Department of Radiology, Qilu Hospital of Shandong University, Jinan, China.
Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
J Vasc Interv Radiol. 2024 Dec;35(12):1823-1832.e3. doi: 10.1016/j.jvir.2024.08.018. Epub 2024 Aug 28.
To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the local recurrence of Stage Ia lung adenocarcinoma after microwave ablation (MWA) for assisting clinical decision making.
The data of 360 patients with Stage Ia lung adenocarcinoma who underwent MWA were included in the training (n = 208), internal test (n = 90), and external test (n = 62) sets based on the inclusion and exclusion criteria. The predictors associated with local recurrence were identified using univariate and multivariate analyses of clinical information. The integrated radiomics features were extracted from pre-MWA and post-MWA (scanned immediately after the ablation) computed tomography (CT) images, and 10 radiomics features were selected by the t-test and least absolute shrinkage and selection operator. The L2-logistic regression of machine learning was applied for the clinical model, CT radiomics model, and combined model including clinical predictors and radiomics features. Model performance was evaluated by the receiver operating characteristic and decision curve analysis.
The ablative margin was an independent clinical predictor (P = 0.001; odds ratio [OR], 0.46; 95% CI, 0.29-0.73). The combined model showed the highest area under the curve value among the 3 models (training, 0.86; 95% CI, 0.81-0.91; internal test, 0.93; 95% CI, 0.87-0.98; external test, 0.89; 95% CI, 0.79-0.96).
The combined model could accurately predict the local recurrence of Stage Ia lung adenocarcinoma after MWA to better support a clinical decision.
开发并比较基于机器学习的 3 种不同模型，利用临床信息和整合的放射组学特征预测经微波消融（MWA）治疗后 I 期肺腺癌的局部复发，以辅助临床决策。
纳入符合纳入和排除标准的 360 例接受 MWA 治疗的 I 期肺腺癌患者的数据，用于训练集（n=208）、内部测试集（n=90）和外部测试集（n=62）。通过单变量和多变量分析确定与局部复发相关的预测因子。从 MWA 前和 MWA 后（消融后立即扫描）的 CT 图像中提取整合的放射组学特征，通过 t 检验和最小绝对收缩和选择算子选择 10 个放射组学特征。应用机器学习的 L2-逻辑回归进行临床模型、CT 放射组学模型和包括临床预测因子和放射组学特征的联合模型。通过接受者操作特征和决策曲线分析评估模型性能。
消融边缘是独立的临床预测因子（P=0.001；优势比[OR]，0.46；95%置信区间，0.29-0.73）。联合模型在 3 种模型中显示出最高的曲线下面积值（训练集，0.86；95%置信区间，0.81-0.91；内部测试集，0.93；95%置信区间，0.87-0.98；外部测试集，0.89；95%置信区间，0.79-0.96）。
联合模型可准确预测 MWA 治疗后 I 期肺腺癌的局部复发，以更好地支持临床决策。
