Preview

PULMONOLOGIYA

Advanced search

Perfusion computer tomography in clarifying the nature of pathological processes in the lung

https://doi.org/10.18093/0869-0189-2020-30-1-92-101

Abstract

The analysis of the literature data on perfusion computed tomography (PCT) in lung diseases is presented. The description of the research method, the method role in the differential diagnosis of the changes nature in the lungs based on the data of the PCT is given. The issues that require further research are being clarified. It was noted that a PCT can become a method of choice in cases when the computed tomography data do not provide the answer to the question about the nature of detected changes in the lung.

About the Authors

I. D. Lagkueva
Federal Russian Scientific Radiology Center, Healthcare Ministry of Russia
Russian Federation

Irina D. Lagkueva, Junior Researcher, Research Department of Novel Technologies and Radiological Diagnosis of Diseases

ul. Profsoyuznaya 86, Moscow, 117997
тel.: (495) 334-81-86 



P. M. Kotlyarov
Federal Russian Scientific Radiology Center, Healthcare Ministry of Russia
Russian Federation

Petr M. Kotlyarov, Doctor of Medicine; professor, Head of Research Department of Novel Technologies and Radiological Diagnosis of Diseases. Affiliation ID: 60105123

ul. Profsoyuznaya 86, Moscow, 117997
тel.: (495) 334-81-86 



N. I. Sergeev
Federal Russian Scientific Radiology Center, Healthcare Ministry of Russia
Russian Federation

Nikolay I. Sergeev, Doctor of Medicine, Leading Researcher, Research Department of Novel Technologies and Radiological Diagnosis of Diseases

ul. Profsoyuznaya 86, Moscow, 117997
тel.: (495) 334-81-86 



V. A. Solodkiy
Federal Russian Scientific Radiology Center, Healthcare Ministry of Russia
Russian Federation

Vladimir A. Solodkiy, Doctor of Medicine; professor, Academician of Russian Academy of Sciences, Director

ul. Profsoyuznaya 86, Moscow, 117997
tel.: (495) 334-81-86 



References

1. Verschakelen J. A., De Wever W. Computed Tomography of the Lung. A Pattern Approach. In: Baert A.L., Knauth M., Sartor K., eds. Medical Radiology. Diagnostic Imaging. Springer-Verlag Berlin Heidelberg; 2007.

2. Kotlyarov P.M. [Radiation methods in the diagnosis of the respiratory diseases]. Russkiy meditsinskiy zhurnal. 2001; 9 (5): 197–207 (in Russian).

3. Swensen S.J., Viggiano R.W., Midthun D.E. et al. Lung nodule enhancement at CT: multicenter study. Radiology. 2000; 214 (1): 73–80. DOI: 10.1148/radiology.214.1.r00ja1473.

4. Kotlyarov P.M. [The multispiral lung computed tomography is the new stage in the development of the lung diseases X-ray diagnostics]. Meditsinskaya vizualizatsiya. 2011; (4): 14–20 (in Russian).

5. Kotlyarov P.M. [Multispiral computed tomography postprocessing for refining diagnosis of diffuse lung diseases]. Pul'monologiya. 2017: 27 (4): 472–477. DOI: 10.18093/08690189-2017-27-4-472-477 (in Russian).

6. Ye X., Chen S., Tian Y. et al. A preliminary exploration of the intravoxel incoherent motion applied in the preoperative evaluation of mediastinal lymph node metastasis of lung cancer. J. Thorac. Dis. 2017; 9 (4): 1073–1080. DOI: 10.21037/jtd.2017.03.110.

7. MacMahon H., Naidich D.P., Goo J.M. et al. Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017. Radiology. 2017; 284 (1): 228–243. DOI: 10.1148/radiol.2017161659.

8. Sim Y.T., Poon F.W. Imaging of solitary pulmonary nodule – a clinical review. Quant. Imaging Med. Surg. 2013; 3 (6): 316–326. DOI: 10.3978/j.issn.2223-4292.2013.12.08.

9. Filippov V.P., Evgushchenko G.V., Gedymin L.E., Sidorova N.F. [The role of lung biopsy in the diagnosis of pulmonary pathology at the prehospital level]. Klinicheskaya meditsina. 2009; 87 (4): 41–43 (in Russian).

10. Zhang M., Kono M. Single pulmonary nodules: evaluation of blood flow patterns with dynamic CT. Radiology. 1997; 205 (2): 471–478. DOI: 10.1148/radiology.205.2.9356631.

11. Rumboldt Z., Al-Okayli R., Devekis J. Perfusion CT for head and neck tumors: a pilot study. Am. J. Neuroradiol. 2005; 26 (5): 1178–1185.

12. Petralia G., Bonello L., Viotti S. et al. CT perfusion in oncology: how to do it. Cancer Imaging. 2010, 10 (1): 8–19. DOI: 10.1102/1470-7330.2010.0001.

13. Kotlyarov P.M., Lagkuyeva I.D., Sergeyev N.I., Solodkiy V.A. [Magnetic resonance imaging for diagnostics of lung diseases]. Pul'monologiya. 2018; 28 (2): 217–223. DOI: 10.18093/0869-0189-2018-28-2-217-233 (in Russian).

14. Miles K.A., Charnsangavej C., Lee F. et al Application of CT in the investigation of angiogenesis in oncology. Acad. Radiol. 2000; 7 (10): 840–850. DOI: 10.1016/S10766332(00)80632-7.

15. Kambadakone A.R., Sahani D.V. Body perfusion CT: technique, clinical applications, and advances. Radiol. Clin. North Am. 2009; 47 (1): 161–178.

16. Malavasi S., Barone D., Gavelli G., Bevilacqua A. Multislice analysis of blood flow values in CT perfusion studies of lung cancer. Biomed. Res. Int. 2017; 2017: 3236893. DOI: 10.1155/2017/3236893.

17. Miles K.A., Griffiths M.R. Perfusion CT: a worthwhile enhancement? Br. J. Radiol. 2003; 76: 220–231. DOI: 10.1259/bjr/13564625.

18. Goh V., Halligan S., Hugill J.A. et al. Quantitative colorectal cancer perfusion measurement using dynamic contrastenhanced multidetector-row computed tomography: effect of acquisition time and implications for protocols. J. Comput. Assist. Tomogr. 2005; 29 (1): 59–63. DOI: 10.1097/01.rct.0000152847.00257.d7.

19. Coche E. Assessment of lung tumor response by perfusion CT. JBR-BTR. 2013; 96 (3): 172–174. DOI: 10.5334/jbr-btr.243.

20. Ohno Y., Fujisawa Y., Koyama H. et al. Dynamic contrastenhanced perfusion area-detector CT assessed with various mathematical models: Its capability for therapeutic outcome prediction for non-small cell lung cancer patients with chemoradiotherapy as compared with that of FDG-PET/CT. Eur. J. Radiol. 2017; 86: 83–91. DOI: 10.1016/j.ejrad.2016.11.008.

21. Ng Q.S., Goh V., Fichte H. et al. Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor quantitative measurements. Radiology. 2006; 239 (2): 547–553. DOI: 10.1148/radiol.2392050568.

22. Bisdas S., Konstantinou G.N., Lee P.S. et al. Dynamic contrast-enhanced CT of head and neck tumors: perfusion measurements using a distributed-parameter tracer kinetic model. Initial results and comparison with deconvolution-based analysis. Phys. Med. Biol. 2007; 52 (20): 6181–6196. DOI: 10.1088/0031-9155/52/20/007.

23. Coche E. Advances and perspectives in lung cancer imaging using multidetector row computed tomography. Expert Rev. Anticancer Ther. 2012; 12 (10): 1313–1326. DOI: 10.1586/era.12.112.

24. Mazzei F.G., Volterrani L., Guerrini S. et al. Reduced time CT perfusion acquisitions are sufficient to measure the permeability surface area product with a deconvolution method. Biomed Res. Int. 2014; 2014: 573268. DOI: 10.1155/2014/573268.

25. Lee T.Y., Ellis R.J., Dunscombe P.B. et al. Quantitative computed tomography of the brain with xenon enhancement: a phantom study with the GE9800 scanner. Phys. Med. Biol. 1990; 35 (7): 925–935. DOI: 10.1088/0031-9155/35/7/008.

26. Gandhi D., Hoeffner E.G., Carlos R.C. et al. Computed tomography perfusion of squamous cell carcinoma of the upper aerodigestive tract. Initial results. J. Comput. Assist. Tomogr. 2003; 27 (5): 687–693. DOI: 10.1097/00004728200309000-00005.

27. Shu S.J., Liu B.L., Jiang H.J. Optimization of the scanning technique and diagnosis of pulmonary nodules with firstpass 64-detector-row perfusion VCT. Clin. Imaging. 2013; 37 (2): 256–264. DOI: 10.1016/j.clinimag.2012.05.004.

28. Ma S.H., Le H.B., Jia B.H. et al. Peripheral pulmonary nodules: relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer. 2008; 8: 186. DOI: 10.1186/1471-2407-8-186.

29. Ng Q.S., Goh V., Milner J. et al. Acute tumor vascular effects following fractionated radiotherapy in human lung cancer: In vivo whole tumor assessment using volumetric perfusion computed tomography. Int. J. Radiat. Oncol. Biol. Phys. 2007; 67 (2): 417–424. DOI: 10.1016/j.ijrobp.2006.10.005.

30. Kiessling F., Boese J., Corvinus C. et al. Perfusion CT in patients with advanced bronchial carcinomas: a novel chance for characterization and treatment monitoring? Eur. Radiol. 2004; 14 (7): 1226–1233.

31. Yuan X., Zhang J., Ao G. et al. Lung cancer perfusion: can we measure pulmonary and bronchial circulation simultaneously? Eur. Radiol. 2012; 22: 1665–1671. DOI: 10.1007/s00330-012-2414-5.

32. Ma E., An R., Gao B. et al. ROI for outlining an entire tumor is a reliable approach for quantification of lung cancer tumor vascular parameters using CT perfusion. Onco Targets Ther. 2016; 9: 2377–2384. DOI: 10.2147/OTT.S98060.

33. Wang Q., Zhang Z., Shan F. et al. Intra‐observer and inter observer agreements for the measurement of dual‐input whole tumor computed tomography perfusion in patients with lung cancer: Influences of the size and inner-air density of tumors. Thorac. Cancer. 2017; 8 (5): 427–435. DOI: 10.1111/1759-7714.12458.

34. Mazzei M.A., Squitieri N.C., Guerrini S. et al. [Quantitative CT perfusion measurements in characterization of solitary pulmonary nodules: new insights and limitations]. Recenti Prog. Med. 2013; 104 (7): 430–437. DOI: 10.1701/1315.14591 (in Italian).

35. Trotsenko S.D., Sotnikov V.M., Pan'shin G.A., Chkhikvadze V.D. [Current problems of postoperative radiotherapy for non-small cell lung cancer]. Vestnik rentgenologii i radiologii. 2015; (2): 47–57. DOI: 10.20862/0042-46762015-0-2-47-57 (in Russian).

36. Solodkiy V.A., Kharchenko V.P., Chkhikvadze V.D. et al. [Results of surgical and combined treatment for nonsmall cell lung cancer with postoperative radiation therapy in a mode of hypofranctionation. Post II. Relapse-free survival and survival without locoregional recurrenc]. Voprosy onkologii. 2016; 62 (1): 72–78 (in Russian).

37. Bremnes R.M., Camps C., Sirera R. Angiogenesis in nonsmall cell lung cancer: the prognostic impact of neoangiogenesis and the cytokines VEGF and bFGF in tumours and blood. Lung Cancer. 2006; 51 (2): 143–158. DOI: 10.1016/j.lungcan.2005.09.005.

38. Liu J., Xiong Z., Hu C. et al. [Correlation between multislice spiral CT pulmonary perfusion imaging and cavity of microvessel in lung cancer]. Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2010; 35 (12): 1242–1247. DOI: 10.3969/j.issn.1672-7347.2010.12.007 (in Chinese).

39. Xiong Z., Liu J.K., Hu C.P. Role of immature microvessels in assessing the relationship between CT perfusion characteristics and differentiation grade in lung cancer. Arch. Med. Res. 2010; 41 (8): 611–617. DOI: 10.1016/j.arcmed.2010.11.005.

40. Ng Q.S., Goh V., Klotz E. et al. Quantitative assessment of lung cancer perfusion using MDCT: does measurement reproducibility improve with greater tumor volume coverage? Am. J. Roentgenol. 2006; 187 (4): 1079–1084. DOI: 10.2214/AJR.05.0889.

41. Shi J., Schmid-Bindert G., Fink C. Dynamic volume perfusion CT in patients with lung cancer: baseline perfusion characteristics of different histological subtypes. Eur. J. Radiol. 2013; 82 (12): e894–900. DOI: 10.1016/j.ejrad.2013.08.023.

42. Li D.W., Wu B.Z., Shi Y.S. et al. Association of CT perfusion imaging with plasma levels of TGF-β1 and VEGF in patients with NSCLC. Asian. Pac. J. Trop. Med. 2016; 9 (2): 177–179.

43. Larici A.R., Calandriello L., Amato M. et al. First-pass perfusion of non-small-cell lung cancer (NSCLC) with 64-detector-row CT: a study of technique repeatability and intra- and interobserver variability. Radiol. Med. 2014; 119 (1): 4–12. DOI: 10.1007/s11547-013-0300-0.

44. Lv Y., Jin Y., Xu D. et al. Assessment of 64-slice spiral computed tomography with perfusion weighted imaging in the early diagnosis of ground-glass opacity lung cancer. J. BUON. 2016; 21 (4): 954–957.

45. Ohno Y., Fujisawa Y., Sugihara N. et al. Dynamic contrastenhanced perfusion area-detector CT: Preliminary comparison of diagnostic performance for N stage assessment with FDG PET/CT in non-small cell lung cancer. Am. J. Roentgenol. 2017; 209 (5): W253–262. DOI: 10.2214/AJR.17.17959.

46. Ohno Y., Koyama H., Matsumoto K. et al. Differentiation of malignant and benign pulmonary nodules with quantitative first-pass 320-detector row perfusion CT versus FDG PET/CT. Radiology. 2011; 258 (2): 599–609. DOI: 10.1148/radiol.10100245.

47. Nasseri F., Eftekhari F. Clinical and radiologic review of normal and abnormal thymus: pearls and pitfalls. Radiographics. 2010; 30 (2): 413–428. DOI: 10.1148/rg.302095131.

48. Goldshtein A.J., Oliva I., Honarpisheh H., Rubinowiz A. A Tour of the thymus: a review of thymic lesions with radiologic correlation. Can. Assoc. Radiol. J. 2015; 66: 5–15. DOI: 10.1016/j.carj.2013.09.003.

49. Tolkacheva G.S., Karmazanovskiy G.G., Vishnevskiy A.A. [What gives the intravenous bolus contrast enhancement for CT (MCT) for the differential diagnosis of small pulmonary peripheral formations?]. Meditsinskaya vizualizatsiya. 2000; 36–40 (in Russian).

50. Kotlyarov P.M., Shimanovskiy N.L. [Bolus contrastenhanced multislice spiral computed tomography of the chest: New possibilities in the diagnosis of lung diseases]. Vestnik rentgenologii i radiologii. 2013; 2: 8–15 (in Russian).

51. Ma S., Le H., Jia B. et al. Peripheral pulmonary nodules: Relationship between multi-slice spiral CT perfusion imaging and tumor angiogenesis and VEGF expression. BMC Cancer. 2008; (8): 186. DOI: 10.1186/1471-2407-8-186.

52. Erasmus J.J., Connolly J.E., McAdams H.P., Roggli V.L. Solitary pulmonary nodules: Part 1. Morphologic evaluation for differentiation of benign and malignant lesions. Radiographics. 2000; 20 (1): 43–58. DOI: 10.1148/radiographics.20.1.g00ja0343.

53. Chayka G.A., Nemerova Z.F. [The question of the differential diagnosis of roundish formation of the lung in the TB dispensary]. Zdravookhranenie Dal'nego Vostoka. 2015; 2 (64): 33–36 (in Russian).

54. Yuan X., Zhang J., Quan C. et al. Differentiation of malignant and benign pulmonary nodules with first-pass dualinput perfusion CT. Eur. Radiol. 2013; 23 (9): 2469–2474. DOI: 10.1007/s00330-013-2842-x.

55. Karashchuk N.P., Kiseleva M.V. [Cancer and tuberculosis of the lung]. Nauchnyy meditsinskiy vestnik Yugry. 2014; 1–2 (5–6): 71–73 (in Russian).

56. Lee Y.H., Kwon W., Kim M.S. et al. Lung perfusion CT: the differentiation of cavitary mass. Eur. J. Radiol. 2010; 73 (1): 59–65. DOI: 10.1016/j.ejrad.2009.04.037.

57. Bakan S., Kandemirli S.G., Dikici A.S. et al. Evaluation of anterior mediastinal solid tumors by CT perfusion: a preliminary study. Diagn. Interv. Radiol. 2017; 23 (1): 10–14. DOI: 10.5152/dir.2016.16093.

58. Ruan C.M., Chen W.J., Zheng L. et al. [Diagnostic values of CT perfusion imaging in pulmonary masses]. Ai Zheng. 2007; 26 (1): 78–83 (in Chinese).


Review

For citations:


Lagkueva I.D., Kotlyarov P.M., Sergeev N.I., Solodkiy V.A. Perfusion computer tomography in clarifying the nature of pathological processes in the lung. PULMONOLOGIYA. 2020;30(1):92-101. (In Russ.) https://doi.org/10.18093/0869-0189-2020-30-1-92-101

Views: 936


ISSN 0869-0189 (Print)
ISSN 2541-9617 (Online)