AUC of internal cross-validation for 100 times in the training set were 0.88 and 0.83 in the validation set. DCA shows that within the threshold range of 0.08 to 1.0, the model gains more net benefit.
The radiomics model based on the ADC map can effectively determine the presence of IP in patients with AIS.
挽救缺血半暗带（IP）是治疗急性缺血性脑卒中（AIS）的关键。我们旨在研究基于表观扩散系数（ADC）图的放射组学模型在识别AIS中IP的价值。
本研究回顾性分析了2014年1月至2019年10月在我院发病24小时内接受治疗的241例涉及前脑循环的AIS患者的数据。以灌注加权成像（PWI）/扩散加权成像（DWI）不匹配模型作为确定IP是否存在的金标准，将患者分为PWI/DWI不匹配组（84例）和非PWI/DWI不匹配组（157例）。在DWI高信号区域之后，在ADC图上病变的最大层面绘制感兴趣区（ROI），共提取896个特征。应用最大相关最小冗余（mRMR）算法选择优化特征子集，然后进一步应用最小绝对收缩和选择算子（LASSO）选择最佳特征以构建预测PWI/DWI不匹配的放射组学特征。使用受试者工作特征（ROC）曲线评估模型的性能。应用100次内部交叉验证评估模型的稳定性。使用决策曲线分析（DCA）评估模型的临床价值。
最终选择21个特征建立放射组学模型。在训练集中，ROC曲线下面积（AUC）为0.92，灵敏度、特异度和准确度分别为0.93、0.75、0.82。在验证集中，AUC为0.90，灵敏度、特异度和准确度分别为0.88、0.74、0.80。训练集中100次内部交叉验证的平均AUC在验证集中分别为0.88和0.83。DCA显示，在0.08至1.0的阈值范围内，模型获得更多净效益。
基于ADC图的放射组学模型可以有效确定AIS患者中IP的存在。
