体重指数对CT 0-4量表可靠性的影响： 计算机断层扫描协议的比较

封面图片

如何引用文章

全文:

详细

论证。由于在对抗COVID-19的过程中使用胸部计算机断层扫描的频率越来越高，因此有必要应用低剂量计算机断层扫描(LDCT)来减少患者身体的剂量负荷，同时保持研究的诊断价值.然而，在已发表的文献中未发现有关患者体重指数对COVID-19患者LDCT诊断准确性影响的数据。

目的是评估患者的BMI对放射科医生在解释COVID-١٩相关肺炎的标准和低剂量胸部CT扫描时在٠-٤视觉半定量CT评分上的一致程度的影响。

材料与方法。一项回顾性多中心研究，其中在一次访问时每位参与者接受了两次连续的胸部检查，使用标准和低剂量方案。对标准和低剂量胸部CT扫描的肺部和软组织核素的解释是以视觉半定量的CT ٠-٤尺度进行的。每个方案的数据根据体重指数的值进行分组（病理学阈值等于公斤/平方米）。协议是根据二元和加权分类计算的。通过方差单因素方差分析来评估各组平均值之间是否存在统计学上的显著差异。

结果。在患者总数（n=231）中，٢٣٠人符合确立的研究纳入标准。专家为每位患者处理了٤项标准和低剂量计算机断层扫描研究，包括肺和软组织卷积核。体重正常的患者比例为 ٣١٪（٧١ 人），样本的中位体重指数中位为 ٢٧.٥（١٨.٣；٤٨.٣）公斤/平方米。无论是二元分类还是加权分类，组间配对比较未发现统计学上的显著差异（p值分别为٠.٠٩和٠.١٢）。超重患者组根据肥胖程度进一步划分，但研究结果对这种划分是不变的（没有统计学上的显着差异：身体质量参数最大不同组别»正常»和»٣度肥胖»的p值为٠.١٧）。

结论。患者的体重指数不影响在٠-٤的视觉半定量CT等级上对 COVID-١٩胸部标准和低剂量计算机断层扫描的解释。

作者简介

Ivan A. Blokhin

Moscow Center for Diagnostics and Telemedicine

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN 代码: 3306-1387
俄罗斯联邦, Moscow

Anna P. Gonchar

Moscow Center for Diagnostics and Telemedicine

Email: a.gonchar@npcmr.ru
ORCID iD: 0000-0001-5161-6540
SPIN 代码: 3513-9531
俄罗斯联邦, Moscow

Maria R. Kodenko

Moscow Center for Diagnostics and Telemedicine; Bauman Moscow State Technical University

编辑信件的主要联系方式.
Email: m.kodenko@npcmr.ru
ORCID iD: 0000-0002-0166-3768
SPIN 代码: 5789-0319
俄罗斯联邦, Moscow; Moscow

Alexander V. Solovev

Moscow Center for Diagnostics and Telemedicine

Email: a.solovev@npcmr.ru
ORCID iD: 0000-0003-4485-2638
SPIN 代码: 9654-4005
俄罗斯联邦, Moscow

Victor A. Gombolevskiy

Artificial Intelligence Research Institute

Email: g_victor@mail.ru
ORCID iD: 0000-0003-1816-1315
SPIN 代码: 6810-3279

MD, Cand. Sci. (Med.)

俄罗斯联邦, Moscow

Roman V. Reshetnikov

Moscow Center for Diagnostics and Telemedicine; The First Sechenov Moscow State Medical University (Sechenov University)

Email: reshetnikov@fbb.msu.ru
ORCID iD: 0000-0002-9661-0254
SPIN 代码: 8592-0558

Cand. Sci. (Phys.-Math.)

俄罗斯联邦, Moscow; Moscow

参考

  1. Islam N, Ebrahimzadeh S, Salameh JP, et al. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev. 2021;3(3):CD013639. doi: 10.1002/14651858.CD013639.pub4
  2. Morozov SP, Chernina VY, Blokhin IA, Gombolevskiy V. Chest computed tomography for outcome prediction in laboratory-confirmed COVID-19: a retrospective analysis of 38,051 cases. Digital Diagnostics. 2020;1(1):27–36. doi: 10.17816/DD46791
  3. Prasad KN, Cole WC, Haase GM. Radiation protection in humans: extending the concept of as low as reasonably achievable (Alara) from dose to biological damage. BJR. 2004;77(914):97–99. doi: 10.1259/bjr/88081058
  4. Sakane H, Ishida M, Shi L, et al. Biological effects of low-dose chest CT on chromosomal DNA. Radiol. 2020;295(2):439–445. doi: 10.1148/radiol.2020190389
  5. Du Y, Lv Y, Zha W, et al. Association of body mass index (BMI) with critical COVID-19 and in-hospital mortality: a dose-response meta-analysis. Metabolism. 2021;117:154373. doi: 10.1016/j.metabol.2020.154373
  6. Ohana M, Ludes C, Schaal M, et al. Quel avenir pour la radiographie thoracique face au scanner ultra-low dose? Revue Pneumologie Clinique. 2017;73(1):3–12. doi: 10.1016/j.pneumo.2016.09.007
  7. Manowitz A, Sedlar M, Griffon M, et al. Use of BMI guidelines and individual dose tracking to minimize radiation exposure from low-dose helical chest CT scanning in a lung cancer screening program. Academ Radiol. 2012;19(1):84–88. doi: 10.1016/j.acra.2011.09.015
  8. Paul NS, Kashani H, Odedra D, et al. The influence of chest wall tissue composition in determining image noise during cardiac CT. Am J Roentgenol. 2011;197(6):1328–1334. doi: 10.2214/AJR.11.6816
  9. Blokhin I, Gombolevskiy V, Chernina V, et al. Inter-observer agreement between low-dose and standard-dose CT with soft and sharp convolution kernels in COVID-19 pneumonia. J Clin Med. 2022;11(3):669. doi: 10.3390/jcm11030669
  10. Morozov SP, Gombolevskiy VA, Elizarov AB, et al. A simplified cluster model and a tool adapted for collaborative labeling of lung cancer CT scans. Computer Methods Programs Biomed. 2021;206:106111. doi: 10.1016/j.cmpb.2021.106111
  11. Powell-Wiley TM, Poirier P, Burke LE, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984–e1010. doi: 10.1161/CIR.0000000000000973
  12. The R Foundation. The R Project for Statistical Computing [Internet]. Available from: . Accessed: 15.03.2022.
  13. Fisher RA. XXI. ―On the dominance ratio. Proceedings Royal Soc Edinburgh. 1923;42:321–341. doi: 10.1017/S0370164600023993
  14. Levene H. Robust tests for equality of variances. In: Olkin I, Ghurye S, Hoeffding W, et al. Contributions to probability and statistics: essays in honor of harold hotelling. Standford University Press; 1961. Р. 279–292.
  15. Mosteller F. Data analysis and regression: a second course in statistics. Addison-Wesley Pub. Co., Boston; 1977. 588 p.
  16. Kubo T, Ohno Y, Nishino M, et al. Low dose chest CT protocol (50 mas) as a routine protocol for comprehensive assessment of intrathoracic abnormality. Eur J Radiol Open. 2016;3:86–94. doi: 10.1016/j.ejro.2016.04.001
  17. Silin АY, Gruzdev IS, Morozov SP. The influence of model iterative reconstruction on the image quality in standard and low-dose computer tomography of the chest. Experimental study. J Clin Pract. 2020;11(4):49–54. doi: 10.17816/clinpract34900
  18. Zhu Z, Ming ZX, Feng ZY, et al. Feasibility study of using gemstone spectral imaging (GSI) and adaptive statistical iterative reconstruction (ASIR) for reducing radiation and iodine contrast dose in abdominal CT patients with high BMI values. PLOS ONE. 2015;10(6):e0129201. doi: 10.1371/journal.pone.0129201
  19. Filatova DA, Sinitsin VE, Mershina EA. Opportunities to reduce the radiation exposure during computed tomography to assess the changes in the lungs in patients with COVID-19: use of adaptive statistical iterative reconstruction. Digital Diagnostics. 2021;2(2):94–104. doi: 10.17816/DD62477
  20. Lee SW, Kim Y, Shim SS, et al. Image quality assessment of ultra-low dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol. 2014;24(4):817–826. doi: 10.1007/s00330-013-3090-9

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. An inter-rater agreement diagram for binary (a) and weighted (b) classifications by body mass index (gray: overweight group, black: normal weight group).

下载 (202KB)
索引源数据
3. Fig. 2. A post-post-hoc analysis of our hypothesis for the similarity of means: (a) binary classification; (b) normalized classification (Sharp CT, Soft CT, Sharp LDCT, and Soft LDCT protocols are coded with A, B, C, and D, respectively; the normal weight group is coded with “1”; the overweight group is coded with “2”).

下载 (479KB)
索引源数据
4. 图1.按体重指数（灰色——超重组，黑色——正常体重组）组的二元 (a) 和加权 (b) 分类专家一致性图。

下载 (188KB)
索引源数据
5. 图2.对均值相似性假设的后验分析： a——二元分类； b——归一化分类（Sharp CT、Soft CT、Sharp LDCT和Soft LDCT方案分别编码为A、B、C和D，正常组编码为 “1”，超重组为 “2”）。

下载 (477KB)
索引源数据

版权所有 © Blokhin I.A., Gonchar A.P., Kodenko M., Solovev A.V., Gombolevskiy V.A., Reshetnikov R.V., 2022

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

##common.cookie##