Application of perfusion computed tomography in renal diseases (review of literature)

Purpose. To analyze the literature data on the use of CT perfusion in kidney diseases and to assess the future prospects of using the technique in clinical practice.

Materials and methods. In electronic databases (PubMed, E-library, Web of Science, Google Scholar), a search was conducted for published studies evaluating the possibilities of using CT perfusion in both neoplastic and non-neoplastic kidney diseases. The article analyzes the results of 40 most relevant works of Russian and foreign researchers devoted to this topic.

Results. According to the analysis of the data obtained, perfusion CT is an effective diagnostic tool in oncology: the technique allows noninvasively assessing the nature of the tumour, including differentiating benign nodes (fat-poor angiomyolipoma and oncocytoma) from renal cell carcinoma; to establish the histological variant of renal cell carcinoma and Fuhrman grade, to characterize the effectiveness of ablative techniques and systemic treatment of renal cell carcinoma. Based on the correlation of CT kidney perfusion data and the results of various methods for determining organ function, the possibility of using perfusion CT as one of the prognostic factors for determining the tactics of treatment of patients with obstructive uropathies, aortomesenteric compression, and also shows the potential of using the technique in transplantology both in patients after surgery and during the examination of donors.

Conclusions. Despite the fact that the role of CT kidney perfusion in various fields of urology and nephrology has been sufficiently studied, some important aspects of the likely application of this technique remain underestimated. Taking into account the high incidence rates and a significant percentage of localized forms of tumors, the study of the role of CT perfusion in planning and evaluating the results of nephron-sparing treatment of renal cell carcinoma may open up new prospects in optimizing surgical tactics.

1. Axel L. Cerebral blood flow determination by rapid-sequence computed tomography: theoretical analysis. Radiology. 1980; 137 (3): 679–686. http://doi.org/10.1148/radiology.137.3.7003648

2. Koh T., Tan C., Cheong L. et al. Cerebral perfusion mapping using a robust and efficient method for deconvolution analysis of dynamic contrast-enhanced images. Neuroimage. 2006; 32 (2): 643–653. http://doi.org/10.1016/j.neuroimage.2006.03.042

3. Schaefer P., Roccatagliata L., Ledezma C. et al. First-pass quantitative CT perfusion identifies thresholds for salvageable penumbra in acute stroke patients treated with intra-arterial therapy. Am. J. Neuroradiol. 2006; 27 (1): 20–25. PMID: 16418350

4. d'Esterre C., Roversi G., Padroni M. et al. CT perfusion cerebral blood volume does not always predict infarct core in acute ischemic stroke. Neurol. Sci. 2015; 36 (10): 1777–1783. http://doi.org/10.1007/s10072-015-2244-8

5. Murphy B., Fox A., Lee D. et al. Identification of penumbra and infarct in acute ischemic stroke using computed tomography perfusion-derived blood flow and blood volume measurements. Stroke. 2006; 37 (7): 1771–1777. http://doi.org/10.1161/01.STR.0000227243.96808.53

6. Alves J., Carneiro Â., Xavier J. Reliability of CT perfusion in the evaluation of the ischaemic penumbra. Neuroradiol. J. 2014; 27 (1): 91–95. http://doi.org/10.15274/NRJ-2014-10010

7. Konig M., Banach-Planchamp R., Kraus M. et al. CT perfusion imaging in acute ischemic cerebral infarct: comparison of cerebral perfusion maps and conventional CT findings. Rofo. 2000; 172 (3): 219–226. German. http://doi.org/10.1055/s-2000-120. PMID: 10778451

8. Koenig M., Kraus M., Theek C. et al. Quantitative assessment of the ischemic brain by means of perfusion-related parameters derived from perfusion CT. Stroke. 2001; 32 (2): 431–437. http://doi.org/10.1161/01.str.32.2.431

9. Miles K., Hayball., Dixon A. Colour perfusion imaging: a new application of computed tomography. Lancet. 1991; 337 (8742): 643–645. http://doi.org/10.1016/0140-6736(91)92455-b

10. Kambadakone A., Sahani D. Body perfusion CT: technique, clinical applications, and advances. Radiol. Clin. N. Am. 2009; 47 (1): 161–178. http://doi.org/10.1016/j.rcl.2008.11.003.

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

12. Garcia-Figueiras R., Goh V., Padhani A. et al. CT perfusion in oncologic imaging: a useful tool? Am. J. Roentgenol. 2013; 200 (1): 8–19. http://doi.org/10.2214/AJR.11.8476

13. Sitartchouk I., Roberts H., Pereira A. et al. Computed tomography perfusion using first pass methods for lung nodule characterization. Invest. Radiol. 2008; 43: 349–358. http://doi.org/10.1097/RLI.0b013e3181690148

14. Sahani D., Holalkere N., Mueller P. et al. Advanced hepatocellular carcinoma: CT perfusion of liver and tumor tissue-initial experience. Radiology. 2007; 243: 736–743. http://doi.org/10.1148/radiol.2433052020

15. Rumboldt Z., Al-Okaili R., Deveikis J. Perfusion CT for head and neck tumors: pilot study. Am. J. Neuroradiol. 2005; 26: 1178–1785. PMID: 15891181

16. Li Y., Yang Z., Chen T., Chen H. et al. Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer. 2008; 61: 44–53. http://doi.org/10.1016/j.lungcan.2007.10.021.

17. d’Assignies G., Couvelard A., Bahrami S. et al. Pancreatic endocrine tumors: tumor blood flow assessed with perfusion CT reflects angiogenesis and correlates with prognostic factors. Radiology. 2008; 250: 407_16. http://doi.org/10.1148/radiol.2501080291.

18. Feng S., Sun C., Li Z. et al. Evaluation of microvessel density and vascular endothelial growth factor in colorectal carcinoma with 64-multidetector-row CT perfusion imaging. Zhonghua Wei Chang Wai Ke Za Zhi. 2008; 11: 537–541. PMID: 19031129

19. Grenier N., Cornelis F., Le Bras Y. et al. Perfusion imaging in renal diseases. Diagn. Interv. Imaging. 2013; 94 (12): 1313–1322. http://doi.org/10.1016/j.diii.2013.08.018

20. Das C., Thingujam U., Panda A. et al. Perfusion computed tomography in renal cell carcinoma. Wld J. Radiol. 2015; 7 (7): 170–179. http://doi.org/10.4329/wjr.v7.i7.170

21. Prezzi D., Khan A., Goh V. Perfusion CT imaging of treatment response in oncology. Eur. J. Radiol. 2015; 84 (12): 2380–2385. http://doi.org/10.1016/j.ejrad.2015.03.022

22. El-Diasty M., Gaballa G., Gad H. et al. Evaluation of CT perfusion parameters for assessment of split renal function in healthy donors. Egypt. J. Radiol. Nuclear Med. 2016; 47 (4): 1681–1688. http://doi.org/10.1016/j.ejrnm.2016.07.017

23. Cai X.-R., Zhou Q.C., Yu J. et al. Assessment of renal function in patients with unilateral ureteral obstruction using whole-organ perfusion imaging with 320-detector row computed tomography. PLoS One. 2015; 10 (4): e0122454. http://doi.org/10.1371/journal.pone.0122454

24. Yilmaz O., Ovali G., Genc A. et al. Perfusion computed tomography could be a new tool for single-session imaging of ureteric obstructive pathology: an experimental study in rats. J. Pediatr. Surg. 2009; 44 (10): 1977–1983. http://doi.org/10.1016/j.jpedsurg.2009.01.072

25. Grenier N., Merville P., Combe C. Radiologic imaging of the renal parenchyma structure and function. Nat. Rev. Nephrol. 2016; 12 (6): 348–359. http://doi.org/10.1038/nrneph.2016.44

26. Helck A., Wessely M., Notohamiprodjo M. et al. CT perfusion technique for assessment of early kidney allograft dysfunction: preliminary results. Eur. Radiol. 2013; 23 (9): 2475–2481. http://doi.org/10.1007/s00330-013-2862-6

27. Deniffel D., Boutelir T., Labani A. et al. Computed tomography perfusion measurements in renal lesions obtained by bayesian estimation, advanced singular-value decomposition deconvolution, maximum slope and Patlak models. Invest. Radiol. 2018; 53 (8): 477–485. http://doi.org/10.1097/RLI.0000000000000477

28. Chen Y., Zhang J., Dai J. et al. Angiogenesis of renal cell carcinoma: perfusion CT findings. Abdom. Imaging. 2010; 35 (5): 622–628. http://doi.org/10.1007/s00261-009-9565-0

29. Chen C., Liu Q., Hao Q. et al. Study of 320-slice dynamic volume CT perfusion in different pathologic types of kidney tumor: preliminary results. PLoS One. 2014; 9 (1): e85522. http://doi.org/10.1371/journal.pone.0085522

30. Chen C., Kang Q., Xu B. et al. Fat poor angiomyolipoma differentiation from renal cell carcinoma at 320-slice dynamic volume CT perfusion. Abdom. Radiol. (NY). 2018; 43 (5): 1223–1230. http://doi.org/10.1007/s00261-017-1286-1

31. Mazzei F., Mazzei M., Cioffi Squitieri N. et al. CT perfusion in the characterisation of renal lesions: an added value to multiphasic CT. Biomed. Res. Int. 2014; 2014: 135013. http://doi.org/10.1155/2014/135013

32. Reiner C., Goetti R., Eberli D. et al. CT perfusion of renal cell carcinoma: impact of volume coverage on quantitative analysis. Invest. Radiol. 2012; 47 (1): 33–40. http://doi.org/10.1097/RLI.0b013e31822598c3

33. Reiner C., Roessle M., Thiesler T. et al. Computed tomography perfusion imaging of renal cell carcinoma: systematic comparison with histopathological angiogenic and prognostic markers. Invest. Radiol. 2013; 48 (4): 183–191. http://doi.org/10.1097/RLI.0b013e31827c63a3

34. Rosenbaum C., Wach S., Kunath F. et al. Dynamic tissue perfusion measurement: a new tool for characterizing renal perfusion in renal cell carcinoma patients. Urol. Int. 2013; 90 (1): 87–94. http://doi.org/10.1159/000341262

35. Rubtsova N.A., Golbits A.B., Kryaneva E.V. et al. The role of ct-perfusion for diagnostic of solid renal tumors. Diagnostic radiology and radiotherapy. 2021; 2 (12): 70–78. http://doi.org/10.22328/2079-5343-2021-12-2-70-78 (In Russian)

36. Chen C., Kang Q., Wei Q. et al. Correlation between CT perfusion parameters and Fuhrman grade in pTlb renal cell carcinoma. Abdom. Radiol (NY). 2017; 42 (5): 1464–1471. http://doi.org/10.1007/s00261-016-1009-z.

37. Chen C., Kang Q., Xu B. et al. Differentiation of low- and high-grade clear cell renal cell carcinoma: Tumor size versus CT perfusion parameters. Clin. Imaging. 2017; 46: 14–19. http://doi.org/10.1016/j.clinimag.2017.06.010

38. Shu J., Tang Y., Cui J. et al. Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade. Eur. J. Radiol. 2018; 109: 8–12. http://doi.org/10.1016/j.ejrad.2018.10.005

39. Drljevic-Nielsen A., Rasmussen F., Mains J. et al. Baseline blood volume identified by dynamic contrast-enhanced computed tomography as a new independent prognostic factor in metastatic renal cell carcinoma. Transl. Oncol. 2020; 13 (10): 100829. http://doi.org/10.1016/j.tranon.2020.100829

40. Mains J., Donskov F, Pedersen E. et al. Dynamic Contrast-Enhanced Computed Tomography-Derived Blood Volume and Blood Flow Correlate With Patient Outcome in Metastatic Renal Cell Carcinoma. Invest. Radiol. 2017; 52 (2): 103–110. http://doi.org/10.1097/RLI.0000000000000315

41. Mains J., Donskov F., Pedersen E. et al. Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients. Br. J. Radiol. 2018; 91 (1082): 20160795. http://doi.org/10.1259/bjr.20160795

42. Fan A., Sundaram V., Kino A. et al. Early Changes in CT Perfusion Parameters: Primary Renal Carcinoma Versus Metastases After Treatment with Targeted Therapy. Cancers (Basel). 2019; 11 (5): 608. http://doi.org/10.3390/cancers11050608

43. Vehabovic-Delic A., Balic M., Rossmann C. et al. Volume Computed Tomography Perfusion Imaging: Evaluation of the Significance in Oncologic Follow-up of Metastasizing Renal Cell Carcinoma in the Early Period of Targeted Therapy – Preliminary Results. J. Comput. Assist. Tomogr. 2019; 43 (3): 493–498. http://doi.org/10.1097/RCT.0000000000000848

44. Fournier L., Oudard S., Thiam R. et al. Metastatic renal carcinoma: evaluation of antiangiogenic therapy with dynamic contrast-enhanced CT. Radiology. 2010; 256 (2): 511–518. http://doi.org/10.1148/radiol.10091362

45. Hudson J., Bailey C., Atri M. et al. The prognostic and predictive value of vascular response parameters measured by dynamic contrast-enhanced-CT, -MRI and -US in patients with metastatic renal cell carcinoma receiving sunitinib. Eur. Radiol. 2018; 28 (6): 2281–2290. http://doi.org/10.1007/s00330-017-5220-2

46. Nielsen T., Ostraat O., Graumann O. et al. Computed Tomography Perfusion, Magnetic Resonance Imaging, and Histopathological Findings After Laparoscopic Renal Cryoablation: An In Vivo Pig Model. Technol. Cancer Res. Treat. 2017; 16 (4): 406–413. http://doi.org/10.1177/1533034616657251

47. Squillaci E., Manenti G., Cicciò C. et al. Perfusion-CT monitoring of cryo-ablated renal cells tumors. J. Exp. Clin. Cancer Res. 2009; 28 (1): 138. http://doi.org/10.1186/1756-9966-28-138

48. Aleksandrova K.A., Serova N.S., Rudenko V.I. et al. Opportunities of CT-perfusion in the evaluation of renal blood flow in patients with urolithiasis. REJR. 2019; 9 (1): 108–117. http://doi.org/10.21569/22227415201991108117 (In Russian)

49. Aleksandrova K.A., Serova N.S., Rudenko V.I. et al. Clinical value of ct-perfusion in patients with ureteric stones. Urologia. 2019; 5: 38–43. http://doi.org/10.18565/urology.2019.5.38-43 (In Russian)

50. Aleksandrova K.A., Serova N.S., Rudenko V.I., et al. Clinical value of ct-perfusion in patients with ureteric stones. REJR. 2018; 8 (4) 208–219. http://doi.org/10.21569/2222-7415-2018-8-4-208-219 (In Russian)

51. Zhang Z., Cen C., Qian K. et al. Assessment of the embolization effect of temperature-sensitive p(N-isopropylacrylamide-co-butyl methylacrylate) nanogels in the rabbit renal artery by CT perfusion and confirmed by macroscopic examination. Sci. Rep. 2021; 11 (1): 4826. http://doi.org/10.1038/s41598-021-84372-w

52. Zhong J., Yuan J., Chong V. et al. The clinical application of one-stop examination with 640-slice volume CT for Nutcracker syndrome. PLoS One. 2013; 8 (9): e74365. http://doi.org/10.1371/journal.pone.0074365

53. Liu D., Liu J., Wen Z. et al. 320-row CT renal perfusion imaging in patients with aortic dissection: A preliminary study. PLoS One. 2017; 12 (2): e0171235. http://doi.org/10.1371/journal.pone.0171235

54. Al-Said J., Kamel O. Changes in renal cortical and medullary perfusion in a patient with renal vein thrombosis. Saudi J. Kidney Dis. Transpl. 2010; 21 (1): 123–127. PMID: 20061706.

55. Braunagel M., Helck A., Wagner A. et al. Dynamic Contrast-Enhanced Computed Tomography: A New Diagnostic Tool to Assess Renal Perfusion After Ischemia-Reperfusion Injury in Mice: Correlation of Perfusion Deficit to Histopathologic Damage. Invest. Radiol. 2016; 51 (5): 316–322. http://doi.org/10.1097/RLI.0000000000000245

56. Miles K., Griffiths M. Perfusion CT: a worthwhile enhancement? Br. J. Radiol. 2003; 76 (904): 220–231. http://doi.org/10.1259/bjr/13564625

57. Miles K. Perfusion CT for the assessment of tumour vascularity: which protocol? Br. J. Radiol. 2003; 76 Spec No 1: S36–42. http://doi.org/10.1259/bjr/18486642

58. Rubtsova N.A., Golbits A.B., Kryaneva E.V. et al. CT perfusion of the kidneys in oncology: a teaching aid. М.: MNIOI im. P.A. Gertsena – filial FGBU “NMITS radiologii” Minzdrava Rossii. 2020. 28 p. (In Russian)

59. Jeong S., Park S., Chang I., Shin J. et al. Estimation of renal function using kidney dynamic contrast material-enhanced CT perfusion: accuracy and feasibility. Abdom. Radiol. (NY). 2021; 46 (5): 2045–2051. http://doi.org/10.1007/s00261-020-02826-7.