Morphological and dynamic contrast enhanced MR imaging features for the differentiation of chordoma and giant cell tumors in the Axial Skeleton

Low-dose GBCA administration for brain tumour dynamic contrast enhanced MRI: a feasibility study

A key limitation of current dynamic contrast enhanced (DCE) MRI techniques is the requirement for full-dose gadolinium-based contrast agent (GBCA) administration. The purpose of this feasibility study was to develop and assess a new low GBCA dose protocol for deriving high-spatial resolution kinetic parameters from brain DCE-MRI. Nineteen patients with intracranial skull base tumours were prospectively imaged at 1.5 T using a single-injection, fixed-volume low GBCA dose, dual temporal resolution interleaved DCE-MRI acquisition. The accuracy of kinetic parameters (ve, Ktrans, vp) derived using this new low GBCA dose technique was evaluated through both Monte-Carlo simulations (mean percent deviation, PD, of measured from true values) and an in vivo study incorporating comparison with a conventional full-dose GBCA protocol and correlation with histopathological data. The mean PD of data from the interleaved high-temporal-high-spatial resolution approach outperformed use of high-spatial, low temporal resolution datasets alone (p < 0.0001, t-test). Kinetic parameters derived using the low-dose interleaved protocol correlated significantly with parameters derived from a full-dose acquisition (p < 0.001) and demonstrated a significant association with tissue markers of microvessel density (p < 0.05). Our results suggest accurate high-spatial resolution kinetic parameter mapping is feasible with significantly reduced GBCA dose.

Characterization of denosumab treatment response in giant cell tumors of bone with dynamic contrast-enhanced MRI

RATIONALE AND OBJECTIVES

Denosumab is a monoclonal antibody used neo-adjuvantly in giant cell tumor of bone (GCTB) to facilitate surgery, or long term for axial tumors where surgery comes with high morbidity. Time intervals for treatment effects to occur are unclear and monitoring tools are limited, complicating optimal drug dose titration. We assessed changes in time intensity curve (TIC) - derived perfusion features on DCE-MRI in GCTB during denosumab treatment and evaluated the duration of treatment effects on tumor perfusion.

MATERIALS AND METHODS

Patients with GCTB who underwent dynamic contrast enhanced (DCE) MRI before (t = 0) and after 3 (t = 3), 6 (t = 6) or 12 (t = 12) months of denosumab treatment were retrospectively included in a single center. Regions of interest were placed on tumor compartments with visually most intense enhancement and TICs were created. Time-to-enhancement (TTE), wash-in rate (WIR), maximal relative enhancement (MRE), and area-under-the-curve (AUC) were calculated. Differences in perfusion features were calculated with the Wilcoxon signed-rank test.

RESULTS

In all 24 patients decreased perfusion on DCE-MRI after start of denosumab treatment was seen. TTE increased between t = 0 and t = 3 (p < 0.001). WIR, MRE and AUC decreased between t = 0 and t = 3 (p < 0.001, p = 0.01 and p = 0.02, respectively). No significant differences in features were found between t = 3 and t = 6 or t = 6 and t = 12. No significant perfusion differences in primary versus recurrent, or axial versus appendicular tumors, were found.

CONCLUSION

MRI perfusion significantly changed in GCTB within 3 months of denosumab treatment compared to baseline. No further significant change occurred between 3 and 6, and 6 and 12 months of treatment. These findings suggest that evaluation of treatment response and subsequent consideration of maintenance with lower doses of denosumab, may already be indicated after 3 months. In cases where long term denosumab is the preferred therapy, monitoring change in tumor characteristics on DCE-MRI may aid optimal drug dose titration, minimizing side effects.

Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases

Objective

The objective of this study was to investigate the use of contrast-enhanced magnetic resonance imaging (CE-MRI) combined with radiomics and deep learning technology for the identification of spinal metastases and primary malignant spinal bone tumor.

Methods

The region growing algorithm was utilized to segment the lesions, and two parameters were defined based on the region of interest (ROI). Deep learning algorithms were employed: improved U-Net, which utilized CE-MRI parameter maps as input, and used 10 layers of CE images as input. Inception-ResNet model was used to extract relevant features for disease identification and construct a diagnosis classifier.

Results

The diagnostic accuracy of radiomics was 0.74, while the average diagnostic accuracy of improved U-Net was 0.98, respectively. the PA of our model is as high as 98.001%. The findings indicate that CE-MRI based radiomics and deep learning have the potential to assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor.

Conclusion

CE-MRI combined with radiomics and deep learning technology can potentially assist in the differential diagnosis of spinal metastases and primary malignant spinal bone tumor, providing a promising approach for clinical diagnosis.

Bone marrow MR perfusion imaging and potential for tumor evaluation

The physiology of bone perfusion is reviewed outlining how it can be measured with dynamic contrast-enhanced MRI as well as intravoxel incoherent imaging. Evaluation of bone perfusion provides a potential means of assessing tumor activity and treatment response beyond that possible with standard MR imaging.

A Computed Tomography Radiomics Nomogram in Differentiating Chordoma From Giant Cell Tumor in the Axial Skeleton

OBJECTIVE

The aim of the study is to develop and validate a computed tomography (CT) radiomics nomogram for preoperatively differentiating chordoma from giant cell tumor (GCT) in the axial skeleton.

METHODS

Seventy-three chordomas and 38 GCTs in axial skeleton were retrospectively included and were divided into a training cohort (n = 63) and a test cohort (n = 48). The radiomics features were extracted from CT images. A radiomics signature was developed by using the least absolute shrinkage and selection operator model, and a radiomics score (Rad-score) was acquired. By combining the Rad-score with independent clinical risk factors using multivariate logistic regression model, a radiomics nomogram was established. Calibration and receiver operator characteristic curves were used to assess the performance of the nomogram.

RESULTS

Five features were selected to construct the radiomics signature. The radiomics signature showed favorable discrimination in the training cohort (area under the curve [AUC], 0.860; 95% confidence interval [CI], 0.760-0.960) and the test cohort (AUC, 0.830; 95% CI, 0.710-0.950). Age and location were the independent clinical factors. The radiomics nomogram combining the Rad-score with independent clinical factors showed good discrimination capability in the training cohort (AUC, 0.930; 95% CI, 0.880-0.990) and the test cohort (AUC, 0.980; 95% CI, 0.940-1.000) and outperformed the radiomics signature (z = 2.768, P = 0.006) in the test cohort.

CONCLUSIONS

The CT radiomics nomogram shows good predictive efficacy in differentiating chordoma from GCT in the axial skeleton, which might facilitate clinical decision making.

MRI enhancement patterns in 28 cases of clival chordomas

Clival chordomas are classically thought of as locally aggressive tumors of the skull base and differentiate themselves from their benign counterparts by demonstrating moderate to marked contrast enhancement, reported as 95-100% in prior studies. The purpose of this review was to evaluate the imaging characteristics of lesions from a single institution classified as clival chordomas with an emphasis of highlighting lesions that do not follow the prevalent current description for chordoma. We searched our institutional databases for all patients with pathologically proven clival chordomas from 1997 to 2017 who had pre-operative imaging available. The images were evaluated for degree of contrast enhancement, MRI signal characteristics, osseous involvement, location, aggressiveness of appearance, and presence of calcifications. 28 cases were identified that had preoperative imaging available for review. Over half of the patients demonstrated either no/minimal (11/28, 39%) or mild enhancement (7/28, 25%). The remaining cases demonstrated moderate (4/28, 14%) and marked enhancement (6/28, 21%). The 4 lesions measuring less than 20 mm all had mild to minimal/no enhancement and lacked aggressive features on CT. Our experience finds that over half (64%) of clival chordomas will demonstrate mild or no enhancement at all. These findings suggest that the lack of MRI contrast enhancement should not be synonymous with a benign clival mass.

Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer

The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.

Current progress and future trends in imaging of musculoskeletal bone tumours

Plain radiographs and MRI remains the gold standard imaging modality for bone tumour and tumour like lesions. Several imaging techniques have been developed to be used in conjunction, but doubt remains over how much additional diagnostic information they provide over and above routine MRI bone tumour sequences. Given the plethora of new modalities, this review aims to highlight some of them and how they may help in the diagnostic assessment of musculoskeletal bone tumours.

Differentiating Between Multiple Myeloma and Metastasis Subtypes of Lumbar Vertebra Lesions Using Machine Learning-Based Radiomics

Objective

To determine whether machine learning based on conventional magnetic resonance imaging (MRI) sequences have the potential for the differential diagnosis of multiple myeloma (MM), and different tumor metastasis lesions of the lumbar vertebra.

Methods

We retrospectively enrolled 107 patients newly diagnosed with MM and different metastasis of the lumbar vertebra. In total 60 MM lesions and 118 metastasis lesions were selected for training classifiers (70%) and subsequent validation (30%). Following segmentation, 282 texture features were extracted from both T1WI and T2WI images. Following regression analysis using the least absolute shrinkage and selection operator (LASSO) algorithm, the following machine learning models were selected: Support‐Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Networks (ANN), and Naïve Bayes (NB) using 10-fold cross validation, and the performances were evaluated using a confusion matrix. Matthews correlation coefficient (MCC), sensitivity, specificity, and accuracy of the models were also calculated.

Results

To differentiate MM and metastasis, 13 features in the T1WI images and 9 features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.605) with accuracy, sensitivity, and specificity of 0.815, 0.879, and 0.790, respectively, in the validation cohort. To differentiate MM and metastasis subtypes, eight features in the T1WI images and seven features in the T2WI images were obtained. Among the 10 classifiers, the ANN classifier from the T2WI images achieved the best performance (MCC = 0.560, 0.412, 0.449), respectively, with accuracy = 0.648; sensitivity 0.714, 0.821, 0.897 and specificity 0.775, 0.600, 0.640 for the MM, lung, and other metastases, respectively, in the validation cohort.

Conclusions

Machine learning–based classifiers showed a satisfactory performance in differentiating MM lesions from those of tumor metastasis. While their value for distinguishing myeloma from different metastasis subtypes was moderate.

Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT

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.

Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI

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.

MRI biomarkers in osseous tumors

Although radiography continues to play a critical role in osseous tumor assessment, there have been remarkable advances in cross-sectional imaging. MRI has taken a lead in this assessment due to high tissue contrast and spatial resolution, which are well suited for bone lesion assessment. More recently, although somewhat lagging other organ systems, quantitative parameters have shown promising potential as biomarkers for osseous tumors. Among these sequences are chemical shift imaging (CSI), apparent diffusion coefficient (ADC), and intravoxel incoherent motion (IVIM) from diffusion-weighted imaging (DWI), quantitative dynamic contrast enhanced (DCE)-MRI, and magnetic resonance spectroscopy (MRS). In this article, we review the background and recent roles of these quantitative MRI biomarkers for osseous tumors. Level of Evidence: 3 Technical Efficacy Stage: 3 J. MAGN. RESON. IMAGING 2019. J. Magn. Reson. Imaging 2019;50:702-718.

Prognostic Implications of Gadolinium Enhancement of Skull Base Chordomas

BACKGROUND AND PURPOSE

Skull base chordomas often demonstrate variable MR imaging characteristics, and there has been limited prior research investigating the potential clinical relevance of this variability. The purpose of this retrospective study was to assess the prognostic implications of signal intensity on standard imaging techniques for the biologic behavior of skull base chordomas.

MATERIALS AND METHODS

Medical records were retrospectively reviewed for 22 patients with pathologically confirmed skull base chordomas. Clinical data were recorded, including the degree of surgical resection, the presence or absence of radiation therapy, and time to progression/recurrence of the tumor or time without progression/recurrence of the tumor following initial treatment. Pretreatment imaging was reviewed for the presence or absence of enhancement and the T2 signal characteristics. Tumor-to-brain stem signal intensity ratios on T2, precontrast T1, and postcontrast T1 spin-echo sequences were also calculated. Statistical analysis was then performed to assess correlations between imaging characteristics and tumor progression/recurrence.

RESULTS

Progression/recurrence of skull base chordomas was seen following surgical resection in 11 of 14 (78.6%) patients with enhancing tumors and in zero of 8 patients with nonenhancing tumors. There was a statistically significant correlation between skull base chordoma enhancement and subsequent tumor progression/recurrence (P < .001), which remained significant after controlling for differences in treatment strategy (P < .001). There was also a correlation between postcontrast T1 signal intensity (as measured by postcontrast T1 tumor-to-brain stem signal intensity ratios) and recurrence/progression (P = .02). While T2 signal intensity was higher in patients without tumor progression (median tumor-to-brain stem signal intensity ratios on T2 = 2.27) than in those with progression (median tumor-to-brain stem signal intensity ratios on T2 = 1.78), this association was not significant (P = .12).

CONCLUSIONS

Enhancement of skull base chordomas is a risk factor for tumor progression/recurrence following surgical resection.

State-of-the-Art Imaging in Human Chordoma of the Skull Base

Purpose of Review

Chordoma are rare tumours of the axial skeleton which occur most often at the base of the skull and in the sacrum. Although chordoma are generally slow-growing lesions, the recurrence rate is high and the location makes it often difficult to treat. Both computed tomography (CT) and magnetic resonance imaging (MRI) are crucial in the initial diagnosis, treatment planning and post-treatment follow-up.

Recent Findings

Basic MRI and CT characteristics of chordoma were described in the late 1980s and early 1990s. Since then, imaging techniques have evolved with increased resolution and new molecular imaging tools are rapidly evolving. New imaging tools have been developed not only to study anatomy, but also physiologic changes and characterization of tissue and assessment of tumour biology. Recent studies show the uptake of multiple PET tracers in chordoma, which may become an important aspect in the diagnosis, follow-up and personalized therapy.

Summary

This review gives an overview of skull base chordoma histopathology, classic imaging characteristics, radiomics and state-of-the-art imaging techniques that are now emerging in diagnosis, treatment planning and disease monitoring of skull base chordoma.

Multifocal metastatic chordoma to the soft tissues of the fingertips: a case report including sonographic features and a review of the literature

Chordoma is a rare, locally aggressive tumor which commonly metastasizes, most often to the lung, liver, and spine. In this case report, a 59-year-old male with history of sacral chordoma and pulmonary metastases presented to the emergency department with swelling and discoloration of multiple left fingertips. The initial radiographs led to a presumptive diagnosis of gout, which did not respond to medical therapy. An ultrasound demonstrated multiple solid masses with vascular hyperechoic septations which were subsequently biopsied and proven to be metastatic chordoma. Metastatic disease to the hand is a well documented but rare manifestation of many malignancies. The clinical presentation and radiographic features of multifocal hand metastases may mimic entities such as systemic deposition and granulomatous diseases. To the best of our knowledge, this is the first case report of soft tissue chordoma metastases to the fingertips as well as the first reported sonographic description of chordoma metastases.

T1-Weighted Dynamic Contrast-Enhanced MR Perfusion Imaging Characterizes Tumor Response to Radiation Therapy in Chordoma

BACKGROUND AND PURPOSE

Chordomas notoriously demonstrate a paucity of changes following radiation therapy on conventional MR imaging. We hypothesized that dynamic contrast-enhanced MR perfusion imaging parameters of chordomas would change significantly following radiation therapy.

MATERIALS AND METHODS

Eleven patients with pathology-proved chordoma who completed dynamic contrast-enhanced MR perfusion imaging pre- and postradiation therapy were enrolled. Quantitative tumor measurements were obtained by 2 attending neuroradiologists. ROIs were used to calculate vascular permeability and plasma volume and generate dynamic contrast-enhancement curves. Quantitative analysis was performed to determine mean and maximum plasma volume and vascular permeability values, while semiquantitative analysis on averaged concentration curves was used to determine the area under the curve. A Mann-Whitney U test at a significance level of P < .05 was used to assess differences of the above parameters between pre- and postradiation therapy.

RESULTS

Plasma volume mean (pretreatment mean = 0.82; posttreatment mean = 0.42), plasma volume maximum (pretreatment mean = 3.56; posttreatment mean = 2.27), and vascular permeability mean (pretreatment mean = 0.046; posttreatment mean = 0.028) in the ROIs significantly decreased after radiation therapy (P < .05); this change thereby demonstrated the potential for assessing tumor response. Area under the curve values also demonstrated significant differences (P < .05).

CONCLUSIONS

Plasma volume and vascular permeability decreased after radiation therapy, suggesting that these dynamic contrast-enhanced MR perfusion parameters may be useful for monitoring chordoma growth and response to radiation therapy. Additionally, the characteristic dynamic MR signal intensity-time curve of chordoma may provide a radiographic means of distinguishing chordoma from other spinal lesions.