Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, China; Department of Radiology, Shenzhen University First Affiliated Hospital, Shenzhen, 518000, China.
Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, China.
Clin Radiol. 2020 Jul;75(7):562.e11-562.e19. doi: 10.1016/j.crad.2020.03.019. Epub 2020 Apr 16.
To develop and evaluate a radiomics composite model for predicting disease-free survival (DFS) in stage I solid lung adenocarcinoma, and compare it to a simple radiomics model.
Patients of pathological stage I solid lung adenocarcinoma treated with lobectomy (n = 119) were enrolled retrospectively. Three hundred and ninety-seven radiomics features per lesion were extracted from enhanced chest computed tomography (CT) imaging. Spearman's correlation coefficient and the LASSO (least absolute shrinkage and selection operator) regression model were used to reduce the dimension and select radiomics features. Univariate or multivariate logistic regression was used to build prediction models. A survival curve based on the radiomics composite model was plotted with Kaplan-Meier survival analysis to stratify the risk of recurrence. The confusion matrix, receiver operating characteristic (ROC) curve, and decision curve analysis were used to evaluate the performance of the prediction models.
Recurrence occurred in 22.6% of patients. The survival curve of the radiomics composite model could accurately differentiate high-risk from low-risk patients. In the validation sets, the areas under the ROC curves (AUCs) of the pathological TNM stage (8th IASLC), clinicopathological model, radiomics model, and radiomics composite model were 0.587 (95% confidence interval [CI] 0.502-0.650), 0.629 (95% CI 0.558-0.682), 0.726 (95% CI 0.681-0.770), and 0.849 (95% CI 0.783-0.898), respectively.
The prognosis of stage I solid lung adenocarcinoma predicted by an individualised radiomics composite model was more accurate than that of the simple radiomics model.
构建并验证一种用于预测Ⅰ期实性肺腺癌无病生存期（DFS）的影像组学复合模型，并与单纯影像组学模型进行比较。
回顾性分析行肺叶切除术治疗的Ⅰ期实性肺腺癌患者（n=119），从增强胸部 CT 图像中提取每个病灶 397 个影像组学特征。采用 Spearman 相关系数和 LASSO（最小绝对收缩和选择算子）回归模型降维和选择影像组学特征。采用单因素或多因素逻辑回归构建预测模型。基于 Kaplan-Meier 生存分析绘制生存曲线对复发风险进行分层。采用混淆矩阵、ROC 曲线和决策曲线分析评价预测模型的性能。
22.6%的患者出现复发。影像组学复合模型的生存曲线能够准确区分高风险和低风险患者。在验证组中，第 8 版 IASLC 病理 TNM 分期、临床病理模型、影像组学模型和影像组学复合模型的 ROC 曲线下面积（AUC）分别为 0.587（95%置信区间 [CI] 0.502-0.650）、0.629（95% CI 0.558-0.682）、0.726（95% CI 0.681-0.770）和 0.849（95% CI 0.783-0.898）。
与单纯影像组学模型相比，基于个体化影像组学复合模型预测Ⅰ期实性肺腺癌的预后更为准确。
