Renal cell carcinoma (RCC) is the most common malignant epithelial tumor of the kidney, accounting for 85–90% of all solid renal tumors in adults and comprising 1–3% of all malignant visceral neoplasms. Recently, computed tomography (CT) is considered to be the „golden standard„ in diagnostic imaging of RCC. However, the use of CT is always associated with radiation exposure and consequently a significant increase in the risk of malignancy in patients with aplastic processes. In recent years, MRI is increasingly attracting the attention of clinicians as a method of choice for the diagnosis and staging of the RCC, due to several advantages over CT: excellent image quality, high information content, the absence of any radiation exposure to the patients and staff, the ability to obtain three-dimensional images, assessment of renal function using contrast.

The application of DWI representing the MRI modality which uses strong bipolar gradients to enhance sensitivity to thermally induced Brownian motion of hydrogen molecules allows to measure molecular diffusion in tissues in vivo. To date, DWI is mainly used for differential diagnosis of tumors of the central nervous system, but in recent years encouraging data has been received on the use of this technique in the diagnosis of diseases of other organs, including kidneys. ADC is a quantitative parameter calculated from DWI images which is used as a measure of diffusion in healthy and affected tissues. The study included 58 adult patients with pathologically verified clear cell subtype of the renal cell carcinoma (ccRCC). All patients underwent renal MR imaging which included diffusion-weighted imaging (DWI) with subsequent apparent diffusion coefficient measurement (ADC). Axial DWI was performed with the following parameters: TR – 12000 ms, TE – 90 ms, field of view – 40•40 cm; matrix – 200•192; NEX – 3; bandwidth – 250 kHz, diffusion direction–slice, slice thickness – 6,0 mm, inter-scan gap – 1,0 mm with b-value – 0,800 mm2/s), acquisition time – 17 s. DWI was conducted before contrast media administration, using single-shot echo-planar imaging sequence with parallel imaging technique and fat saturation during one breath-hold. Color ADC map was generated automatically at the workstation (Advantage Windows, GE Healthcare). The ADC was calculated with linear regression analysis of the function S=S0 × exp (–b × ADC), where S is the signal intensity after application of the diffusion gradient and S0 is the signal intensity on the DW image acquired at b = 0 sec/mm2. The region of interest (ROI) was placed within a portion of the solid area where the minimum ADC value on the ADC map was registered according to the color by visual inspection. In the result of the clinical trial we observed significant difference in mean ADC value of the normal renal parenchyma and ccRCC – 1,82 ± 0,16 • 10–3 mm2/s vs 2,15 ± 0,12 • 10–3 mm2/s correspondingly (р < 0,05). Also statistically reliable difference in ADC values in patients with high and low ccRCC grades was obtained. Application of DWI modality of MR imaging with ADC calculation allows obtaining valuable information that is vital for the diagnosis of ccRCC and differentiation of its degree of malignancy which can be used in order to enhance the biopsy results as well as a separate diagnostic tool. Further research is needed to establish the difference in the ADC values of the other histological subtypes of RCC and benign tumors of the kidney.

Ключові слова: renal cell carcinoma, magnetic resonance imaging, diffusion-weighted imaging, apparent diffusion coefficient

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1. 1. Hatakenaka M, Soeda H, Yabuuchi H, Matsuo Y, Kamitani T, Oda Y et al. Apparent diffusion coefficients of breast tumors: clinical application. Magnetic Resonance in Medical Sciences. 2008;7(1):23-9. doi.org/10.2463/mrms.7.23
2. 2. Le Bihan D. Molecular diffusion nuclear magnetic resonance imaging. Magnetic Resonance Quarterly. 1991;7(1):1-30.
3. 3. Mathews J, Forsythe A, Brady Z, Butler M, Goergen S, Byrnes G et al. Cancer risk in 680 000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians. BMJ. 2013;346:360. doi.org/10.1136/bmj.f2360
4. 4. Miguel V, Fernando L, Carlos M, Ricardo A. Nuclear grade prediction of renal cell carcinoma using contrasted computed tomography. J Urol. 2009;181(4):249-249. doi.org/10.1016/S0022-5347(09)60709-6
5. 5. Sun M, Ngo L, Genega E, Atkins M, Finn M, Rofsky N et al. Renal cell carcinoma: dynamic contrastenhanced MR imaging for differentiation of tumor subtypes—correlation with pathologic findings. Radiology. 2009;250(3):793-802. doi.org/10.1148/radiol.2503080995
6. 6. Taouli B, Thakur R, Mannelli L, Babb J, Kim S, Hecht E et al. Renal lesions: characterization with diffusion-weighted imaging versus contrast-enhanced MR imaging. Radiology. 2009;251(2):398-407. doi.org/10.1148/radiol.2512080880
7. 7. Wang H, Cheng L, Zhang X, Wang D, Guo A, Gao Y et al. Renal cell carcinoma: diffusion-weighted MR imaging for subtype differentiation at 3.0 T. Radiology. 2010;257(1):135-43. doi.org/10.1148/radiol.10092396