Introduction

MRI is often recommended for patients presenting with pain in the spine, who are suspected to have lesions compressing the spinal cord. The most likely malignancy is metastatic cancer and primary bone cancer. Other borderline malignant (or aggressive benign) tumors and benign lesions are also found in the spine, and each lesion needs to be correctly diagnosed to decide the most appropriate treatment plan. Although MRI can provide detailed morphological information about the deformation of the bone and the presence of soft tissue lesion, there are no specific imaging features that can be used to diagnose different types of lesions. Several studies have shown that the vascular parameters measured by dynamic-contrast-enhanced MRI can provide additional information to help differentiation of lesions in the spine, e.g. primary bone cancers such as myeloma, lymphoma, chordoma [1-4]; benign lesions such as tuberculosis, giant cell tumor of the bone [4-5]; metastatic cancers of different origins [6-8]. PET/CT provides another important imaging modality to measure metabolism for diagnosis of lesions, or for staging of cancer that has been confirmed for therapy planning. Glucose metabolism measured by 18F-FDG and vascular parameters measured by DCE-MRI may provide complementary information representing different aspects of the lesion. Their associations in the lung cancer [9] and the head and neck cancer [10] have been studied before. The purpose of this study is to investigate the association between quantitative imaging parameters measured by DCE-MRI and 18F-FDG PET/CT.

Methods

In a retrospective review of patients receiving spinal MRI that included a DCE sequence, and 18F-FDG PET/CT, a total of 49 patients were identified. They received PET/CT after MRI, either for diagnosis or staging. The histological types are listed in Table 1. MR scans were performed on a 3T Siemens system. After the abnormal region was identified, DCE-MRI was performed using the 3D VIBE sequence, with TR=4.1ms, TE=1.5 ms, flip angle=10°, matrix=256×192 and FOV=250×250 mm. Approximately 30 slices with 3-mm thickness were prescribed to cover the abnormal vertebrae. The contrast agents, 0.1 [mmol/kg] Gd-DTPA, was injected after one set of pre-contrast images was acquired. A total of 12 frames were acquired, and the total DCE-MRI time period was about 160 seconds. For each case, an ROI was manually placed on the area showing the strongest enhancement to measure signal intensity time course. Three heuristic parameters were calculated: the steepest wash-in signal enhancement ratio [(S2-S1)/S0]; The maximum signal enhancement ratio [(Smax-S0)/S0]; and the wash-out slope [(Speak-Slast)/Speak], or if no peak [(S67s-Slast)/S67s]. The DCE time course was also analyzed with a 2-compartmental pharmacokinetic model to obtain Ktrans (1/min) and kep (1/min), following the previously reported method [3]. PET/CT was done using the Siemens Biograph 64 PET/CT scanner, covering from head to thigh. The subject received an intravenous bolus of approximately 370 MBq (10 mCi) 18F-FDG. Attenuation correction CT was performed using a 64-slice multidetector helical scanner prior to PET acquisition. The CT parameters were kVp=140, mA=100, and reconstructed slice thickness of 3 mm. The analysis on PET was done according to the lesions analyzed on MRI. The radiologist outlined the lesion ROI on the FDG uptake map, and the SUVmax was measured. The results in three groups: malignant, borderline malignant/aggressive benign, and benign were analyzed and compared.

Results

The mean and standard deviation of analyzed parameters in three groups, as well as in each histological subtype, are summarized in Table 1. The highest number of histological subtype is 19 metastatic cancer and 8 plasmacytoma. The MRI and PET images of one renal metastasis and one plasmacytoma are shown in Figures 1 and 2. The images from two benign lesions, Schwannoma and Hemangioma, are shown in Figure 3. The DCE pharmacokinetic parameters, Ktrans and kep, were not significantly correlated with SUVmax, in either the entire group of 49 cases, or in each of the three malignant/benign groups. The parameters between the two largest subtypes: 19 metastatic cancer and 8 plasmacytoma, were compared. The mean kep was 0.78±0.17 in plasmacytoma, significantly higher than 0.61±0.18 in mets (p=0.019). In contrast, SUVmax was 5.58±2.16 in plasmacytoma, significantly lower than 9.37±4.26 in mets (p=0.025). The sensitivity and specificity to differentiate between them were 87.5% and 73.7% based on kep, and 78.9% and 62.5% based on SUVmax. To evaluate whether different DCE-MRI analysis methods yielded consistent results, we performed correlation analysis between heuristic and pharmacokinetic parameters. It was found that kep was highly correlated with wash-out slope, and Ktrans was highly correlated with max SE ratio and steepest wash-in SE ratio, as shown in Figure 4. Therefore, both heuristic and pharmacokinetic analysis methods can be used.

Discussion

The results showed that vascular parameters measured by DCE-MRI were not correlated with metabolic information measured by 18F-FDG PET, suggesting that they reveal different aspects of lesions and may provide complementary information for lesion diagnosis, staging, or prediction of aggressiveness or prognosis. The variation in SUVmax was high, and it might not provide reliable information for diagnosis of different spinal lesions. The differentiation accuracy between metastases and plasmacytoma was better based on kep than SUVmax. The main clinical role of FDG PET is for detection and staging of metastases in the whole body. In addition, glucose metabolism is related to tumor aggressiveness, and that may provide important information for prediction of prognosis. For example, different plasmacytomas may have different risks progressing to multiple myeloma, and it will be interesting to use combined DCE-MRI and PET parameters measured in this study to predict their conversion risk by monitoring the long-term prognosis of these patients.

Acknowledgements

This study is supported in part by NIH R01 CA127927, the National Natural Science Foundation of China (81701648, 81471634, 81871326).

References

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