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1 Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2
Kusunoki-cho, Chuo-ku, Kobe 650-0017, Japan.
2 Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline
Ave., Boston, MA 02115.
3 Department of Radiology, Kobe Ekisaikai Hospital, 1-21-1 Manabigaoka,
Tarumi-ku, Kobe 655-30004, Japan.
4 Division of Cardiovascular and Respiratory Medicine, Department of Internal
Medicine, Kobe University Graduate School of Medicine, Kobe 650-0017,
Japan.
5 Division of Cardiovascular, Thoracic and Pediatric Surgery, Kobe University
Graduate School of Medicine, Kobe 650-0017, Japan.
Received February 6, 2003;
accepted after revision July 23, 2003.
Address correspondence to Y. Ohno
(yosirad{at}kobe-u.ac.jp).
Abstract
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SUBJECTS AND METHODS. Sixty patients with lung cancer (35 men, 25 women) underwent dynamic perfusion MRI, perfusion scintigraphy, and preoperative and postoperative pulmonary function tests (forced expiratory volume in 1 sec [FEV1]). Perfusion MRIs were obtained with a 3D turbo field-echo sequence (TR/TE, 2.7/0.6; flip angle, 40°; matrix, 128 x 96) using a 1.5-T scanner. Regional blood flow was calculated from the signal intensity–time curves after bolus injection of contrast medium on MRI (QMRI) and uptake ratios of radioisotope on perfusion scintigraphy (QPS). Postoperative lung functions predicted by MRI (FEV1,MRI) and perfusion scintigraphy (FEV1,PS) were calculated from preoperative FEV1 and regional Qs. To determine the capability of MRI as an alternative to scintigraphy, we evaluated correlations and the limits of agreement between predicted FEV1,MRI and postoperative FEV1 and between predicted FEV1,PS and postoperative FEV1.
RESULTS. The correlation coefficient of postoperative FEV1 with FEV1,MRI (r = 0.93, p < 0.0001) was better than that with FEV1,PS (r = 0.89, p < 0.0001). The limits of agreement between postoperative FEV1 and predicted FEV1,MRI (0.9% ± 10.4%) were smaller than those between postoperative FEV1 and predicted FEV1,PS (2.1% ± 13.2%).
CONCLUSION. Dynamic perfusion MRI is a feasible alternative to pulmonary perfusion scintigraphy for predicting postoperative lung function in patients with lung cancer.
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Currently, the only method for imaging regional pulmonary perfusion is a nuclear medicine study using technetium-99m (99mTc)-labeled macroaggregated albumin (MAA). For prediction of functional loss, quantitative radionuclide pulmonary perfusion scintigraphy is the current most widely applied method [4, 6, 7]. However, poor spatial resolution, especially for differentiating lobes and segments, remains a major limitation of this method.
Some investigators have reported the usefulness of dynamic contrast-enhanced perfusion MRI to evaluate regional pulmonary perfusion [8–10]. Although this method has been described in the assessment of physiology and pathophysiology in healthy volunteers and animal models [11–13], little has been reported on its capability to substitute for pulmonary perfusion scintigraphy for quantitative and semiquantitative assessment of pulmonary blood flow and prediction of postoperative lung function.
We performed 3D dynamic contrast-enhanced perfusion MRI in patients with lung cancer who were candidates for resection. These patients underwent extensive pre- and postoperative pulmonary function tests and ventilation–perfusion scanning, as well as imaging studies such as chest radiography and CT. Findings included a change in emphysema and various perfusion abnormalities with invasion of pulmonary vessels. Thus, our patient population provided a spectrum of pulmonary perfusion abnormalities.
We hypothesized that dynamic contrast-enhanced perfusion MRI may be used in place of pulmonary perfusion scintigraphy in patients with lung cancer. Specifically, the purposes of our study were to determine the capability of dynamic perfusion MRI to evaluate regional blood flow as a substitute for pulmonary perfusion scintigraphy and to show the potential for assessing postoperative lung function in patients with lung cancer.
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Dynamic Contrast-Enhanced Perfusion MRI
All MRI studies were performed on a 1.5-T scanner (Gyroscan Intera, Philips
Medical Systems, Best, The Netherlands) using a phased array coil. Dynamic
perfusion MRIs (TR/TE, 2.7/0.6; flip angle, 40°; matrix, 128 x 96;
reconstructed matrix, 256 x 192; rectangular field of view,
450–530 x 315–371 mm) were acquired with a 3D radiofrequency
spoiled gradient-echo sequence. We used a 100-mm, 10-partition 3D slab
thickness with a contiguous slice in the coronal plane and a left-to-right
phase-encoded direction, resulting in an effective partition thickness of 10
mm and real phase encoding in the slice direction of five steps. The temporal
resolution was 1.0 sec for each 3D data set. In all patients, a bolus of
3–5 mL of gadopentetate dimeglumine (Magnevist, Schering Japan, Osaka,
Japan) was administered via a cubital vein with an automatic infusion system
(Sonic shot, Nemoto, Tokyo, Japan) at a rate of 3–5 mL/sec, followed by
20 mL of saline solution at the same rate. The basic theory and application of
dynamic contrast-enhanced perfusion MRI have been documented in previous
reports [8,
9]. After careful instruction,
patients practiced the breath-holding technique to reproduce precisely the
same degree of inspiration for each scanning series before the MRI studies. On
each scan, 35 images were obtained during a 35-sec breath-hold at
end-inspiration. All 60 dynamic contrast-enhanced perfusion MRI examinations
were completed successfully. No adverse effects were observed.
Data Analysis of Dynamic Contrast-Enhanced Perfusion MRI
The signal intensity–time curve after administration of gadopentetate
dimeglumine was generated by measuring the signal intensity in the region of
interest defined in the right upper, right middle, right lower, left upper,
left middle, and left lower lung fields, excluding large vessels and pulmonary
arteries, in every slice for all subjects (60 regions of interest per patient)
on the MRI scanner.
From each region of interest, data were transferred and analyzed using a Power Book G3 (Apple Computer, Cupertino, CA) with Excel 98 software (Microsoft, Redmond, WA).
To extract quantitative indexes, we fitted the signal intensity–time
course curves to a gamma variate function (Fig.
1A,
1B) using equation 1 described
in the literature
[10–13]:
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From the gamma variate function, the apparent mean transit time was
calculated as the first moment of the MRI signal intensity–time curve
using an equation also described in the literature
[10]:
The regional pulmonary blood volume was calculated directly from the area of the MRI signal intensity–time course curve for a region of interest. Using the central volume principle, we determined regional blood flow in each region of interest (QROI) by dividing pulmonary blood volume by mean transit time [10, 12–15]. Each QROI was normalized to the integrated arterial input function from the main trunk of the pulmonary artery [10]. The approximate time for taking each region of interest measurement was 1 min.
To compare the regional perfusion of dynamic contrast-enhanced perfusion
MRI with 99mTc-MAA perfusion scintigraphy in each lung field, we
calculated Q in each region of interest evaluated on dynamic
perfusion MRI (QMRI) as
follows:
99mTc-MAA Perfusion Scintigraphy Technique
Pulmonary perfusion scintigraphy was performed after IV administration of
185 MBq of 99mTc-MAA. Images were obtained by a large-field-of-view
gamma camera (e-CAM, Siemens Medical Systems, Forchheim, Germany) equipped
with a medium-energy all-purpose collimator according to the method described
by Markos et al. [6]. The
matrix size was 256 x 256, and the mean energy window and SD of
99mTc was 140 ± 14 keV.
Data Analysis of 99mTc-MAA Perfusion Scintigraphy
In both the anterior (A) and posterior (P) images,
rectangular regions of interest, equal in size, were drawn over the whole lung
(Awhole lung and Pwhole lung). Both
lungs were divided into six regions of interest in the right upper, right
middle, right lower, left upper, left middle, and left lower lung fields in
each subject. To compare the QMRI with
99mTc-MAA perfusion scintigraphic data, we calculated regional
blood flow evaluated by 99mTc-MAA perfusion scintigraphy
(QPS) in each region of interest as
follows:
Pulmonary Function Test
Pulmonary function tests were performed while the patient was at rest in a
seated position. These tests consisted of spirometry and body plethysmography
using System 9 (Minato Ikagaku, Osaka, Japan). The flow-volume loops were
recorded according to American Thoracic Society criteria
[16] within 2 weeks of
surgery. Postoperative FEV1 was measured at the end of the third
month (mean, 11.4 weeks) after surgery with the same equipment and technique.
The FEV1s were normalized as a percentage of predicted
(%FEV1).
Prediction of Postoperative Lung Function
To evaluate the capability for predicting postoperative lung function, we
calculated the predicted postoperative FEV1 (percentage of
predicted) by dynamic perfusion MRI and perfusion scintigraphy as
follows:
Statistical Analysis
To evaluate the capability of dynamic perfusion MRI as an alternative to
99mTc-MAA perfusion scintigraphy, we assessed the correlation, the
limits of agreement, 95% confidence interval (CI) for the bias, and the 95%
CIs of lower and upper limits of agreement between QMRI
and QPS in each lung field.
To evaluate the accuracy of predicted FEV1,MRI and predicted FEV1,PS, we evaluated the following: the relationship between each predicted FEV1 and postoperative FEV1; the limits of agreement between each predicted FEV1 and postoperative FEV1; and the 95% CIs of lower and upper limits of agreements between each predicted FEV1 and postoperative FEV1.
A p value less than 0.05 was considered to be significant in all statistical analyses. The basic theory and application of the limits of agreement, 95% CI for the bias, and the 95% CIs of the lower and upper limits of agreement have been documented in the literature [17].
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The correlation between postoperative FEV1 and predicted FEV1,MRI in each patient is shown in Figure 4. Excellent correlation existed between postoperative FEV1 and predicted FEV1,MRI (r = 0.93, r2 = 0.86, p < 0.0001). The mean and limits of agreement between postoperative FEV1 and predicted FEV1,MRI are shown in Figure 5. The mean and SE were 0.9% and 0.7%, respectively. The 95% CI for bias was –0.5% to 2.3%. The limits of agreement between postoperative FEV1 and predicted FEV1,MRI were between –9.5% and 11.3%. The 95% CI for the lower limit of agreement was –11.9% to –7.1%. The 95% CI for the upper limit of agreement was 8.9–13.7%.
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The correlation between postoperative FEV1 and predicted FEV1,PS in each patient is shown in Figure 6. The correlation between postoperative FEV1 and predicted FEV1,PS was excellent (r = 0.89, r2 = 0.79, p < 0.0001). The mean and limits of agreement between postoperative FEV1 and predicted FEV1,PS are shown in Figure 7. The mean and SE were 2.1% and 0.9%, respectively. The 95% CI for bias was 0.3–3.9%. The limits of agreement between postoperative FEV1 and predicted FEV1,PS were between –11.1% and 15.3%. The 95% CI for the lower limit of agreement was –14.1% to –8.1%. The 95% CI for the upper limit of agreement was 12.3–18.3%.
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The correlation of relative Q between dynamic perfusion MRI and perfusion scintigraphy was excellent. Hence, the mean difference between QMRI and QPS in each lung field was 1.4% with a 95% CI of 1.0–1.8%. The QMRI was evaluated by first-pass of gadolinium contrast agent. The QPS was evaluated by the distribution at the time when 99mTc-MAA was injected. The QPS was evaluated by the uptake of radioisotope by embolized 99mTc-MAA with a molecular size larger than the gadolinium contrast agent. Thus, dynamic MRI tends to give a slightly higher evaluation of regional perfusion. Despite these findings, the limits of agreements (–5.2% and 8.0%) were low enough for us to be confident that dynamic perfusion MRI can be used in place of perfusion scintigraphy for clinical purposes.
Recently, perfusion scintigraphy has played an important role in the preoperative evaluation of pulmonary function in patients with lung cancer [18–20]. Many investigators have suggested the utility of perfusion scintigraphy for the estimation of postoperative lung function [18–20]. However, perfusion scintigraphy was often limited to patients with low postoperative FEV1 because of cost [4, 21]. Furthermore, prediction errors may be large in some cases [22], and accuracy is not improved by the combined use of ventilation scintigraphy [6, 7].
Regarding prediction of postoperative lung function, predicted FEV1,MRI showed excellent correlation with measured postoperative FEV1. The correlation coefficient between postoperative FEV1 and predicted FEV1,MRI (r = 0.93) was better than that between postoperative FEV1 and predicted FEV1,PS (r = 0.89). In addition, the mean difference and the limits of agreement between postoperative FEV1 and predicted FEV1,MRI were smaller than those between postoperative FEV1 and predicted FEV1,PS. The limits of agreement between postoperative FEV1 and predicted FEV1,MRI are considered small enough for clinical study when compared with perfusion scintigraphy.
In patients with lung cancer, 99mTc-MAA perfusion scintigraphy has been most frequently used for prediction of postoperative lung function. Reports have shown good or poor correlation with predicted and postoperative lung function on lobectomy and pneumonectomy [18–20]. However, these studies were not evaluated as to degree of prediction error of perfusion scintigraphy for prediction of postoperative lung function. One report showed promising results using quantitative CT with a dual threshold on standard preoperative CT. Wu et al. [23] showed the capability of quantitative CT for prediction of postoperative FEV1. They reported good correlation between postoperative FEV1 predicted by quantitative CT or perfusion scintigraphy and measured postoperative FEV1. However, the average percentage of errors between prediction by quantitative CT and measured postoperative FEV1 were –4.3% ± 9.7% on pneumonectomy and –3.5% ± 12.8% on lobectomy and were larger than those seen in our study.
When compared with the results of the aforementioned studies, our results showed better correlation and smaller prediction error by performing dynamic perfusion MRI technique with higher temporal resolution and sharper bolus profile of contrast medium. We found that dynamic perfusion MRI showed superior and more objective definition of lobar anatomy in surgical candidates than did perfusion scintigraphy. Therefore, dynamic perfusion MRI may predict postoperative lung function more accurately than other techniques. In addition, dynamic perfusion MRI has the potential for assessment of invasion of the pulmonary artery; spatial resolution of dynamic perfusion MRI can be made to equal that of contrast-enhanced MR angiography using a parallel imaging technique such as sensitivity encoding.
Examination time is also a factor in the potential for dynamic perfusion MRI to substitute for perfusion scintigraphy. Examination time of dynamic perfusion MRI is shorter than that of perfusion scintigraphy. Therefore, more patients considering lung resection can be examined in a timely manner, although the cost of dynamic perfusion MRI per patient is equal to or greater than that of perfusion scintigraphy in the United States. In this study, we did not evaluate the cost-effectiveness of preoperative estimation of postoperative lung function by dynamic perfusion MRI. We plan a future study to compare the cost-effectiveness of dynamic perfusion MRI with perfusion scintigraphy and to show the suitability of dynamic perfusion MRI as an alternative to perfusion scintigraphy.
There were some limitations to this study. First, although all dynamic perfusion MRI examinations were successfully completed without adverse effects and the regional perfusion from signal intensity–time curves had been completely calculated, eight patients with lung cancer and severe chronic obstructive pulmonary diseases needed to "shallow breathe" for data acquisition, and the image quality was slightly degraded between 21 and 35 sec after bolus injection of contrast medium. In patients with lung cancer and low pulmonary function, poor breath-holding capabilities may result in an underestimation of regional perfusion and regional pulmonary function. However, dynamic perfusion MRI is a new technique for estimating postoperative lung function, and advances in faster scanning time may make this technique more practical.
Second, the regional blood flow measurement by the indicator dilution method is a semiquantitative assessment. Although indicator dilution theories are frequently used to determine regional blood volume and regional blood flow by various perfusion MRI techniques, the direct application of these principles to contrast-enhanced first-pass dynamic perfusion MRI experiments is difficult. Regional blood volume can be determined by direct calculation of the area under the observed tissue concentration curve. However, the calculated regional blood flow and mean transit time are less straightforward. Weisskoff et al. [24] pointed out that the use of the central volume principle to calculate mean transit time for locations in a tissue volume is incorrect because MRI signal intensity changes for specific tissue regions of interest reflect the radiotracer concentration remaining in the tissue rather than that leaving the tissue.
Third, the method also assumes that the signal intensity observed is linearly related to the concentration of contrast medium in the blood. Theoretically, this relationship is not linear. However, over a limited range of contrast concentrations, it appears to be sufficiently linear to be valid [25].
Fourth, the model used also assumes that the contrast agent remains in the intravascular space [26]. To the extent that the contrast agent acts as a pure intravascular marker, the volume of distribution will reflect the blood volume. As we have mentioned, preliminary studies found a strong correlation between blood volume divided by apparent mean transit time, which is an MRI measure of regional flow, and regional perfusion as measured with microspheres [9]. Similar results were obtained by Wilke et al. [11, 27], who were able to obtain a quantitative assessment of myocardial perfusion using similar techniques, suggesting that indicator dilution techniques can provide measures of regional blood flow despite these limitations.
In conclusion, dynamic contrast-enhanced perfusion MRI allows noninvasive assessment of pulmonary perfusion in the preoperative evaluation of patients with lung cancer. Dynamic perfusion MRI offers a feasible alternative to perfusion scintigraphy to assess regional blood flow and predict postoperative lung function in patients with lung cancer.
Acknowledgments
We thank Donna Wolfe and Ronald Kukla for their contributions to the
preparation of this manuscript.
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) for
first pass.
