Application of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted magnetic resonance imaging in the differential diagnosis of metastases and myeloma in the spine-Univariate and multivariate analysis of related parameters

Radiomics model based on MRI to differentiate spinal multiple myeloma from metastases: A two-center study

Purpose

Spinal multiple myeloma (MM) and metastases are two common cancer types with similar imaging characteristics, for which differential diagnosis is needed to ensure precision therapy. The aim of this study is to establish radiomics models for effective differentiation between them.

Methods

Enrolled in this study were 263 patients from two medical institutions, including 127 with spinal MM and 136 with spinal metastases. Of them, 210 patients from institution I were used as the internal training cohort and 53 patients from Institution II were used as the external validation cohort. Contrast-enhanced T1-weighted imaging (CET1) and T2-weighted imaging (T2WI) sequences were collected and reviewed. Based on the 1037 radiomics features extracted from both CET1 and T2WI images, Logistic Regression (LR), AdaBoost (AB), Support Vector Machines (SVM), Random Forest (RF), and multiple kernel learning based SVM (MKL-SVM) were constructed. Hyper-parameters were tuned by five-fold cross-validation. The diagnostic efficiency among different radiomics models was compared by accuracy (ACC), sensitivity (SEN), specificity (SPE), area under the ROC curve (AUC), YI, positive predictive value (PPV), negative predictive value (NPY), and F1-score.

Results

Based on single-sequence, the RF model outperformed all other models. All models based on T2WI images performed better than those based on CET1. The efficiency of all models was boosted by incorporating CET1 and T2WI sequences, and the MKL-SVM model achieved the best performance with ACC, AUC, and F1-score of 0.862, 0.870, and 0.874, respectively.

Conclusions

The radiomics models constructed based on MRI achieved satisfactory diagnostic performance for differentiation of spinal MM and metastases, demonstrating broad application prospects for individualized diagnosis and treatment.

Dynamic Contrast Enhanced MR Perfusion and Diffusion-Weighted Imaging of Marrow-Replacing Disorders of the Spine: A Comprehensive Review

Significant advancements in cancer treatment have led to improved survival rates for patients, particularly in the context of spinal metastases. However, early detection and monitoring of treatment response remain crucial for optimizing patient outcomes. Although conventional imaging methods such as bone scan, PET, MR imaging, and computed tomography are commonly used for diagnosing and monitoring treatment, they present challenges in differential diagnoses and treatment response monitoring. This review article provides a comprehensive overview of the principles, applications, and practical uses of dynamic contrast-enhanced MR imaging and diffusion-weighted imaging in the assessment and monitoring of marrow-replacing disorders of the spine.

Multiple myeloma following bone metastasis of renal cell carcinoma: a case report

Background

The clinical manifestations of multiple myeloma (MM) and bone metastatic tumor are both systemic bone pain, which is difficult to distinguish from imaging manifestations, leading to misdiagnosis and missed diagnosis.

Case summary

We reported a man with a unique case whose tumors were MM with bone metastatic tumor of clear cell renal cell carcinoma (CCRCC). Computed tomography (CT) showed multifocal osteolytic bone destruction, while magnetic resonance imaging (MRI) showed multifocal bone marrow infiltration with soft tissue mass. Pathology and immunohistochemistry established the diagnosis of the coexistence of myeloma with bone metastatic tumor of CCRCC in the spine. Immunotherapy and systemic chemotherapy were adopted in the clinic, and vertebral decompression was performed after anemia was corrected. This case with MM and bone metastatic tumor of CCRCC received radiotherapy and immunotherapy and acquired satisfying outcome after 1 year of follow-up.

Conclusion

It is difficult to differentiate MM and bone metastatic tumor on imaging, especially when there are bone lesions at the same time, which is an easily missed diagnosis and needs to be comprehensively evaluated in combination with functional procedures, clinical laboratory tests, and histopathology.

An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma

BACKGROUND

Spinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma.

MATERIALS AND METHODS

A total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves.

RESULTS

A clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p < 0.001) sets. The AUC values of the radiomics nomogram model were higher than that of radiologist in training, validation and external test sets (all p < 0.05). Moreover, no significant difference in AUC values of radiomics nomogram model was found between the validation set and external test set (p = 0.212).

CONCLUSION

The radiomics nomogram can differentiate spinal metastasis and multiple myeloma with a moderate to good performance, and may be as a valuable method to assist in the clinical diagnosis and preoperative decision-making.

Pitfalls of Diffusion-Weighted Imaging: Clinical Utility of T2 Shine-through and T2 Black-out for Musculoskeletal Diseases

Diffusion-weighted imaging (DWI) with an apparent diffusion coefficient (ADC) value is a relatively new magnetic resonance imaging (MRI) sequence that provides functional information on the lesion by measuring the microscopic movement of water molecules. While numerous studies have evaluated the promising role of DWI in musculoskeletal radiology, most have focused on tumorous diseases related to cellularity. This review article aims to summarize DWI-acquisition techniques, considering pitfalls such as T2 shine-through and T2 black-out, and their usefulness in interpreting musculoskeletal diseases with imaging. DWI is based on the Brownian motion of water molecules within the tissue, achieved by applying diffusion-sensitizing gradients. Regardless of the cellularity of the lesion, several pitfalls must be considered when interpreting DWI with ADC values in musculoskeletal radiology. This review discusses the application of DWI in musculoskeletal diseases, including tumor and tumor mimickers, as well as non-tumorous diseases, with a focus on lesions demonstrating T2 shine-through and T2 black-out effects. Understanding these pitfalls of DWI can provide clinically useful information, increase diagnostic accuracy, and improve patient management when added to conventional MRI in musculoskeletal diseases.

Intravoxel incoherent motion to differentiate spinal metastasis: A pilot study

Background

To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis.

Methods

This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow, ADCfast, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired. Receiver operating characteristic (ROC) and the area under the ROC curve (AUC) analysis was used to evaluate the diagnostic performance. Each parameter was substituted into a logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.

Results

The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis (all p &lt; 0.05). The logistic regression model results showed that ADCfast and f were independent factors affecting the outcome (P &lt; 0.05). The AUC values of ADCfast and f were 0.823 (95% confidence interval (CI): 0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95% CI: 0.858 to 0.992).

Conclusions

IVIM MR imaging might be helpful to differentiate spinal metastasis from tuberculous spondylitis, and provide guidance for clinical treatment.

Differentiation between spinal multiple myeloma and metastases originated from lung using multi-view attention-guided network

Purpose

Multiple myeloma (MM) and metastasis originated are the two common malignancy diseases in the spine. They usually show similar imaging patterns and are highly demanded to differentiate for precision diagnosis and treatment planning. The objective of this study is therefore to construct a novel deep-learning-based method for effective differentiation of two diseases, with the comparative study of traditional radiomics analysis.

Methods

We retrospectively enrolled a total of 217 patients with 269 lesions, who were diagnosed with spinal MM (79 cases, 81 lesions) or spinal metastases originated from lung cancer (138 cases, 188 lesions) confirmed by postoperative pathology. Magnetic resonance imaging (MRI) sequences of all patients were collected and reviewed. A novel deep learning model of the Multi-view Attention-Guided Network (MAGN) was constructed based on contrast-enhanced T1WI (CET1) sequences. The constructed model extracts features from three views (sagittal, coronal and axial) and fused them for a more comprehensive differentiation analysis, and the attention guidance strategy is adopted for improving the classification performance, and increasing the interpretability of the method. The diagnostic efficiency among MAGN, radiomics model and the radiologist assessment were compared by the area under the receiver operating characteristic curve (AUC).

Results

Ablation studies were conducted to demonstrate the validity of multi-view fusion and attention guidance strategies: It has shown that the diagnostic model using multi-view fusion achieved higher diagnostic performance [ACC (0.79), AUC (0.77) and F1-score (0.67)] than those using single-view (sagittal, axial and coronal) images. Besides, MAGN incorporating attention guidance strategy further boosted performance as the ACC, AUC and F1-scores reached 0.81, 0.78 and 0.71, respectively. In addition, the MAGN outperforms the radiomics methods and radiologist assessment. The highest ACC, AUC and F1-score for the latter two methods were 0.71, 0.76 & 0.54, and 0.69, 0.71, & 0.65, respectively.

Conclusions

The proposed MAGN can achieve satisfactory performance in differentiating spinal MM between metastases originating from lung cancer, which also outperforms the radiomics method and radiologist assessment.

Magnetic resonance imaging study of normal cranial bone marrow conversion at high altitude

BACKGROUND

To use conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) to investigate the effects of long-term hypoxia on cranial bone marrow conversion in healthy people at high altitudes.

METHODS

A total of 1,130 individuals were selected from altitudinal areas of 2,000-3,000, 3,100-4,000, and >4,100 m. Each altitude range was divided into 5 age groups: 0-5, 6-14, 15-29, 30-49, and ≥50 years. Firstly, cranial bone marrow typing of the participants in each altitude range was performed on sagittal T1-weighted images (T1WI) according to the average diploe thickness and signal intensity of the normal skull, and the relationship between bone marrow conversion and age was analyzed. Secondly, the apparent diffusion coefficient (ADC) values of the frontal bone, parietal bone, occipital bone, and temporal bone were measured in the DWI post-processing workstation and statistical methods were used to analyze whether different altitudinal gradients and long-term hypoxic environment had any effect on cranial bone marrow conversion.

RESULTS

There was a positive correlation between bone marrow type and age in the healthy populations at all 3 levels of altitude (P<0.05). The average thickness of the cranial diploe also positively correlated with age (P<0.05); in the age ranges of 30-49 and ≥50 years, the ADC values of the occipital and temporal bone marrow positively correlated with increasing altitude (P<0.05).

CONCLUSIONS

The cranial bone marrow of normal people at high altitudes changes from Type I to Type IV with increasing age and under the influence of long-term chronic hypoxia. The bone marrow of the occipital and temporal bones of healthy people aged 30-49 and ≥50 years showed erythromedularization during the process of Type III and IV bone marrow conversion.

Comparison of conventional magnetic resonance imaging and diffusion-weighted imaging in the differentiation of bone plasmacytoma from bone metastasis in the extremities

PURPOSE

To compare conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differentiation of bone plasmacytoma from bone metastasis in the extremities.

MATERIALS AND METHODS

A total of 65 patients with 27 bone plasmacytomas (11 men; mean age, 63.6±8.2 [SD] years) and 38 patients with bone metastases (20 men; mean age, 64.1±11.5 [SD] years) were retrospectively included. Plasmacytomas and metastases were compared for size, peritumoral edema, signal intensity (SI), SI pattern, apparent diffusion coefficient (ADC) values and standard deviation (SD) of ADC. Receiver operating characteristic analysis with area under the curve (AUC) was used to calculate sensitivity, specificity, and accuracy of MRI and DWI for the diagnosis of plasmacytoma according to a defined cut-off value.

RESULTS

On conventional MRI, plasmacytomas showed less peritumoral edema (22% vs. 71%; P<0.001), were more often hyperintense on T1-weighted image (48% vs. 18%; P=0.022) and more homogeneous on T2-weighted image (78% vs. 26%; P<0.001) and contrast-enhanced T1-weighted images (70% vs. 25%; P=0.001) than bone metastases. Mean ADC value and SD of ADC were significantly lower in bone plasmacytomas (760.1±196.9 [SD] μm²/s and 161.5±62.7 [SD], respectively) than in bone metastases (1214.2±382.6 [SD] μm²/s and 277.0±110.3 [SD], respectively) (P<0.001). Using an ADC value≤908.3μm²/s, DWI yielded 88% sensitivity and 78% specificity for the diagnosis of plasmacytoma. ADC value yielded best area under the curve (AUC=0.913), followed by SD of ADC (AUC=0.814) and homogeneity on T2-weighted images (AUC=0.757). The combination of conventional MRI and DWI (AUC=0.894) showed improved diagnostic performance over conventional MRI alone (AUC= 0.843) for discriminating between plasmacytoma and metastasis.

CONCLUSION

Conventional MRI in combination with DWI can be useful to discriminate between bone plasmacytoma and bone metastasis in the extremities.

T1-weighted Dynamic Contrast-enhanced MRI to Differentiate Nonneoplastic and Malignant Vertebral Body Lesions in the Spine

Background Dynamic contrast agent-enhanced (DCE) perfusion MRI may help differentiate between nonneoplastic and malignant lesions in the spine. Purpose To investigate the correlation between fractional plasma volume (V_p), a parameter derived from DCE perfusion MRI, and histopathologic diagnosis for spinal lesions. Materials and Methods In this retrospective study, patients who underwent DCE perfusion MRI and lesion biopsy between May 2015 and May 2018 were included. Inclusion criteria were short time interval (<30 days) between DCE perfusion MRI and biopsy, DCE perfusion MRI performed before biopsy, and DCE perfusion MRI performed at the same spine level as biopsy. Exclusion criteria were prior radiation treatment on vertebrae of interest, poor DCE perfusion MRI quality, nondiagnostic biopsy, and extensive spinal metastasis or prior kyphoplasty. One hundred thirty-four lesions were separated into a nonneoplastic group (n = 51) and a malignant group (n = 83) on the basis of histopathologic analysis. Two investigators manually defined regions of interest in the vertebrae. DCE perfusion MRI parameter V_p was calculated by using the Tofts pharmacokinetic two-compartment model. V_p was quantified, normalized to adjacent normal vertebrae, and compared between the two groups. A Mann-Whitney U test and receiver operating characteristic analysis was performed to verify the difference in V_p between the nonneoplastic and malignant groups. Reproducibility was assessed by calculating the Cohen κ coefficient. Results One hundred patients (mean age, 65 years ± 11 [standard deviation]; 52 men) were evaluated. V_p was lower in nonneoplastic lesions versus malignant lesions (1.6 ± 1.3 vs 4.2 ± 3.0, respectively; P < .001). The sensitivity of V_p was 93% (77 of 83; 95% confidence interval [CI]: 85%, 97%), specificity was 78% (40 of 51; 95% CI: 65%, 89%), and area under the receiver operating characteristic curve was 0.88 (95% CI: 0.82, 0.95). Cohen κ coefficient suggested substantial agreement in both intra- (κ = 0.72) and interreader (κ = 0.70) reproducibility. Conclusion This study indicated that dynamic contrast agent-enhanced perfusion MRI parameter, fractional plasma volume, was able to differentiate between nonneoplastic spinal lesions and malignant lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Haller in this issue.