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筛查发现的肺癌患者死亡率的多因素分析

Multifactorial Analysis of Mortality in Screening Detected Lung Cancer.

作者信息

Digumarthy Subba R, De Man Ruben, Canellas Rodrigo, Otrakji Alexi, Wang Ge, Kalra Mannudeep K

机构信息

Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA.

Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

出版信息

J Oncol. 2018 May 16;2018:1296246. doi: 10.1155/2018/1296246. eCollection 2018.

Abstract

We hypothesized that severity of coronary artery calcification (CAC), emphysema, muscle mass, and fat attenuation can help predict mortality in patients with lung cancer participating in the National Lung Screening Trial (NLST). Following regulatory approval from the Cancer Data Access System (CDAS), all patients diagnosed with lung cancer at the time of the screening study were identified. These subjects were classified into two groups: survivors and nonsurvivors at the conclusion of the NLST trial. These groups were matched based on their age, gender, body mass index (BMI), smoking history, lung cancer stage, and survival time. CAC, emphysema, muscle mass, and subcutaneous fat attenuation were quantified on baseline low-dose chest CT (LDCT) for all patients in both groups. Nonsurvivor group had significantly greater CAC, decreased muscle mass, and higher fat attenuation compared to the survivor group ( < 0.01). No significant difference in severity of emphysema was noted between the two groups ( > 0.1). We thus conclude that it is possible to create a quantitative prediction model for lung cancer mortality for subjects with lung cancer detected on screening low-dose CT (LDCT).

摘要

我们推测,冠状动脉钙化(CAC)、肺气肿、肌肉质量和脂肪衰减程度有助于预测参与国家肺癌筛查试验(NLST)的肺癌患者的死亡率。在获得癌症数据访问系统(CDAS)的监管批准后,确定了筛查研究时所有被诊断为肺癌的患者。这些受试者被分为两组:NLST试验结束时的幸存者和非幸存者。根据年龄、性别、体重指数(BMI)、吸烟史、肺癌分期和生存时间对这些组进行匹配。对两组所有患者的基线低剂量胸部CT(LDCT)进行CAC、肺气肿、肌肉质量和皮下脂肪衰减的量化。与幸存者组相比,非幸存者组的CAC明显更高、肌肉质量下降且脂肪衰减更高(<0.01)。两组之间肺气肿严重程度无显著差异(>0.1)。因此,我们得出结论,有可能为在筛查低剂量CT(LDCT)上检测出肺癌的受试者建立肺癌死亡率的定量预测模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/756b/5976935/8a02f24594e7/JO2018-1296246.001.jpg

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