Table_1_A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study.docx

ObjectiveThermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy.
MethodsThis study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making.
ResultsThirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful.
ConclusionA CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.

研究目的
热消融是治疗肺部恶性肿瘤的微创手术方式，但术中评估肿瘤完全消融的标准主要依赖临床医师的主观判断，缺乏量化指标。本研究旨在构建并验证一种基于术中计算机断层扫描（computed tomography）的放射组学列线图（radiomic nomogram），以预测肺部恶性肿瘤的完全消融情况。
研究方法
本研究纳入92例原发性或转移性肺部恶性肿瘤患者的104个病灶，将其随机分为训练队列（n=74）与验证队列（n=30）。当研究医师确认消融手术完成时，从原始CT图像中提取放射组学特征。采用最小冗余最大相关性（minimum redundancy maximum relevance, mRMR）与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）完成高维数据降维与特征筛选。基于放射组学特征联合独立临床预测因素，通过多因素logistic回归分析构建预测模型。计算曲线下面积（area under the curve, AUC）、准确率、灵敏度与特异度，采用受试者工作特征（receiver operating characteristic, ROC）曲线与校准曲线评估模型的预测性能，并通过决策曲线分析（decision curve analysis, DCA）评估该列线图的临床实用性与决策净获益。
研究结果
本研究筛选出13个CT特征以构建放射组学预测模型，该模型对肺部恶性肿瘤完全消融的判断具备良好预测性能。整合放射组学特征与临床预测因素的CT放射组学列线图，在训练队列与验证队列中的AUC分别为0.88（95%置信区间0.80~0.96）与0.87（95%置信区间0.71~1.00）。该放射组学列线图在训练队列与验证队列中均校准良好，与肿瘤完全消融的实际情况高度吻合；决策曲线分析结果显示该列线图具备临床应用价值。
研究结论
基于CT的放射组学列线图在术中判断肺部恶性肿瘤完全消融方面具备良好的预测价值，可为临床决策提供辅助支持。