© 2015 Springer-Verlag Berlin Heidelberg Purpose: To investigate the differences between imaging features of spinal tuberculosis (TB) and metastatic cancer measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The presentation of TB on convention MRI may not show the typical TB signs, and they may be mis-diagnosed as malignant diseases. DCE-MRI may provide additional information to help making differential diagnosis. Materials and methods: DCE-MRI was performed in 24 TB and 22 metastatic cancer patients. The DCE kinetic pattern was determined as “wash-out”, “plateau” or “persistent enhancement”. The characteristic DCE parameters were calculated from the signal intensity time course. The two-compartmental pharmacokinetic model was used to obtain Ktrans, which is the parameter associated with the delivery of MR contrast agents into the lesion, and kep, which is the parameter associated with the distribution and clearance of contrast agents from the lesion. Results: Of the 24 TB, one case showed the wash-out kinetic pattern, 12 cases showed the plateau pattern, and 11 cases showed the persistent enhancement pattern. Of the 22 metastatic cancers, 12 cases showed wash-out, 7 cases showed plateau, and 3 cases showed persistent enhancement patterns. Compared to the metastatic cancer group, the TB group had a lower kep (0.27 ± 0.15 vs. 0.49 ± 0.23 min−1, P < 0.001). The ROC analysis showed that the area under the curve was 0.780 for kep. Conclusions: DCE-MRI may provide additional information for differentiation between spinal TB and metastasis, when their manifestations on conventional imaging were similar.

Purpose

To investigate the differences between imaging features of spinal tuberculosis (TB) and metastatic cancer measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The presentation of TB on convention MRI may not show the typical TB signs, and they may be mis-diagnosed as malignant diseases. DCE-MRI may provide additional information to help making differential diagnosis.

Materials and methods

DCE-MRI was performed in 24 TB and 22 metastatic cancer patients. The DCE kinetic pattern was determined as "wash-out", "plateau" or "persistent enhancement". The characteristic DCE parameters were calculated from the signal intensity time course. The two-compartmental pharmacokinetic model was used to obtain K (trans), which is the parameter associated with the delivery of MR contrast agents into the lesion, and k ep, which is the parameter associated with the distribution and clearance of contrast agents from the lesion.

Results

Of the 24 TB, one case showed the wash-out kinetic pattern, 12 cases showed the plateau pattern, and 11 cases showed the persistent enhancement pattern. Of the 22 metastatic cancers, 12 cases showed wash-out, 7 cases showed plateau, and 3 cases showed persistent enhancement patterns. Compared to the metastatic cancer group, the TB group had a lower k ep (0.27 ± 0.15 vs. 0.49 ± 0.23 min(-1), P < 0.001). The ROC analysis showed that the area under the curve was 0.780 for k ep.

Conclusions

DCE-MRI may provide additional information for differentiation between spinal TB and metastasis, when their manifestations on conventional imaging were similar.

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.

Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning methods. To accommodate the large variability in metastatic lesion sizes, we develop a Siamese deep neural network approach comprising three identical subnetworks for multi-resolution analysis and detection of spinal metastasis. At each location of interest, three image patches at three different resolutions are extracted and used as the input to the networks. To further reduce the false positives (FPs), we leverage the similarity between neighboring MRI slices, and adopt a weighted averaging strategy to aggregate the results obtained by the Siamese neural networks. The detection performance is evaluated on a set of 26 cases using a free-response receiver operating characteristic (FROC) analysis. The results show that the proposed approach correctly detects all the spinal metastatic lesions while producing only 0.40 FPs per case. At a true positive (TP) rate of 90%, the use of the aggregation reduces the FPs from 0.375 FPs per case to 0.207 FPs per case, a nearly 44.8% reduction. The results indicate that the proposed Siamese neural network method, combined with the aggregation strategy, provide a viable strategy for the automated detection of spinal metastasis in MRI images.

Purpose

To characterize the morphological and dynamic-contrast-enhanced (DCE) MRI features of chordoma and giant cell tumor (GCT) of bone occurring in the axial skeleton.

Materials and methods

A total of 13 patients with chordoma and 26 patients with GCT who received conventional T1, T2, and DCE-MRI on 3 Tesla MR scanners were retrospectively identified and analyzed. Two radiologists evaluated morphological features independently, including the lesion location, expansile bone changes, vertebral compression, presence of paraspinal soft tissue mass, fibrous septa, and the signal intensity on T1WI and T2WI. The inter-observer agreement was evaluated by kappa test. The DCE kinetics was measured to obtain the initial area under curve (IAUC) and the wash-out slope; also the two-compartmental pharmacokinetic model was applied to obtain K^trans and k_ep . The diagnostic accuracy was evaluated by CHAID decision tree and ROC analysis.

Results

Chordomas were more likely to show soft tissue mass than GCTs (13/13 = 100% versus 15/26 = 58%; P = 0.007), as well as fibrous septa (9/13 = 69% versus 0; P < 0.001). In decision tree analysis, presence of fibrous septa and lesion location yield 31/39 = 79% accuracy. The DCE-MRI pharmacokinetic parameters K^trans and k_ep of GCTs were significantly higher than those of chordomas, 0.13 ± 0.65 versus 0.06 ± 0.04 (1/min) for K^trans , 0.62 ± 0.22 versus 0.17 ± 0.12 (1/min) for k_ep , P < 0.001 for both. If using kep = 0.43/min as the cut-off value, it achieved 100% sensitivity and 92% specificity to differentiate chordoma from GCT, with an overall accuracy of 37/39 = 95%. The IAUC was highly correlated with K^trans (r = 0.94), and the slope was highly correlated with k_ep (r = 0.95).

Conclusion

Several morphological features were significantly different between chordoma and GCT, but their diagnostic performance was inferior to that of DCE-MRI.

Level of evidence

4 J. Magn. Reson. Imaging 2017;45:1068-1075.

Spinal myeloma and metastatic cancer cause similar symptoms and show similar imaging presentations, thus making them difficult to differentiate. In this study, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed to differentiate between 9 myelomas and 22 metastatic cancers that present as focal lesions in the spine. The characteristic DCE parameters, including the peak signal enhancement percentage (SE%), the steepest wash-in SE% during the ascending phase and the wash-out SE%, were calculated by normalizing to the precontrast signal intensity. The two-compartmental pharmacokinetic model was used to obtain K(trans) and kep. All nine myelomas showed the wash-out DCE pattern. Of the 22 metastatic cancers, 12 showed wash-out, 7 showed plateau, and 3 showed persistent enhancing patterns. The fraction of cases that showed the wash-out pattern was significantly higher in the myeloma group than the metastatic cancer group (9/9=100% vs. 12/22=55%, P=.03). Compared to the metastatic cancer group, the myeloma group had a higher peak SE% (226%±72% vs. 165%±60%, P=.044), a higher steepest wash-in SE% (169%±51% vs. 111%±41%, P=.01), a higher K(trans) (0.114±0.036 vs. 0.077±0.0281/min, P=.016) and a higher kep (0.88±0.26 vs. 0.49±0.23 1/min, P=.002). The receiver operating characteristic analysis to differentiate between these two groups showed that the area under the curve was 0.798 for K(trans), 0.864 for kep and 0.919 for combined K(trans) and kep. These results show that DCE-MRI may provide additional information for making differential diagnosis to aid in choosing the optimal subsequent procedures or treatments for spinal lesions.

Purpose

To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and ¹⁸F-FDG PET/CT in spinal tumors, and their role in differential diagnosis.

Methods

A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters K^trans and k_ep. On PET, the maximum standardized uptake value SUV_max was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters.

Results

The K^trans, k_ep and SUV_max were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between K^trans and SUV_max (r = - 0.20, P = 0.18), or between k_ep and SUV_max (r = - 0.16, P = 0.28). The k_ep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUV_max was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of k_ep and SUV_max was 0.79 and 0.78, respectively.

Conclusions

The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.

Objectives

To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.

Methods

In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.7 months (range, 28.7-152.1 months). The clinical information including age, gender, lesion location, multi-vertebral involvement, and surgical methods, were obtained. CT images acquired before the operation were retrieved for radiomics analysis. For each case, the tumor regions of interest (ROI) was manually outlined, and a total of 107 radiomics features were extracted. The features were selected via the sequential selection process by using the support vector machine (SVM), then used to construct classification models with Gaussian kernels. The differentiation between recurrence and non-recurrence groups was evaluated by ROC analysis, using 10-fold cross-validation.

Results

Of the 62 patients, 17 had recurrence with a recurrence rate of 27.4%. None of the clinical information was significantly different between the two groups. Patients receiving curettage had a higher recurrence rate (6/16 = 37.5%) compared to patients receiving TES (6/26 = 23.1%) or intralesional spondylectomy (5/20 = 25%). The final radiomics model was built using 10 selected features, which achieved an accuracy of 89% with AUC of 0.78.

Conclusions

The radiomics model developed based on pre-operative CT can achieve a high accuracy to predict the recurrence of spinal GCTB. Patients who have a high risk of early recurrence should be treated more aggressively to minimize recurrence.

PURPOSE:To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers using radiomics and deep learning, compared to traditional hot-spot ROI analysis. METHODS:In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE) sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung; 31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics. For each case, the 3D tumor mask was generated by using the normalized-cut algorithm. Radiomics analysis was performed to extract histogram and texture features from three DCE parametric maps. Deep learning was performed using these maps as inputs into a conventional convolutional neural network (CNN), as well as using all 12 sets of DCE images into a convolutional long short term memory (CLSTM) network. RESULTS:For hot-spot ROI analysis, mean wash-out slope was 0.25 ± 10% for lung metastases and -9.8 ± 12.9% for other tumors. CHAID classification using a wash-out slope of -6.6% followed by wash-in enhancement ratio of 98% achieved a diagnostic accuracy of 0.79. Radiomics analysis using features representing tumor heterogeneity only reached the highest accuracy of 0.71. Classification using CNN achieved a mean accuracy of 0.71 ± 0.043, whereas a CLSTM improved accuracy to 0.81 ± 0.034. CONCLUSIONS:DCE-MRI machine-learning analysis methods have potential to predict lung cancer metastases in the spine, which may be used to guide subsequent workup for confirmed diagnosis.

Purpose

This project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.

Material and methods

A retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified. VEGF and p53 expression and clinical and general imaging information were collected. Multivariate Cox regression models were used to verify the prognostic factors. The radiomics features were extracted from the regions of interest (ROIs) in preoperative CT, and then important features were selected by the SVM to build classification models, evaluated by 10-fold crossvalidation. The clinical variables were processed using the same method to build a conventional model for comparison.

Results

The immunohistochemistry of 80 patients was obtained: 49 with high-VEGF and 31 with low-VEGF, 68 with wild-type p53, and 12 with mutant p53. p53 and VEGF were independent prognostic factors affecting PFS found in multivariate Cox regression analysis. For VEGF, the Spinal Instability Neoplastic Score (SINS) was greater in the high than low groups, p < 0.001. For p53, SINS (p = 0.030) and Enneking stage (p = 0.017) were higher in mutant than wild-type groups. The VEGF radiomics model built using 3 features achieved an area under the curve (AUC) of 0.88, and the p53 radiomics model built using 4 features had an AUC of 0.79. The conventional model built using SINS, and the Enneking stage had a slightly lower AUC of 0.81 for VEGF and 0.72 for p53.

Conclusion

p53 and VEGF are associated with prognosis in patients with spinal GCTB, and the radiomics analysis based on preoperative CT provides a feasible method for the evaluation of these two biomarkers, which may aid in choosing better management strategies.