Abstract
BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) can characterize perfusion and vascularization of tissues. DCE MRI parameters can differentiate between malignant and benign lesions and predict tumor grading. The purpose of this study was to correlate DCE MRI findings and various histopathological parameters in head and neck squamous cell carcinoma (HNSCC). PATIENTS AND METHODS: Sixteen patients with histologically proven HNSCC (11 cases primary tumors and in 5 patients with local tumor recurrence) were included in the study. DCE imaging was performed in all cases and the following parameters were estimated: Ktrans, Ve, Kep, and iAUC. The tumor proliferation index was estimated on Ki 67 antigen stained specimens. Microvessel density parameters (stained vessel area, total vessel area, number of vessels, and mean vessel diameter) were estimated on CD31 antigen stained specimens. Spearman's non-parametric rank sum correlation coefficients were calculated between DCE and different histopathological parameters. RESULTS: The mean values of DCE perfusion parameters were as follows: Ktrans 0.189 ± 0.056 min−1, Kep 0.390 ± 0.160 min−1, Ve 0.548 ± 0.119%, and iAUC 22.40 ± 12.57. Significant correlations were observed between Kep and stained vessel areas (r = 0.51, P = .041) and total vessel areas (r = 0.5118, P = .043); between Ve and mean vessel diameter (r = −0.59, P = .017). Cell count had a tendency to correlate with Ve (r = −0.48, P = .058). In an analysis of the primary HNSCC only, a significant inverse correlation between Ktrans and KI 67 was identified (r = −0.62, P = .041). Our analysis showed significant correlations between DCE parameters and histopathological findings in HNSCC.
Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) has been reported as a technique which is able to characterize perfusion and vascularization of tissues [1], [2]. It has been shown that DCE MRI can be helpful to differentiate between malignant and benign lesions [1]. For example, Yuan et al. reported that lung cancer had a larger volume transfer constant (Ktrans) and a lower volume of the extravascular extracellular leakage space (Ve) in comparison to benign lesions [3]. Similar results were reported by Li et al. for breast lesions [4]. Furthermore, according to Cho et al., DCE MRI parameters can be used to distinguish prostatic cancer from benign changes [5]. Moreover, DCE MRI parameters can also predict tumor grading. As reported previously, Ktrans correlated well with Gleason score in prostatic cancer [5], [6]. According to other reports, Ktrans and Ve correlated with glioma grade [7], [8].
DCE MRI parameters were also associated with prognosis in several malignancies [9], [10]. Koo et al. showed that breast cancers with higher Ktrans or lower Ve had poor prognostic factors and were often of the triple-negative subtype [10].
According to the literature, DCE MRI parameters can predict response to therapy in different tumors. For instance, some authors mentioned that low pretreatment Ktrans in regional lymph node metastases in head and neck cancer was associated with a poor response to concurrent chemoradiation therapy [11].
Furthermore, Andersen et al. showed that DCE MR parameters obtained prior to chemoradiotherapy predicted survival of patients with cervical cancer [12].
Presumably, DCE MRI parameters may be based on tissue composition, such as cellularity and vascular density. However, in this regard there are contradictory data in the literature. While some studies identified significant correlations between DCE MRI and histopathological parameters, others did not [13], [14], [15], [16].
The purpose of this study was to correlate DCE MRI findings and various histopathological parameters in head and neck squamous cell carcinoma (HNSCC).
Material and Methods
This study was IRB-approved and all patients gave their written informed consent.
Patients
Sixteen patients (2 women and 14 men, mean age 57.0 ± 7.5 years, median age 57 years) with histologically proven HNSCC were included in the study. In 11 cases primary HNSCC and in 5 patients local tumor recurrence was diagnosed.
Perfusion Measure
In all cases, dynamic contrast-enhanced (DCE) imaging was performed using T1w DCE sequences according to a protocol reported previously [17]. T1w DCE included 40 subsequent scans à 6 seconds. Imaging parameters were as follows: TR/TE 2.47/0.97 ms, slice thickness 5 mm, flip angle 8°, voxel size 1.2 × 1.0 × 5.0 mm. The contrast application (0.1 mmol Gadobutrol per kg of bodyweight (Gadovist®, Bayer Healthcare, Leverkusen, Germany)) was started after the fifth scan at a rate of 3 ml per second and flushing with 10 ml of normal saline using a power injector (Spectris Solaris, Medrad, Bayer Healthcare, Leverkusen, Germany). The acquired images were transferred to a software module for tissue perfusion estimation (Tissue 4D, Siemens Medical Systems, Erlangen, Germany) as described previously [17], [18]. The software offers a population based approach for the arterial input function (AIF) and the best of three available AIF-options was chosen according to the result of the chi2-parameter, which serves as an error measure for the model fit [17]. The AIF was scaled in relation to the gadolinium dose and modeled according to the bi-exponential model of Tofts and Kermode [19].
The following pharmacokinetic parameters were calculated (for exemplary parameter images see Figure 1, A-E) [17], [18]:
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Ktrans: volume transfer constant which estimates the diffusion of contrast medium from the plasma through the vessel wall into the interstitial space, representing vessel permeability;
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Ve; volume of the extravascular extracellular leakage space (EES);
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Kep: parameter for diffusion of contrast medium from the EES back to the plasma. It is in close relation with Ktrans and ve and is calculated by the formula kep = Ktrans × Ve−1;
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iAUC: model-free measurement of the amount of contrast medium delivered to a VOI within a given time period.
Figure 1.
Imaging and histopathological findings in primary hypopharyngeal squamous cell carcinoma. (A) Postcontrast, fat-saturatedT1w image showing a large left-sided cervical mass. (B-E). DCE imaging findings. Estimated DCE parameters are as follows: Ktrans = 0.302 min−1 (B), Kep = 0.470 min−1 (C), Ve = 0.659% (D), IAUC = 41.77% (E). (F) Immunohistochemical stain (MIB-1 monoclonal antibody). Ki 67 index is 75% and cell count is 789. (G) Immunohistochemical stain (CD 31 monoclonal antibody). Estimated microvessel parameters are as follows: stained vessel area = 0.85%, total vessel area = 1.67%, mean vessel diameter = 15.17 μm, and number of vessels = 28.
In every case, the four parameter maps were projected onto the additionally performed T2 weighted fat-suppressed TSE sequences and the tumor was delineated manually on each slice. This resulted in mean values of Ktrans, Kep, Ve and iAUC averaged over the complete tumor. Areas of gross necrosis or large feeding vessels were avoided.
Histopathological Analysis
In every case, sections from formalin-fixed and paraffin-embedded tissue were cut at 5 mm and stained with various antigens. The tumor proliferation index was estimated on Ki 67 antigen stained specimens by using MIB-1 monoclonal antibody (DakoCytomation, Denmark) as reported previously [20]. The area with the highest number of positive tumor nuclei was selected for the analysis (Figure 1F).
Furthermore, microvessel density was estimated on CD31 antigen stained specimens (DakoCytomation, Denmark) according to the previous description (Figure 1G) [21].
All stained samples were digitalized by using a research microscope Jenalumar (Zeiss, Jena, Germany), with camera Diagnostic instruments 4.2., magnification ×400. The digital histopathological images were transferred as uncompressed TIFF images to ImageJ software 1.48v (National Institutes of Health Image program) with a Windows operating system [22], [23]. The following histopathological parameters were estimated using the software:
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proliferation index (KI 67) as percentage of stained nuclei;
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cell count as a number of all nuclei;
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stained vessel area (%) calculated as CD 31 positive area divided by the total area of the analyzed histological specimens;
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total vessel area (%), i.e. percentage of vessel area including stained vessel area (CD 31+) and vessel lumen.
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number of vessels;
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mean vessel diameter, i.e. a sum of all stained vessel diameters divided by the number of vessels;
In every case, all histopathological parameters were estimated per two high power fields a 0.16 mm2.
Statistical Analysis
Statistical analysis and graphics creation was performed with SPSS 20 (IBM SPSS Statistics, Armonk, NY, USA). Values are presented as mean ± SD. Mean value comparison was carried out using the Mann–Whitney U test. Spearman's non-parametric rank correlation coefficients were calculated between DCE and different histopathological parameters. Significance level was set at P ≤ .05.
Results
A complete overview of the results including median values and ranges is shown in Table 1. The mean values of DCE perfusion parameters were as follows: Ktrans 0.189 ± 0.056 min−1, Kep 0.390 ± 0.160 min−1, Ve 0.548 ± 0.119%, and iAUC 22.40 ± 12.57.
Table 1.
Analyzed Perfusion and Histopathological Parameters of the Tumors
| Parameter | M ± SD | Median | Range |
|---|---|---|---|
| DCE MR parameter | |||
| Ktrans | 0.189 ± 0.056 | 0.1785 | 0.079–0.302 |
| Kep | 0.390 ± 0.160 | 0.3605 | 0.136–0.636 |
| Ve | 0.548 ± 0.119 | 0.5245 | 0.372–0.743 |
| IAUC | 22.40 ± 12.57 | 25.97 | 3.028–41.77 |
| Histopathological parameter | |||
| Cell count, n | 1033 ± 362 | 1003 | 313–1657 |
| Ki 67, % | 46.25 ± 23.98 | 35.00 | 20–90 |
| Stained vessel area,% | 6.259 ± 7.831 | 2.075 | 0.77–27.20 |
| Total vessel area, % | 7.476 ± 8506 | 3.687 | 1.208–30.35 |
| Mean vessel diameter, μm | 19.94 ± 7.113 | 18.34 | 11.49–35.38 |
| Mean number of vessels, n | 53.50 ± 69.41 | 34.00 | 15–301 |
Histopathological analysis yielded the following results: mean cell count was 1033.19 ± 361.67 and the mean level of the proliferation index was 46.25% ± 23.98%. In addition, mean values of tissue vessels were as follows: stained vessel area 6.259% ± 7.831%, total vessel area 7.476% ± 8.506%, mean vessel diameter 19.94% ± 7.113 μm, and mean number of vessels 53.50 ± 69.41 (Table 1).
The results of correlation analysis are shown in Table 2. Significant correlations were observed between Kep and stained vessel areas (r = 0.51, P = .041) and total vessel areas (r = 0.5118, P = .043); between Ve and mean vessel diameter (r = −0.59, P = .017). Cell count had a tendency to correlate with Ve (r = −0.48, P = .058).
Table 2.
Identified Correlations Between DCE Perfusion and Histopathological Parameters in HNSCC
| Parameters | Ktrans, min−1 | Kep, min−1 | Ve, % | IAUC |
|---|---|---|---|---|
| Cell count, n | r = −0.12 (P = .65) | r = 0.17 (P = .53) | r = −0.48 (P = .058) | r = −0.24 (P = .38) |
| Ki 67, % | r = −0.16 (P = .56) | r = −0,13 (P = .63) | r = −0,061 (P = .82) | r = −0,15 (P = .58) |
| Stained vessel area, % | r = 0.21 (P = .42) | r = 0.51 (P = .041) | r = −0.38 (P = .14) | r = 0,4147 (P = .11) |
| Total vessel area, % | r = 0.24 (P = .38) | r = 0.51 (P = .043) | r = −0.39 (P = .13) | r = 0.32 (P = .22) |
| Mean vessel diameter, μm | r = 0.021 (P = .94) | r = 0.39 (P = .13) | r = −0,59 (P = .017) | r = −0.026 (P = .92) |
| Mean number of vessels, n | r = 0.23 (P = .39) | r = 0.14 (P = .61) | r = 0.031 (P = .91) | r = 0.22 (P = .42) |
There were no significant correlations between other analyzed parameters (Table 2).
In an analysis of the primary HNSCC only, a significant inverse correlation between Ktrans and KI 67 was identified (r = −0.62, P = .041). Furthermore, stronger significant correlations were found between Kep and stained vessel areas, total vessel areas, and between Ve and mean vessel diameter (Table 3).
Table 3.
Identified Correlations Between DCE Perfusion and Histopathological Parameters in Primary HNSCC (n = 11)
| Parameters | Ktrans, min−1 | Kep, min−1 | Ve, % | IAUC |
|---|---|---|---|---|
| Cell count, n | r = −0.25 (P = .47) | r = 0.0001 (P = .99) | r = −0.37 (P = .27) | r = −0.29 (P = .37) |
| Ki 67, % | r = −0.62 (P = .041) | r = −0.45 (P = .16) | r = −0.16 (P = .64) | r = −0.49 (P = .129) |
| Stained vessel area, % | r = 0.21 (P = .53) | r = 0.67 (P = .02) | r = −0.53 (P = .096) | r = 0.31 (P = .34) |
| Total vessel area, % | r = 0.07 (P = .83) | r = 0.62 (P = .04) | r = −0.65 (P = .03) | r = 0.29 (P = .38) |
| Mean vessel diameter, μm | r = −0.17 (P = .62) | r = 0.43 (P = .18) | r = −0.69 (P = .017) | r = 0.06 (P = .85) |
| Mean number of vessels, n | r = 0.24 (P = .49) | r = 0.23 (P = .49) | r = −0.048 (P = .88) | r = 0.32 (P = .32) |
Discussion
The present study documents significant correlations between different DCE MRI and histopathological parameters in HNSCC.
Previously, some studies also analyzed relationships between histological and perfusion parameters in several malignancies. For instance, Li et al. showed that the mean Ktrans and Kep values significantly correlated with microvessel density in breast cancer [4]. According to Oto et al. Kep positively correlated with the mean blood vessel count and mean vessel area fraction parameters calculated by CD31 staining in prostate cancer [13]. Additionally, the mean blood vessel count and mean vessel area fraction parameters showed a moderate negative correlation with Ve [13].
However, according to the literature several tumors had different associations between DCE and histopathological parameters. For example, Jia et al. found that Ktrans and Ve were both positively correlated with microvessel density in high grade glioma, but not in low grade tumors [14].
Our results confirmed these findings. We also found statistically significant positive correlations between Kep and vessel areas in our cases. Furthermore, Ve correlated inversely with mean vessel diameter. Moreover, these correlations were stronger in primary tumors.
As reported previously, DCE parameters correlated not only with vessel density, but also with other tumor markers in different malignancies. For instance, Aryal et al. reported that Ve significantly correlated with tumor cellularity in glioma [15]. Jansen et al. identified a significant correlation between Kep and expression level of vascular endothelial growth factor in cervical lymph node metastases from HNSCC [16]. The authors also found that KI 67 inversely correlated with Ktrans and Ve [16].
In the present study, we found no significant correlations between cell count or KI 67 and DCE MRI parameters. Only Ve tended to correlate with cell count.
However, in the cases with primary tumors Ktrans correlated significantly inversely with KI 67. This fact suggests different associations between DCE parameters and histopathology in primary versus recurrent tumors.
Similar relationships were reported previously in other malignancies. For instance, perfusions parameters correlated well with KI 67 in glioma, when the data from high and low grade tumors were combined [24]. However, no significant correlation was observed in high or low grade tumors, when they were analyzed separately [24].
The associations between DCE MRI and histopathological parameters identified in the present analysis may be helpful to differentiate HNSCC with low and high proliferation potential. Similarly, the use of DCE MRI can also distinguish tumors with high angiogenesis from lesions with low microvessel density. Therefore, DCE MRI parameters can be used as a noninvasive tool for tumor angiogenesis. In fact, according to Song et al., Ktrans and Ve values decreased statistically significantly after anti-angiogenic treatment in an orthotopic mouse model of human hepatocellular carcinoma [25]. Other authors confirmed these results [26]. This leads to the assumption that DCE MRI may predict response to anti-angiogenic therapy. Presumably, tumors with high microvessel density have a better response to anti-angiogenic drugs.
Clearly, further works with more cases are needed to confirm our results and to search for other relevant relationships between DCE and histopathology in different malignancies.
In conclusion, our analysis showed significant correlations between Kep, Ve and microvessel density in HNSCC. Ve also tended to correlate with cell count.
In primary tumors, the correlations between Kep, Ve and vessel parameters were stronger. Furthermore, in primary tumors, Ktrans correlated significantly with KI 67.
Footnotes
There was no conflict of extravascular extracellular leakage space interest
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