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基于影像组学的临床和病理ⅠA 期纯磨玻璃密度非小细胞肺癌生存风险分层。

Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non-Small Cell Lung Cancer.

机构信息

From the Departments of Radiology (T.W., Y.Y., X.S.) and Thoracic Surgery (Y.S., X.L., Y.Z., J.D., M.Z., D.X., C.C.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, Zhengmin Rd 507, Shanghai 200443, China; and Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C.).

出版信息

Radiology. 2022 Feb;302(2):425-434. doi: 10.1148/radiol.2021210109. Epub 2021 Nov 2.

Abstract

Background Radiomics-based biomarkers enable the prognostication of resected non-small cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA pure-solid tumors requires further determination. Purpose To construct an efficient radiomics signature for survival risk stratification personalized for patients with clinical stage and pathologic stage IA pure-solid NSCLC. Materials and Methods In this retrospective study, six radiomics signatures were constructed for patients with stage IA pure-solid NSCLC who underwent resection between January 2011 and December 2013 at authors' institution and were tested in the radiogenomics data set. The radiomics features were extracted from the intratumoral two-dimensional region, three-dimensional volume, and peritumoral area using PyRadiomics. The discriminative abilities of the signatures were quantified using the area under the time-dependent receiver operating characteristic curve (AUC), and the optimal signature was selected for patient stratification. Results The study included 592 patients with stage IA pure-solid NSCLC (median age, 61 years; interquartile range, 55-66 years; 269 women) for radiomics analysis: 381 patients for training, 163 for internal validation, and 48 for external validation. The radiomics signature combining three-region features yielded the highest 3- and 5-year AUCs of 0.77 and 0.78, respectively, in the internal validation set and 0.76 and 0.75, respectively, in the external validation set. Multivariable analysis suggested that the radiomics signature remained an independent prognostic factor (hazard ratio, 6.2; 95% CI: 3.5, 11.0; < .001) and improved the discriminative ability and clinical usefulness of conventional clinical predictors. Conclusion The radiomics signature with multiregional features helped stratify the survival risk of patients with clinical stage and pathologic stage IA pure-solid non-small cell lung cancer. Published under a CC BY 4.0 license. See also the editorial by Hsu and Sohn in this issue.

摘要

背景 基于放射组学的生物标志物可用于预测可切除的非小细胞肺癌(NSCLC),但它们在临床分期和病理分期 IA 纯实性肿瘤中的有效性尚需进一步确定。目的 为临床分期和病理分期 IA 纯实性 NSCLC 患者构建一种有效的放射组学特征,用于生存风险分层。材料与方法 本回顾性研究纳入了作者所在机构 2011 年 1 月至 2013 年 12 月接受手术治疗的临床分期和病理分期 IA 纯实性 NSCLC 患者,共 592 例。使用 PyRadiomics 从肿瘤内二维区域、三维体积和肿瘤周围区域提取放射组学特征。使用时间依赖性接收器操作特征曲线(AUC)下面积来量化特征的判别能力,并选择最优特征进行患者分层。结果 本研究纳入了 592 例临床分期和病理分期 IA 纯实性 NSCLC 患者(中位年龄为 61 岁,四分位间距为 55-66 岁;269 例为女性)进行放射组学分析:381 例用于训练,163 例用于内部验证,48 例用于外部验证。在内部验证集中,联合三区特征的放射组学特征的 3 年和 5 年 AUC 最高,分别为 0.77 和 0.78;在外部验证集中,3 年和 5 年 AUC 分别为 0.76 和 0.75。多变量分析表明,放射组学特征仍然是独立的预后因素(风险比,6.2;95%CI:3.5,11.0;<.001),并提高了传统临床预测因素的判别能力和临床实用性。结论 多区域特征的放射组学特征有助于分层临床分期和病理分期 IA 纯实性非小细胞肺癌患者的生存风险。在知识共享署名 4.0 许可下发布。也可参见本期 Hsu 和 Sohn 的社论。

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