Rationale and Objectives
This study aimed to evaluate the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiation of four spinal lesions by using heuristic and pharmacokinetic parameters analyzed from DCE signal intensity time course.
Materials and Methods
DCE-MRI of 62 subjects with confirmed myeloma ( n = 9), metastatic cancer ( n = 22), lymphoma ( n = 7), and inflammatory tuberculosis (TB) ( n = 24) in the spine were analyzed retrospectively. The region of interest was placed on strongly enhanced tissues. The DCE time course was categorized as the “wash-out,” “plateau,” or “persistent enhancement” pattern. The maximum enhancement, steepest wash-in enhancement, and wash-out slope using the signal intensity at 67 seconds after contrast injection as reference were measured. The Tofts 2-compartmental pharmacokinetic model was applied to obtain K trans and k ep . Pearson correlation between heuristic and pharmacokinetic parameters was evaluated, and receiver operating characteristic curve analysis was performed for pairwise group differentiation.
Results
The mean wash-out slope was −22% ± 10% for myeloma, 1% ± 0.4% for metastatic cancer, 3% ± 3% for lymphoma, and 7% ± 10% for TB, and it could significantly distinguish myeloma from metastasis (area under the curve [AUC] = 0.884), lymphoma (AUC = 1.0), and TB (AUC = 1.0) with P = .001, and distinguish metastasis from TB (AUC = 0.741) with P = .005. The k ep and wash-out slope were highly correlated (r = 0.92), and they showed a similar diagnostic performance. The K trans was significantly correlated with the maximum enhancement (r = 0.71) and the steepest wash-in enhancement (r = 0.85), but they had inferior diagnostic performance compared to the wash-out slope.
Conclusions
DCE-MRI may provide additional diagnostic information, and a simple wash-out slope had the best diagnostic performance. The heuristic and pharmacokinetic parameters were highly correlated.
Introduction
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the standard method widely used for diagnosis of breast and prostate cancer . Because it can be easily performed, many studies have applied this method to characterize soft tissue lesions in other organs . In this study DCE-MRI was used to characterize four lesions in the spine, including myeloma, metastatic cancer, lymphoma, and inflammatory tuberculosis (TB). In DCE-MRI, multiple sets of images were acquired before and after injection of contrast agents, and the measured signal intensity time course (or, the enhancement kinetics) reflected the vascular and cellular properties of the lesion. The heuristic parameters, such as maximum enhancement, wash-in slope, and wash-out slope can be directly measured from the DCE kinetics. The more sophisticated two-compartmental model can be used to analyze the transport of contrast agents between the vascular space and the interstitial space to obtain pharmacokinetic parameters such as K trans and k ep . Many different methods have been applied to analyze the DCE kinetics, but there is no consensus on which method is the best . It is in general agreed that as long as characteristic parameters can be reliably extracted, they may provide helpful information for a correct diagnosis as well as surgical planning.
DCE-MRI has been applied to diagnose different spinal lesions and distinguish between hypervascular (eg, renal cell carcinoma) and hypovascular (eg, prostate cancer) metastases . It has also been used to characterize vascular properties of hematologic malignancies (eg, myeloma and lymphoma) with different grades of bone marrow involvement, as well as to evaluate changes after various therapies for predicting treatment outcome and prognosis . These studies used different DCE-MRI analysis methods to generate heuristic parameters or pharmacokinetic parameters. So far there is no published study to systematically compare the different DCE parameters for spinal lesions analyzed using different approaches. The purpose of this study is to investigate the correlation of DCE-MRI parameters obtained by using heuristic and pharmacokinetic analyses and to investigate further their diagnostic performance for differentiating four spinal lesions. The obtained DCE-MRI diagnostic parameters may provide information for characterizing the extent of disease for guiding biopsy and planning of surgery and subsequent adjuvant treatments.
Materials and Methods
Patients
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MRI Protocol
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Heuristic Parameters Analyzed From DCE Kinetics
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MaximumEnhancementRatio=[(Smax−S0)/S0] Maximum
Enhancement
Ratio
=
[
(
S
max
−
S
0
)
/
S
0
]
SteepestWash-inEnhancementRatio=[(S2−S1)/S0] Steepest
Wash-in
Enhancement
Ratio
=
[
(
S
2
−
S
1
)
/
S
0
]
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Wash-outSlope=[(Slast−Speak)/Speak]×100%or,[(Slast−S67seconds)/S67seconds]×100% Wash-out
Slope
=
[
(
S
last
−
S
peak
)
/
S
peak
]
×
100
%
or
,
[
(
S
last
−
S
67
seconds
)
/
S
67
seconds
]
×
100
%
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Pharmacokinetic Analysis to Obtain K trans and k ep
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Cb=D[a1exp(−m1t)+a2exp(−m2t)] C
b
=
D
[
a
1
exp
(
−
m
1
t
)
+
a
2
exp
(
−
m
2
t
)
]
where D was the injection dose, 0.1 [mmol/kg]. The parameters used for fast, medium, and slow blood curves were taken from the commercial DCE analysis program SygnoTissue4D (Siemens Healthcare, Erlangen, Germany). For the fast blood curve, a 1 = 92.0 [kg/L], a 2 = 6.4 [kg/L], m 1 = 5.3 [1/min], and m 2 = 0.016 [1/min]; for the medium blood curve, a 1 = 24.0 [kg/L], a 2 = 6.2 [kg/L], m 1 = 3.0 [1/min], and m 2 = 0.016 [1/min]; for the slow blood curve, a 1 = 3.99 [kg/L], a 2 = 4.78 [kg/L], m 1 = 0.144 [1/min], and m 2 = 0.011 [1/min].
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Statistical Analyses
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Results
Lesion Group Comparisons
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TABLE 1
The Distribution of Enhancement Kinetic Patterns in the Four Lesion Groups
Wash-out
n (%) Plateau
n (%) Persistent
n (%) Myeloma
( n = 9) 9(100) 0 0 Metastasis
( n = 22) 7(32) 11(50) 4(18) Lymphoma
( n = 7) 0 7(100) 0 Tuberculosis
( n = 24) 1(4) 12(50) 11(46)
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TABLE 2
Heuristic Parameters Analyzed From DCE Enhancement Kinetics of Lesions in Four Disease Groups, and the Pharmacokinetic Parameters Obtained by Using the Fast, Medium, and Slow Blood Curves (Mean ± Standard Deviation Is Shown)
Maximum Enhancement Ratio Wash-in Enhancement Ratio Wash-out Slope K trans -Fast
(1/min) K trans -Medium
(1/min) K trans -Slow
(1/min) k ep -Fast
(1/min) k ep -Medium
(1/min) k ep -Slow
(1/min) Myeloma
( n = 9) 2.3 ± 0.7 1.7 ± 0.5 −22±10% 0.114 ± 0.036 0.29 ± 0.15 1.24 ± 0.77 0.88 ± 0.26 1.86 ± 0.88 6.50 ± 3.13 Metastasis
( n = 22) 1.7 ± 0.6 1.1 ± 0.4 1 ± 0.4% 0.077 ± 0.028 0.18 ± 0.07 0.66 ± 0.32 0.49 ± 0.23 1.09 ± 0.39 4.22 ± 1.46 Lymphoma
( n = 7) 2.7 ± 0.4 1.0 ± 0.3 3 ± 3% 0.068 ± 0.016 0.16 ± 0.03 0.67 ± 0.17 0.34 ± 0.06 0.83 ± 0.07 4.27 ± 0.79 Tuberculosis
( n = 24) 2.0 ± 0.8 1.0 ± 0.6 7 ± 10% 0.077 ± 0.036 0.17 ± 0.08 0.77 ± 0.63 0.27 ± 0.15 0.72 ± 0.25 3.90 ± 2.23
DCE, dynamic contrast-enhanced.
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ROC Diagnostic Comparisons
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TABLE 3
The Area Under the ROC Curve (AUC) to Differentiate Between Different Disease Groups Using Heuristic and Pharmacokinetic Parameters Measured From DCE Kinetics
Maximum Enhancement Ratio Wash-in Enhancement Ratio Wash-out Slope K trans -Fast
(1/min) k ep -Fast
(1/min) AUC (95% CI)P Value AUC (95% CI)P Value AUC (95% CI)P Value AUC (95% CI)P Value AUC (95% CI)P Value Myeloma vs. metastasis0.810\* (0.658–.963).0070.803\* (0.638–0.968).0090.884\* (0.766–1.00).0010.798\* (0.632–0.964).0100.864\* (0.717–1.00).002 Myeloma vs lymphoma 0.683(0.408–0.957) .2230.810\* (0.588–1.00).0391.000\* (1.00–1.00).0010.841\* (0.636–1.00).0231.000\* (1.00–1.00).001 Myeloma vs tuberculosis(TB) 0.583(0.373–0.793) .4670.737\* (0.533–0.942).0411.000\* (1.00–1.00).0010.782\* (0.617–0.948).0140.995\* (0.980–1.00).001 Metastasis vs lymphoma0.909\* (0.793–1.00).001 0.435(0.200–0.671) .610 0.734(0.558–0.910) .067 0.487(0.263–0.711) .919 0.734(0.558–0.910) .067 Metastasis vs TB 0.631(0.466–0.796) .129 0.602(0.433–0.771) .2350.741\* (0.595–0.886).005 0.464(0.293–0.636) .6760.780\* (0.643–0.918).001 Lymphoma vs TB 0.702(0.521–0.884) .108 0.521(0.299–0.742) .869 0.607(0.419–0.796) .395 0.512(0.300–0.724) .925 0.673(0.490–0.855) .171
95% CI: 95% confidence intervals shown in brackets.
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Correlation of DCE Heuristic Parameters and K trans , k ep
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Discussion
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Conclusions
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Acknowledgments
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