Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study.

IF 3.5 2区 医学 Q2 ONCOLOGY
Weiyue Chen, Guihan Lin, Ye Feng, Yongjun Chen, Yanjun Li, Jianbin Li, Weibo Mao, Yang Jing, Chunli Kong, Yumin Hu, Minjiang Chen, Shuiwei Xia, Chenying Lu, Jianfei Tu, Jiansong Ji
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引用次数: 0

Abstract

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 (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), 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 GPTV3 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 GPTV3-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 GPTV3 effectively mined tumor features to predict ALK mutation status and stratify PFS outcomes in patients with lung adenocarcinoma.

瘤内和瘤周CT放射组学预测肺腺癌患者间变性淋巴瘤激酶突变和生存:一项多中心研究。
背景:探讨瘤内和瘤周放射组学在术前预测肺腺癌患者间变性淋巴瘤激酶(ALK)突变状态和生存中的价值。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(训练组和外部验证组1-3)。基于ct的放射组学特征分别从肿瘤总体积(GTV)和肿瘤周围3-、6-、9-、12-和15-mm区域(GPTV3、GPTV6、GPTV9、GPTV12和GPTV15)中提取,筛选出最相关的特征构建放射组学模型预测ALK(+)。将最佳放射组学模型的放射组学评分(Rad-scores)与临床预测因子结合构建联合模型。采用受试者工作特征(ROC)分析评价疗效。使用Cox比例风险模型检查无进展生存期(PFS)结果。结果:4组患者中ALK(+)占21.19%(107/505)。使用支持向量机算法的GPTV3放射组学模型的预测性能最好,验证集中的平均AUC最高,为0.811。临床TNM分期和胸膜压痕是独立的预测因素。结合gptv3 - rad评分和临床预测因子的联合模型在预测三个验证集的ALK(+)方面的表现优于单独的临床模型[AUC: 0.855 (95% CI: 0.766-0.919) vs. 0.648 (95% CI: 0.543-0.745), P = 0.001;结论:基于GPTV3的联合模型可以有效地挖掘肿瘤特征,预测肺腺癌患者ALK突变状态,并对PFS结果进行分层。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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