Our study aimed to determine if quantitative mpMRI data could be used as a non-invasive diagnostic imaging tool to differentiate renal cell carcinoma subtypes.
Surgery (partial or radical nephrectomy) remains the standard of care for young and healthy patients with incidentally found RCC. However, the substantial number of elderly patients with such tumors poses a clinical challenge, because rates of medical comorbidities and chances of renal impairment after surgery are generally higher among these patients [15]. This noninvasive quantitative mpMRI could prove clinically useful for treatment planning in elderly patients with co-morbidities in whom biopsy or surgery is high risk, or to promote conservative treatment approaches such as active surveillance and focal ablation in eligible patients and is not part of the standard workup in most clinical centers [16].
To our knowledge, there are few published studies addressing the quantitative role of multiparametric MRI in the characterization of subtypes of renal cell carcinoma with no solid results till now, moreover most of these studies were conducted upon 1.5 tesla MRI machines, our clinical research was unique as it was done at 3 tesla MRI machine, also all our cases underwent surgical resection o the mass with histopathological correlation.
Our analysis regarding the diagnostic value of different quantitative parameters of mpMRI in differentiation between different subtypes of renal cell carcinoma agreed with reported data of previous studies done by Cornelis et al. [1] and Yano et al. [17].
The mean ADC value for ccRCC (1.56 ± 0.27 × 10−3 mm2/s) was significantly higher than that of pRCC (0.96 ± 0.25 × 10−3 mm2/s, P < 0.001) and cbRCC (0.89 ± 0.29 × 10−3 mm2/s, P < 0.001). Similar to our results, Hassanen et al. reported that the mean ADC value of clear cell RCC (1.789 ± 0.5624 × 10−3 mm2/s) was significantly higher (P = 0.0003) than that of both papillary RCC (1.034 ± 0.3411 × 10−3 mm2/s) and chromophobe RCC (1.19 × 10−3 mm2/s) [18].
We found that the cutoff value of ADC in differentiation between clear cell RCC and non-clear cell RCC was 1.35 × 10−3 mm2/s with AUC of 0.936, sensitivity of 91.7% and specificity of 76.7%. This was in agreement with Hassanen et al. study who reported an ADC cutoff value of 1.309 × 10−3 mm2/s for differentiation between the clear cell RCC and non-clear cell RCC with sensitivity of 79.17% and specificity of 78.57% [18].
In the current study, we detected that the mean ADC ratio for ccRCC (0.75 ± 0.13) was significantly higher than that of pRCC (0.46 ± 0.12, P < 0.001) and cbRCC (0.41 ± 0.15, P < 0.001). However, the ADC ratio demonstrated no significant difference between pRCC and cbRCC (P = 0.997).
We found also that the mean SII of pRCC was significantly higher than that of non-papillary RCC, in addition to demonstration of significant difference between ccRCC and cbRCC.
So, the clear cell RCC signal intensity index and ADC ratio is significantly different from those of other RCC, which was constant with the previous studies of Pedrosa et al., Karlo et al., Jhaveri et al., and Yano et al. [17, 19,20,21].
Contrary to Yano et al. study [17] regarding the absolute corticomedullary enhancement parameter, we found that it plays beneficial significant role in differentiation between ccRCC (196.7 ± 81.6) and non-clear subtypes of cbRCC (177.8 ± 77.7, P < 0.001) and pRCC (164.3 ± 84.6, P < 0.001); however it did not show significant difference between chromophobe and papillary subtype (P = 0.889), which was agreed with study of Cornelis et al. [2].
We found that ADC ratio had the best diagnostic performance (AUC = 0.938) followed by Absolute CM Enhancement (AUC = 0.937). However, SII showed lower diagnostic performance (AUC = 0.63) compared to the other two parameters. Similar to our results, Cornelis et al. found that the diagnostic performance of early arterial tumor enhancement (AUC = 0.93) and ADC ratio (AUC = 0.84) were higher than that of SII (AUC = 0.75) [2].
There are numerous clarifications for the discriminatory values paucity in characterization of solid renal lesions subtypes compared to others. No limit on lesion size was set in our study population, similar to other studies which were done for the same purpose as Cornelis et al., Mirka et al., Hötker et al., and Galmiche et al. [2, 22,23,24] without limits on lesion size, while contrary to Yano et al. who limited the study population to tumors < 3 cm [17] or Schieda et al. who studied lesions less than 4 cm in size [11].
We included large lesions more than 4 cm although MR characterization is more likely to be used with small renal masses less than 4 cm for which biopsy can be more challenging and where the prevalence of malignancy is lower. The selection of larger lesions in our study allow better evaluation of diffusion restriction and tissue enhancement; however, it led to variability in mpMRI quantitative results when compared to studies applied only on small renal masses.
In our study, the involved MRI evaluations were carried out on 3 tesla single-scanner rather than Yano et al. whose study was carried out on two 3 T scanners and one of eleven 1.5 T scanners [17]. We think that the studies having single-scanner are possibly more accurate and strong as they can adjust this variance.
In our study, we have number of limitations. First, it is relatively small sample size. Second, although our study was performed on a single-scanner and using a single contrast agent and that was potentially more robust because of controlling the variance. The scanners heterogeneity may actually be challenging the ability of application of the quantitative data of a single-scanner in the practice clinically; if the quantitative data are to be clinically utilized, it should able to be applied across several scanner platforms.