基于 CT 的放射组学模型可能预测恶性肺肿瘤微波消融的早期疗效。
CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors.
机构信息
Shaoxing University School of Medicine, Shaoxing, 312000, China.
Zhejiang University School of Medicine, Hangzhou, 310000, China.
出版信息
Cancer Imaging. 2023 Jun 12;23(1):60. doi: 10.1186/s40644-023-00571-w.
PURPOSE
To establish and validate radiomics models for predicting the early efficacy (less than 3 months) of microwave ablation (MWA) in malignant lung tumors.
METHODS
The study enrolled 130 malignant lung tumor patients (72 in the training cohort, 32 in the testing cohort, and 26 in the validation cohort) treated with MWA. Post-operation CT images were analyzed. To evaluate the therapeutic effect of ablation, three models were constructed by least absolute shrinkage and selection operator and logistic regression: the tumoral radiomics (T-RO), peritumoral radiomics (P-RO), and tumoral-peritumoral radiomics (TP-RO) models. Univariate and multivariate analyses were performed to identify clinical variables and radiomics features associated with early efficacy, which were incorporated into the combined radiomics (C-RO) model. The performance of the C-RO model was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). The C-RO model was used to derive the best cutoff value of ROC and to distinguish the high-risk group (Nomo-score of C-RO model below than cutoff value) from the low-risk group (Nomo-score of C-RO model higher than cutoff value) for survival analysis of patients.
RESULTS
Four radiomics features were selected from the region of interest of tumoral and peritumoral CT images, which showed good performance for evaluating prognosis and early efficacy in three cohorts. The C-RO model had the highest AUC value in all models, and the C-RO model was better than the P-RO model (AUC in training, 0.896 vs. 0.740; p = 0.036). The DCA confirmed the clinical benefit of the C-RO model. Survival analysis revealed that in the C-RO model, the low-risk group defined by best cutoff value had significantly better progression-free survival than the high-risk group (p<0.05).
CONCLUSIONS
CT-based radiomics models in malignant lung tumor patients after MWA could be useful for individualized risk classification and treatment.
目的
建立并验证预测恶性肺肿瘤微波消融（MWA）早期疗效（<3 个月）的放射组学模型。
方法
本研究纳入了 130 例接受 MWA 治疗的恶性肺肿瘤患者（训练队列 72 例，测试队列 32 例，验证队列 26 例）。对术后 CT 图像进行分析。为了评估消融的治疗效果，通过最小绝对收缩和选择算子和逻辑回归构建了三个模型：肿瘤放射组学（T-RO）、肿瘤周围放射组学（P-RO）和肿瘤-肿瘤周围放射组学（TP-RO）模型。进行单变量和多变量分析以确定与早期疗效相关的临床变量和放射组学特征，并将其纳入联合放射组学（C-RO）模型。通过受试者工作特征（ROC）曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估 C-RO 模型的性能。使用 C-RO 模型确定 ROC 的最佳截断值，并区分高风险组（C-RO 模型的 Nomo 评分低于截断值）和低风险组（C-RO 模型的 Nomo 评分高于截断值），以进行患者的生存分析。
结果
从肿瘤和肿瘤周围 CT 图像的感兴趣区域中选择了四个放射组学特征，它们在三个队列中均表现出良好的预后和早期疗效评估性能。在所有模型中，C-RO 模型的 AUC 值最高，并且 C-RO 模型优于 P-RO 模型（训练队列的 AUC 分别为 0.896 和 0.740，p=0.036）。DCA 证实了 C-RO 模型的临床获益。生存分析表明，在 C-RO 模型中，通过最佳截断值定义的低风险组的无进展生存期明显优于高风险组（p<0.05）。
结论
MWA 后恶性肺肿瘤患者的基于 CT 的放射组学模型可用于个体化风险分类和治疗。
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