Combined Diffusion-Weighted, Blood Oxygen Level–Dependent, and Dynamic Contrast-Enhanced MRI for Characterization and Differentiation of Renal Cell Carcinoma

Purpose

To investigate a multiparametric magnetic resonance imaging (MRI) approach comprising diffusion-weighted imaging (DWI), blood oxygen–dependent (BOLD), and dynamic contrast-enhanced (DCE) MRI for characterization and differentiation of primary renal cell carcinoma (RCC).

Material and Methods

Fourteen patients with clear-cell carcinoma and four patients with papillary RCC were examined with DWI, BOLD MRI, and DCE MRI at 1.5T. The apparent diffusion coefficient (ADC) was calculated with a monoexponential decay. The spin-dephasing rate R2* was derived from parametric R2* maps. DCE-MRI was analyzed using a two-compartment exchange model allowing separation of perfusion (plasma flow [F P ] and plasma volume [V P ]), permeability (permeability surface area product [PS]), and extravascular extracellular volume (V E ). Statistical analysis was performed with Wilcoxon signed-rank test, Pearson’s correlation coefficient, and receiver operating characteristic curve analysis.

Results

Clear-cell RCC showed higher ADC and lower R2* compared to papillary subtypes, but differences were not significant. F P of clear-cell subtypes was significantly higher than in papillary RCC. Perfusion parameters showed moderate but significant inverse correlation with R2*. V E showed moderate inverse correlation with ADC. F p and V p showed best sensitivity for histological differentiation.

Conclusion

Multiparametric MRI comprising DWI, BOLD, and DCE MRI is feasible for assessment of primary RCC. BOLD moderately correlates to DCE MRI–derived perfusion. ADC shows moderate correlation to the extracellular volume, but does not correlate to tumor oxygenation or perfusion. In this preliminary study DCE-MRI appeared superior to BOLD and DWI for histological differentiation.

Renal cell carcinoma (RCC) is the most common solid tumor of the kidney . Surgical excision (eg, total or partial nephrectomy) has been the long-standing sole therapy. With the advent of novel therapeutic agents, such as agents targeting the tyrosine kinase (TK) receptors of vascular endothelial growth factor or mammalian target of rapamycin (mTOR) , a more detailed diagnosis of RCC and differentiation of RCC subtypes has become of increasing interest. Apart from recent developments such as novel phase mask filters and newer nanosized contrast agents , functional magnetic resonance imaging (MRI) techniques such as dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) have been applied for characterization of tumor tissue and separation of tumor subtypes. DCE MRI assesses the transit of gadolinium derivates through the tissue as a function of time. By applying physical models, information on perfusion and permeability can be derived, which may serve as biomarkers towards antiangiogenic therapies . DWI exploits the random walk of water molecules as an endogenous contrast agent . The apparent diffusion coefficient (ADC) serves as a potentially convenient, but ambiguous measure on cellularity and perfusion and has been proposed as biological marker for tumor biology and treatment response .

Tumor oxygen levels are also an auspicious biological marker in oncology because low oxygenation levels have been shown to be linked to therapy failure and poor patient outcome . Blood oxygen level–dependent (BOLD) MRI is the only method to noninvasively assess the tissue oxygen content. It uses the paramagnetic effect of deoxyhemoglobin for indirect depiction of renal oxygenation . The concentration of deoxyhemoglobin increases with rising oxygen consumption, leading to a decreasing T2* relaxation time of the surrounding tissue. The spin dephasing rate R2* is therefore an index of the oxygenation of tissue (R2* = 1/T2*). BOLD MRI has been shown to be useful for evaluation of parenchymal kidney diseases, such as renal artery stenosis , and correlates with renal tumor histology or grade . In other tumor entities such as breast cancer, BOLD MRI provided complementary information to DCE MRI , as does BOLD MRI and DWI for the assessment of diabetic nephropathy .

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Material and methods

Study Subjects

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Imaging

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DWI

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BOLD

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DCE MRI

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Postprocessing

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Pathology

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Statistical Analysis

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t=r×n−2√1−r2√ t

−

In which df = degrees of freedom; n = number of pairs of data; and r = correlation coefficient.

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Results

Morphology and Histology

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Table 1

Morphologic Information of Assessed Tumors

Morphology Size (cm) Necrosis Homogeneity Clear cell 5.6 ± 2.5 2 2 Papillary 4.1 ± 1.4 0 3 T1 signal Hypointense Isointense Hyperintense Clear cell 4 8 2 Papillary 2 1 1 T2 signal Hypointense Isointense Hyperintense Clear cell 3 6 5 Papillary 1 1 2 Enhancement Hypointense Isointense Hyperintense Clear cell 6 2 6 Papillary 3 1 0

Tumor signal intensity was compared to renal cortex.

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Functional Analysis

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Table 2

Results of Functional Parameters Derived from DWI (ADC), BOLD (R2*), and DCE MRI (F P , V P , V E , PS)

Histology ADC mm 2 /second R2* seconds −1 F P mL/100 mL/min V P mL/100 mL V E mL/100 mL PS mL/100 mL/min Clear cell

n = 12 1.45 ± 0.16 14.2 ± 7.5 189.1 ± 126.4 ∗ 36.5 ± 23.9 9.2 ± 9.4 3.4 ± 5.4 Papillary

n = 4 1.28 ± 0.15 21.7 ± 10.9 21.1 ± 16.4 10.01 ± 10.3 9.5 ± 1.7 1.8 ± 0.2 Necrotic

n = 2 1.76 ± 0.07 5.3 ± 1.2 NA NA NA NA

ADC, apparent diffusion coefficient; BOLD, blood oxygen–dependent; DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; F P , plasma flow; MRI, magnetic resonance imaging; NA, not available; PS, permeability surface area product; R2*, spin-dephasing rate; V E , extravascular extracellular volume; V P , plasma volume.

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Correlation of Functional MRI Parameters

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Table 3

Correlation of Functional MRI Parameters

R2* F P V P V E PS R2* 1 −0.67 † −0.73 † 0.20 0.18 ADC 0.18 0.34 0.42 −0.57 † 0.24

ADC, apparent diffusion coefficient; F P , plasma flow; MRI, magnetic resonance imaging; PS, permeability surface area product; R2*, spin-dephasing rate; V E , extravascular extracellular volume; V P , plasma volume.

Correlation was calculated with Pearson’s correlation coefficient.

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ROC Curve Analysis

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Table 4

Receiver Operator Characteristic Analysis of Functional MRI Parameters

Threshold AUC SE Sensitivity (%) Specificity (%) CI 95% R2* 12.1 seconds −1 0.688 0.139 100 33 0.415–0.960 ADC 1.1 mm 2 /second 0.854 0.037 100 50 0.781–0.927 F P 21 mL/100 mL/min 0.979 0.068 100 75 0.846–1.000 V P 10 mL/100 mL 0.938 0.034 100 75 0.871–1.000 PS NA 0.646 0.114 NA NA 0.422–0.870 V E NA 0.375 0.120 NA NA 0.140–0.610

ADC, apparent diffusion coefficient; AUC, area under the curve; F P , plasma flow; NA, not available; MRI, magnetic resonance imaging; PS, permeability surface area product; R2*, spin-dephasing rate; SE, standard error; V E , extravascular extracellular volume; V P , plasma volume.

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Discussion

Results of the Single Modalities

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Correlation of the Techniques

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Conclusions

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References

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Combined Diffusion-Weighted, Blood Oxygen Level–Dependent, and Dynamic Contrast-Enhanced MRI for Characterization and Differentiation of Renal Cell Carcinoma

Purpose

Material and Methods

Results

Conclusion

Material and methods

Study Subjects

Imaging

DWI

BOLD

DCE MRI

Postprocessing

Pathology

Statistical Analysis

Results

Morphology and Histology

Functional Analysis

Correlation of Functional MRI Parameters

ROC Curve Analysis

Discussion

Results of the Single Modalities

Correlation of the Techniques

Conclusions

References

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