瘤内及瘤周CT影像组学在预测肺腺癌患者间变性淋巴瘤激酶突变及生存中的应用：一项多中心研究
Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study.
作者信息
Chen Weiyue, Lin Guihan, Feng Ye, Chen Yongjun, Li Yanjun, Li Jianbin, Mao Weibo, Jing Yang, Kong Chunli, Hu Yumin, Chen Minjiang, Xia Shuiwei, Lu Chenying, Tu Jianfei, Ji Jiansong
机构信息
Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Csaenter of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.
School of Medicine, Clinical College of The Affiliated Central Hospital, Lishui University, Lishui, 323000, China.
出版信息
Cancer Imaging. 2025 Mar 13;25(1):35. doi: 10.1186/s40644-025-00856-2.
BACKGROUND
To explore the value of intratumoral and peritumoral radiomics in preoperative prediction of anaplastic lymphoma kinase (ALK) mutation status and survival in patients with lung adenocarcinoma.
METHODS
We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1-3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV, GPTV, GPTV, GPTV, and GPTV), and screened the most relevant features to construct radiomics models to predict ALK (+). The combined model incorporated radiomics scores (Rad-scores) of the best radiomics model and clinical predictors was constructed. Performance was evaluated using receiver operating characteristic (ROC) analysis. Progression-free survival (PFS) outcomes were examined using the Cox proportional hazards model.
RESULTS
In the four sets, 21.19% (107/505) patients were ALK (+). The GPTV radiomics model using a support vector machine algorithm achieved the best predictive performance, with the highest average AUC of 0.811 in the validation sets. Clinical TNM stage and pleural indentation were independent predictors. The combined model incorporating the GPTV-Rad-score and clinical predictors achieved higher performance than the clinical model alone in predicting ALK (+) in three validation sets [AUC: 0.855 (95% CI: 0.766-0.919) vs. 0.648 (95% CI: 0.543-0.745), P = 0.001; 0.882 (95% CI: 0.801-0.962) vs. 0.634 (95% CI: 0.548-0.714), P < 0.001; 0.810 (95% CI: 0.727-0.877) vs. 0.663 (95% CI: 0.570-0.748), P = 0.006]. The prediction score of the combined model could stratify PFS outcomes in patients receiving ALK-TKI therapy (HR: 0.37; 95% CI: 0.15-0.89; P = 0.026) and immunotherapy (HR: 2.49; 95% CI: 1.22-5.08; P = 0.012).
CONCLUSION
The presented combined model based on GPTV effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.
背景
探讨肿瘤内及瘤周影像组学在术前预测肺腺癌患者间变性淋巴瘤激酶（ALK）突变状态及生存情况中的价值。
方法
我们回顾性收集了来自四家医院的505例符合条件的肺腺癌患者的数据（训练集和外部验证集1 - 3）。基于CT的影像组学特征分别从大体肿瘤体积（GTV）以及包含瘤周3、6、9、12和15毫米区域的GTV（GPTV、GPTV、GPTV、GPTV和GPTV）中提取，并筛选出最相关特征以构建预测ALK（+）的影像组学模型。构建了结合最佳影像组学模型的影像组学评分（Rad-scores）和临床预测指标的联合模型。使用受试者工作特征（ROC）分析评估性能。采用Cox比例风险模型检查无进展生存期（PFS）结果。
结果
在这四组中，21.19%（107/505）的患者为ALK（+）。使用支持向量机算法的GPTV影像组学模型具有最佳预测性能，在验证集中平均AUC最高为0.811。临床TNM分期和胸膜凹陷是独立预测指标。在三个验证集中，结合GPTV-Rad评分和临床预测指标的联合模型在预测ALK（+）方面比单独的临床模型具有更高的性能[AUC：0.855（95%CI：0.766 - 0.919）对0.648（95%CI：0.543 - 0.745），P = 0.001；0.882（95%CI：0.801 - 0.962）对0.634（95%CI：0.548 - 0.714），P < 0.001；0.810（95%CI：0.727 - 0.877）对0.663（95%CI：0.570 - 0.748），P = 0.006]。联合模型的预测评分可对接受ALK-TKI治疗（HR：0.37；95%CI：0.15 - 0.89；P = 0.026）和免疫治疗（HR：2.49；95%CI：1.22 - 5.08；P = 0.012）患者的PFS结果进行分层。
结论
所提出的基于GPTV的联合模型有效地挖掘了肿瘤特征，以预测肺腺癌患者的ALK突变状态并对PFS结果进行分层。
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