This study involved 40 patients [18 female (45%), and 22 male (55%)]. The patients’ age ranges from 5 to 78 years with median age 61.5 years.

There were 13 types of primary tumors. The frequency of different primary tumors was 8 breast carcinoma (20%) (Fig. 1), 8 lung cancer (20%) (Figs. 2, 3), 6 prostatic carcinoma (15%), 5 colorectal carcinoma (12.5%) (Fig. 4), 1 neuroblastoma (2.5%), 2 thyroid carcinoma (5%), 1 carcinoma of the tongue (2.5%), 2 ovarian carcinoma (5%), 3 hepatocellular carcinoma (7.5%), 1 pancreatic carcinoma (2.5%), 1 nasopharyngeal carcinoma (2.5%), 1 endometrial carcinoma (2.5%) and 1 carcinoma of the urinary bladder (2.5%).

Fig. 1
figure 1

AH MRI and PET CT images of a 64-year-old female patient with a history of breast cancer A T1 FSE, B T2 FSE, C T2WO Dixon, D T2 FO Dixon E T1 post-contrast WO Dixon, F T1 post-contrast OP Dixon, G IP/OP ratio MR images and H FDG PET CT show multiple lumbar and sacral vertebral metastasis (white arrows) with IP/OP ratio (11%)

Fig. 2
figure 2

AH MRI and PET CT images of a 56-year-old male patient with a history of lung cancer A T1 FSE B T2 FSE C T2 WO Dixon, D T1 post-contrast WO Dixon, E T1 FO Dixon F T1 post-contrast OP Dixon, G IP/OP ratio MR images and H FDG PET CT show multiple dorsal vertebral metastasis with IP/OP ratio 3.5%

Fig. 3
figure 3

AH MRI and PET CT images of a 69-year-old male patient with a history of lung cancer A T1FSE, B T2 FSE C T2 WO Dixon, D T1 post-contrast WO Dixon, E T1FO Dixon F T1 post-contrast OP Dixon, G IP/OP ratio MR images and H FDG PET CT show multiple lumbar vertebral metastasis (white arrows) with IP/OP ratios of two lesions (6% and 5.5%)

Fig. 4
figure 4

AH MRI and PET CT images of a 66-year-old male patient with a history of colon cancer A T1 FSE, B T2 FSE C T2 WO Dixon, D T2 FO Dixon E T1 post-contrast WO Dixon, F T1 post-contrast OP Dixon, G IP/OP ratio MR images and H FDG PET CT show multiple dorsal vertebral metastasis (white arrows) with IP/OP ratio (9%)

By using 18F-FDG PET scan as a gold standard test, 21 patients (52.5%) showed vertebral metastasis and 19 patients (47.5%) showed no vertebral metastasis.

In this study, 182 vertebral lesions were detected by MRI and by using 18F-FDG PET scan as a gold standard test, 161 lesions from 182 proved to be malignant (metastatic) and 21 lesions proved to be benign. There was an excellent agreement (100%) between the two readers.

Table 1 shows the diagnostic performance of T2 Dixon and T1 post-contrast Dixon images in differentiation between benign and metastatic vertebral lesion. It shows an outstanding diagnostic performance of T1 post-contrast Dixon (WO, FO and OP) and excellent diagnostic performance of T2 Dixon (WO and OP).

Table 1 Diagnostic performance of the T1 and T2 Dixon modalities

Table 2 shows the clinical utility of the different Dixon modalities in case finding and screening.

Table 2 Clinical utility index (CUI) of the 4 modalities

According to the in-phase/opposed-phase (IP/OP) ratio, there was a statically significant difference between metastatic and non-metastatic benign lesions (P < 0.001) with a significant decrease in the in-phase/opposed-phase ratio in the metastatic lesion (ranges from 2.5 to 15%) compared with high ratio non-metastatic benign lesions (ranges from 28 to 76%) with a cut-point ratio of ≤ 15% which is a perfect discriminator of malignant lesions (Fig. 5).

Fig. 5
figure 5

ROC curve for in-phase/opposed-phase ratio to discriminate malignant from benign lesion

Discussion

MRI detection of metastatic vertebral lesions depends on the contrast difference between marrow-replaced metastatic lesions and normal fatty marrow [21,22,23]. T1-weighted images and fat suppression T2-weighted images are used widely for the diagnosis of vertebral metastasis [24, 25].

In this study, the sensitivity and specificity of T2 Dixon (WO) and (OP) were 78.3% and 100%, respectively. There were 35 false-negative vertebral lesions diagnosed by T2 Dixon (WO) and (OP). These lesions were either sclerotic vertebral metastasis that displayed low signal in all T2 Dixon sequences, small-size vertebral metastatic (less than 1cm in size) or post-treatment lesions in a patient who previously received neoadjuvant therapy [2]. These results were in agreement with the results of Hahn and his colleagues; they concluded that the sensitivity and specificity of T2 Dixon (WO) were 79.4% and 98.8%, respectively. They also reported that it was difficult to differentiate between normal bone marrow and adjacent osteosclerotic vertebral metastatic lesion as both lesions exhibited low signal on T2 Dixon images [2].

In the current study, the sensitivity and specificity of T1 and T2 Dixon (FO) images were the same 89.4% and 100%, respectively. Seventeen false-negative lesions were diagnosed by Dixon fat-only images. These false-negative lesions were either small vertebral metastases seen at the corner of the vertebral bodies and faulty diagnosed as Schmorl’s degenerative nodules or previously treated patients with neoadjuvant therapy. These results were matched with the results of Maeder and his colleagues they concluded that previous neoadjuvant therapy may induce the appearance of fat inside the metastatic vertebral lesions and gave false-negative results [3]. These results were also matched with the results of Zhadanov and his colleagues who reported that the ratio of T1 Dixon (FO) images for the detection of metastatic vertebral lesion was significantly higher than that of conventional T1-weighted images [26].

In the current study, the sensitivity and specificity of T1 post-contrast Dixon (OP) were 83.1% and 100%, respectively. Twenty-seven false-negative vertebral lesions were diagnosed by T1 post-contrast Dixon (OP). These lesions were either small size vertebral metastasis (less than 1cm in size) or post-treatment vertebral lesions in previously treated patients with neoadjuvant therapy. These results were in agreement with the results of Erly and his colleagues they reported that vertebral opposed-phase imaging was sensitive and specific for differentiation between benign and malignant vertebral compression fractures.

In the current study, the sensitivity and specificity of T1 post-contrast Dixon (WO) images were 92.1% and 100%, respectively. Twelve false-negative vertebral lesions were diagnosed by T1 post-contrast (WO) Dixon. These lesions were either small size vertebral metastatic lesions less (than 1cm in size) or post-treatment vertebral lesions in previously treated patients with neoadjuvant therapy.

In this study, the sensitivity, accuracy and diagnostic performance of T1 post-contrast Dixon for detection of vertebral metastasis were higher than T2 Dixon images. As post-contrast techniques tend to increase the detection of small and osteosclerotic vertebral metastasis that cannot be detected by non-contrast T2 Dixon images. These results were in agreement with the results of Zhadanov and his colleagues they reported that T1 post-contrast Dixon images were significantly valuable for the detection of bony lesions more than non-contrast and conventional MRI techniques [26].

This study concluded that there was an outstanding diagnostic performance of T1 post-contrast Dixon (WO, FO and OP) and excellent diagnostic performances of T2 Dixon (WO and OP) in differentiation between benign and metastatic vertebral lesions. These results were in agreement with the results of Hahn and his colleagues they demonstrated excellent diagnostic performance of T1 and T2 Dixon in the detection of vertebral metastasis [2].

As regards the clinical utility of different Dixon modalities in the finding of vertebral metastasis, this study concluded that there were excellent positive clinical utility of T1 post-contrast Dixon (WO, FO and OP) and good positive clinical utility of T2 Dixon (WO and OP).

Regarding the clinical utility of different Dixon modalities in the screening of vertebral metastasis, the current study concluded that there were fair negative clinical utility of T1 post-contrast Dixon (WO and FO) and poor negative clinical utility of T2 Dixon (WO and OP).

Dixon chemical shift imaging added potential values in challenging examinations with common pitfalls that can be encountered when dealing with spine MR imaging. Dixon could be used to identify typical vertebral hemangiomas as it usually shows a significantly decreased signal on the OP images compared with the increased signal of malignant vertebral lesions. Dixon showed promising results in differentiation between malignant and osteoporotic vertebral fractures, as in osteoporotic vertebral fractures fatty marrow remains, while in malignant vertebral fracture fatty marrow is replaced by malignant tissues. Dixon can differentiate between vertebral metastases and degenerative bone marrow changes [27].

Many studies reported that the comparison of metastatic lesion signal intensity between in-phase and opposed-phase Dixon images was valuable for the diagnosis of vertebral metastasis [13, 23, 28, 29].

In this study, as regard the in-phase/opposed-phase (IP/OP) ratio there was a statically significant difference between metastatic and non-metastatic benign lesions ratios (P < 0.001) with a significant decrease in ratio in metastatic lesion compared with a high ratio of non-metastatic benign lesions. These results were in agreement with the results of the study of Donners and his colleagues who reported that the ratio of fat fraction was significantly lower in malignant vertebral fractures compared to osteoporotic fractures [30].

By using the ROC curve analysis, the current study concluded that 15 % was the best IP/OP ratio cut-point value for differentiation between malignant and benign lesions with 100 % sensitivity, 100% specificity and 100% diagnostic accuracy. In contrast, the study of Pozzi and his colleagues concluded that the use of apparent diffusion coefficient (ADC) cutoff value showed a significant overlap between benign and malignant lesions with 81.3% sensitivity, 55% specificity and 76% diagnostic accuracy [31].

In the current study, the IP/OP ratio cut-point value was slightly higher than that calculated by the study of Donners and his colleagues as they concluded that the best cutoff value for differentiation between malignant and benign lesion was 11.5 % and this may be due to the use of different formula for calculation of the ratio between the two studies [30].

The ongoing introduction of artificial intelligence allows new imaging tools and applications. The study of Gitto and colleagues reported that a support vector machine (SVM) model that depends on T2-weighted images and ADC maps radiomic features can differentiate between benign and malignant vertebral lesions with 76% accuracy [32].

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