Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang 050000, China.
Biomed Res Int. 2021 Feb 2;2021:6621894. doi: 10.1155/2021/6621894. eCollection 2021.
To evaluate the efficiency of a radiomics model in predicting the prognosis of patients with acute paraquat poisoning (APP).
Chest computed tomography images and clinical data of 80 patients with APP were obtained from November 2014 to October 2017, which were randomly assigned to a primary group and a validation group by a ratio of 7 : 3, and then the radiomics features were extracted from the whole lung. Principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were used to select the features and establish the radiomics signature (Rad-score). Multivariate logistic regression analysis was used to establish a radiomics prediction model incorporating the Rad-score and clinical risk factors; the model was represented by nomogram. The performance of the nomogram was confirmed by its discrimination and calibration.
The area under the ROC curve of operation was 0.942 and 0.865, respectively, in the primary and validation datasets. The sensitivity and specificity were 0.864 and 0.914 and 0.778 and 0.929, and the prediction accuracy rates were 89.5% and 87%, respectively. Predictors included in the individualized predictive nomograms include the Rad-score, blood paraquat concentration, creatine kinase, and serum creatinine. The AUC of the nomogram was 0.973 and 0.944 in the primary and validation datasets, and the sensitivity and specificity were 0.943 and 0.955, respectively, in the primary dataset and 0.889 and 0.929 in the validation dataset, and the prediction accuracy was 94.7% and 91.3%, respectively.
The radiomics nomogram incorporates the radiomics signature and hematological laboratory data, which can be conveniently used to facilitate the individualized prediction of the prognosis of APP patients.
评估放射组学模型预测急性百草枯中毒（APP）患者预后的效率。
从 2014 年 11 月至 2017 年 10 月获得了 80 例 APP 患者的胸部 CT 图像和临床资料，通过 7：3 的比例将其随机分配到初级组和验证组，并从整个肺部提取放射组学特征。采用主成分分析（PCA）和最小绝对收缩和选择算子（LASSO）回归法选择特征并建立放射组学特征（Rad-score）。采用多变量逻辑回归分析建立纳入 Rad-score 和临床危险因素的放射组学预测模型；该模型由列线图表示。通过区分度和校准度来验证列线图的性能。
初级和验证数据集的 ROC 曲线下面积分别为 0.942 和 0.865。灵敏度和特异度分别为 0.864 和 0.914，以及 0.778 和 0.929，预测准确率分别为 89.5%和 87%。纳入个体化预测列线图的预测因子包括 Rad-score、血百草枯浓度、肌酸激酶和血清肌酐。初级和验证数据集的列线图 AUC 分别为 0.973 和 0.944，初级数据集的灵敏度和特异度分别为 0.943 和 0.955，验证数据集的灵敏度和特异度分别为 0.889 和 0.929，预测准确率分别为 94.7%和 91.3%。
放射组学列线图纳入了放射组学特征和血液学实验室数据，可以方便地用于预测 APP 患者的预后。
