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定量计算机断层扫描成像生物标志物在肺癌诊断与管理中的应用

Quantitative Computed Tomography Imaging Biomarkers in the Diagnosis and Management of Lung Cancer.

作者信息

Kim Hyungjin, Park Chang Min, Goo Jin Mo, Wildberger Joachim E, Kauczor Hans-Ulrich

机构信息

From the *Department of Radiology, College of Medicine, and †Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul; ‡Aerospace Medical Group, Air Force Education and Training Command, Jinju City; §Cancer Research Institute, Seoul National University, Seoul, South Korea; ║Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands; and ¶Department of Diagnostic and Interventional Radiology, University of Heidelberg, Heidelberg, Germany.

出版信息

Invest Radiol. 2015 Sep;50(9):571-83. doi: 10.1097/RLI.0000000000000152.

Abstract

Tumor diameter has traditionally been used as a standard metric in terms of diagnosis and prognosis prediction of lung cancer. However, recent advances in imaging techniques and data analyses have enabled novel quantitative imaging biomarkers that can characterize disease status more comprehensively and/or predict tumor behavior more precisely. The most widely used imaging modality for lung tumor assessment is computed tomography. Therefore, we focused on computed tomography imaging biomarkers such as tumor volume and mass, ground-glass opacities, perfusion parameters, as well as texture features in this review. Herein, we first appraised the conventional 1- or 2-dimensional measurement with brief discussion on their limits and then introduced the potential imaging biomarkers with emphasis on the current understanding of their clinical usefulness with respect to the malignancy differentiation, treatment response monitoring, and patient outcome prediction.

摘要

传统上,肿瘤直径一直被用作肺癌诊断和预后预测的标准指标。然而,成像技术和数据分析的最新进展催生了新型定量成像生物标志物,这些标志物能够更全面地表征疾病状态和/或更精确地预测肿瘤行为。用于肺肿瘤评估的最广泛使用的成像方式是计算机断层扫描。因此,在本综述中,我们重点关注了计算机断层扫描成像生物标志物,如肿瘤体积和质量、磨玻璃影、灌注参数以及纹理特征。在此,我们首先评估了传统的一维或二维测量方法,并简要讨论了其局限性,然后介绍了潜在的成像生物标志物,重点阐述了目前对其在恶性肿瘤鉴别、治疗反应监测和患者预后预测方面临床应用价值的理解。

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