ADC as a useful diagnostic tool for differentiating benign and malignant vertebral bone marrow lesions and compression fractures: a systematic review and meta-analysis

Review of whole-body magnetic resonance imaging in multiple myeloma

Multiple Myeloma (MM) is a hematological malignancy affecting bone marrow, most frequently in elderly men. Imaging has a crucial role in this disease. Recently, whole-body MRI has been introduced and it has gained growing interest due to is high sensitivity and specificity in evaluating bone marrow involvement in MM. Diffusion-weighted sequences (DWI) with apparent diffusion coefficient (ADC) maps have emerged as the most sensitive technique to evaluate patients with MM, both in the pre- and post-treatment setting. Aim of this review is to provide an overview of the role and main imaging findings of whole-body MRI in MM.

Multiparametric quantification of T1 and T2 relaxation time of bone metastasis in comparison with red or fatty bone marrow using magnetic resonance fingerprinting

OBJECTIVES

To assess the T1 and T2 values of bone marrow lesions in spine and pelvis derived from magnetic resonance fingerprinting (MRF) and to evaluate the differences in values among bone metastasis, red marrow and fatty marrow.

METHODS

Sixty patients who underwent lumbar spine and pelvic MRI with magnetic resonance fingerprinting were retrospectively included. Among eligible patients, those with bone metastasis, benign red marrow deposition and normal fatty marrow were identified. Two radiologists independently measured the T1 and T2 values from metastatic bone lesions, fatty marrow, and red marrow deposition on three-dimensional-magnetic resonance fingerprinting. Intergroup comparison and interobserver agreement were analyzed.

RESULTS

T1 relaxation time was significantly higher in osteoblastic metastasis than in red marrow (1674.6 ± 436.3 vs 858.7 ± 319.5, p < .001). Intraclass correlation coefficients for T1 and T2 values were 0.96 (p < 0.001) and 0.83 (p < 0.001), respectively. T2 relaxation time of osteoblastic metastasis and red marrow deposition had no evidence of a difference (osteoblastic metastasis, 57.9 ± 25.0 vs red marrow, 58.0 ± 34.4, p = 0.45), as were the average T2 values of osteolytic metastasis and red marrow deposition (osteolytic metastasis, 45.3 ± 15.1 vs red marrow, 58.0 ± 34.4, p = 0.63).

CONCLUSIONS

We report the feasibility of three-dimensional-magnetic resonance fingerprinting based quantification of bone marrow to differentiate bone metastasis from red marrow. Simultaneous T1 and T2 quantification of metastasis and red marrow deposition was possible in spine and pelvis and showed significant different values with excellent inter-reader agreement.

ADVANCE IN KNOWLEDGE

T1 values from three-dimensional-magnetic resonance fingerprinting might be a useful quantifier for evaluating bone marrow lesions.

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.

A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI

To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.

Imaging of bone marrow pitfalls with emphasis on MRI

Normal marrow contains both hematopoietic/red and fatty/yellow marrow with a predictable pattern of conversion and skeletal distribution on MRI. Many variations in normal bone marrow signal and appearances are apparent and the reporting radiologist must differentiate these from other non-neoplastic, benign or neoplastic processes. The advent of chemical shift imaging has helped in characterising and differentiating more focal heterogeneous areas of red marrow from marrow infiltration. This review aims to cover the MRI appearances of normal marrow, its evolution with age, marrow reconversion, variations of normal marrow signal, causes of oedema-like marrow signal, and some common non-neoplastic entities, which may mimic marrow neoplasms.

Advances in Bone Marrow Imaging: Strengths and Limitations from a Clinical Perspective

Conventional magnetic resonance imaging (MRI) remains the modality of choice to image bone marrow. However, the last few decades have witnessed the emergence and development of novel MRI techniques, such as chemical shift imaging, diffusion-weighted imaging, dynamic contrast-enhanced MRI, and whole-body MRI, as well as spectral computed tomography and nuclear medicine techniques. We summarize the technical bases behind these methods, in relation to the common physiologic and pathologic processes involving the bone marrow. We present the strengths and limitations of these imaging methods and consider their added value compared with conventional imaging in assessing non-neoplastic disorders like septic, rheumatologic, traumatic, and metabolic conditions. The potential usefulness of these methods to differentiate between benign and malignant bone marrow lesions is discussed. Finally, we consider the limitations hampering a more widespread use of these techniques in clinical practice.

Deep learning-based artificial intelligence model for classification of vertebral compression fractures: A multicenter diagnostic study

OBJECTIVE

To develop and validate an artificial intelligence diagnostic system based on X-ray imaging data for diagnosing vertebral compression fractures (VCFs).

METHODS

In total, 1904 patients who underwent X-ray at four independent hospitals were retrospectively (n=1847) and prospectively (n=57) enrolled. The participants were separated into a development cohort, a prospective test cohort and three external test cohorts. The proposed model used a transfer learning method based on the ResNet-18 architecture. The diagnostic performance of the model was evaluated using receiver operating characteristic curve (ROC) analysis and validated using a prospective validation set and three external sets. The performance of the model was compared with three degrees of musculoskeletal expertise: expert, competent, and trainee.

RESULTS

The diagnostic accuracy for identifying compression fractures was 0.850 in the testing set, 0.829 in the prospective set, and ranged from 0.757 to 0.832 in the three external validation sets. In the human and deep learning (DL) collaboration dataset, the area under the ROC curves(AUCs) in acute, chronic, and pathological compression fractures were as follows: 0.780, 0.809, 0.734 for the DL model; 0.573, 0.618, 0.541 for the trainee radiologist; 0.701, 0.782, 0.665 for the competent radiologist; 0.707,0.732, 0.667 for the expert radiologist; 0.722, 0.744, 0.610 for the DL and trainee; 0.767, 0.779, 0.729 for the DL and competent; 0.801, 0.825, 0.751 for the DL and expert radiologist.

CONCLUSIONS

Our study offers a high-accuracy multi-class deep learning model which could assist community-based hospitals in improving the diagnostic accuracy of VCFs.

Apparent diffusion coefficient values in Modic changes – interobserver reproducibility and relation to Modic type

Background

Modic Changes (MCs) in the vertebral bone marrow were related to back pain in some studies but have uncertain clinical relevance. Diffusion weighted MRI with apparent diffusion coefficient (ADC)-measurements can add information on bone marrow lesions. However, few have studied ADC measurements in MCs. Further studies require reproducible and valid measurements. We expect valid ADC values to be higher in MC type 1 (oedema type) vs type 3 (sclerotic type) vs type 2 (fatty type). Accordingly, the purpose of this study was to evaluate ADC values in MCs for interobserver reproducibility and relation to MC type.

Methods

We used ADC maps (b 50, 400, 800 s/mm²) from 1.5 T lumbar spine MRI of 90 chronic low back pain patients with MCs in the AIM (Antibiotics In Modic changes)-study. Two radiologists independently measured ADC in fixed-sized regions of interests. Variables were MC-ADC (ADC in MC), MC-ADC% (0% = vertebral body, 100% = cerebrospinal fluid) and MC-ADC-ratio (MC-ADC divided by vertebral body ADC). We calculated mean difference between observers ± limits of agreement (LoA) at separate endplates. The relation between ADC variables and MC type was assessed using linear mixed-effects models and by calculating the area under the receiver operating characteristic curve (AUC).

Results

The 90 patients (mean age 44 years; 54 women) had 224 MCs Th12-S1 comprising type 1 (n = 111), type 2 (n = 91) and type 3 MC groups (n = 22). All ADC variables had higher predicted mean for type 1 vs 3 vs 2 (p < 0.001 to 0.02): MC-ADC (10^− 6 mm²/s) 1201/796/576, MC-ADC% 36/21/14, and MC-ADC-ratio 5.9/4.2/3.1. MC-ADC and MC-ADC% had moderate to high ability to discriminate between the MC type groups (AUC 0.73–0.91). MC-ADC-ratio had low to moderate ability (AUC 0.67–0.85). At L4-S1, widest/narrowest LoA were for MC-ADC 20 ± 407/12 ± 254, MC-ADC% 1.6 ± 18.8/1.4 ± 10.4, and MC-ADC-ratio 0.3 ± 4.3/0.2 ± 3.9. Difference between observers > 50% of their mean value was less frequent for MC-ADC (9% of MCs) vs MC-ADC% and MC-ADC-ratio (17–20%).

Conclusions

The MC-ADC variable (highest mean ADC in the MC) had best interobserver reproducibility, discriminated between MC type groups, and may be used in further research. ADC values differed between MC types as expected from previously reported MC histology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05610-4.

Diffusion-weighted magnetic resonance imaging in differentiation between different vertebral lesions using ADC mapping as a quantitative assessment tool

Background

Diffusion-weighted imaging is one of the most useful clinical MRI techniques. Including this technique with other sequences used for routine spine scanning improves sensitivity and the capacity to characterize lesions. This study aims to evaluate the utility of apparent diffusion coefficient obtained from diffusion-weighted MR imaging in differentiating between benign and malignant vertebral lesions according to the optimal cutoff ADC value.

Results

This study included 30 patients at Ain Shams University hospitals; all of them were subjected to full clinical assessment and magnetic resonance imaging. Patients were classified into 4 groups: inflammatory lesions (12 cases) followed by malignant lesions (7 cases), then benign neoplastic lesions (6 cases), then traumatic lesions (3 cases) and osteoporosis (two cases). Inflammatory lesions revealed restricted diffusion. Benign neoplastic lesions/hemangioma showed low signal at DWIs due to free diffusion, while malignant/metastatic lesions showed restricted diffusion. Traumatic lesions showed restricted diffusion. The osteoporotic lesions showed iso- to hyper-intense signal at DWIs. The mean ADC value of the benign lesions was 1.8 ± 0.43 mm2/s, while metastatic tumors was 0.96 ± 0.5 × 10–3 mm2/s; however, overlapping values may be present.

Conclusions

Compared with benign tumors, malignant tumors have lower ADC values; nevertheless, some lesions, such as tuberculosis, have low ADC values that are like those of malignant tumors. Diffusion MRI and ADC values should always be analyzed in conjunction with standard MRI sequences as well as a thorough clinical history and examination.

Diffusion-Weighted Magnetic Resonance Imaging: Clinical Potential and Applications

Since its discovery in the 1980s [...].

Automated Differentiation Between Osteoporotic Vertebral Fracture and Malignant Vertebral Fracture on MRI Using a Deep Convolutional Neural Network

STUDY DESIGN

Retrospective study of magnetic resonance imaging (MRI).

OBJECTIVES

To assess the ability of a convolutional neural network (CNN) model to differentiate osteoporotic vertebral fractures (OVFs) and malignant vertebral compression fractures (MVFs) using short-TI inversion recovery (STIR) and T1-weighted images (T1WI) and to compare it to the performance of three spine surgeons.

SUMMARY OF BACKGROUND DATA

Differentiating between OVFs and MVFs is crucial for appropriate clinical staging and treatment planning. However, an accurate diagnosis is sometimes difficult. Recently, CNN modeling-an artificial intelligence technique-has gained popularity in the radiology field.

METHODS

We enrolled 50 patients with OVFs and 47 patients with MVFs who underwent thoracolumbar MRI. Sagittal STIR images and sagittal T1WI were used to train and validate the CNN models. To assess the performance of the CNN, the receiver operating characteristic curve was plotted and the area under the curve was calculated. We also compared the accuracy, sensitivity, and specificity of the diagnosis made by the CNN and three spine surgeons.

RESULTS

The area under the curve of receiver operating characteristic curves of the CNN based on STIR images and T1WI were 0.967 and 0.984, respectively. The CNN model based on STIR images showed a performance of 93.8% accuracy, 92.5% sensitivity, and 94.9% specificity. On the other hand, the CNN model based on T1WI showed a performance of 96.4% accuracy, 98.1% sensitivity, and 94.9% specificity. The accuracy and specificity of the CNN using both STIR and T1WI were statistically equal to or better than that of three spine surgeons. There were no significant differences in sensitivity based on both STIR images and T1WI between the CNN and spine surgeons.

CONCLUSION

We successfully differentiated OVFs and MVFs based on MRI with high accuracy using the CNN model, which was statistically equal or superior to that of the spine surgeons.Level of Evidence: 4.

Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance

PURPOSE

To evaluate stability and machine learning-based classification performance of radiomic features of spine bone tumors using diffusion- and T2-weighted magnetic resonance imaging (MRI).

MATERIAL AND METHODS

This retrospective study included 101 patients with histology-proven spine bone tumor (22 benign; 38 primary malignant; 41 metastatic). All tumor volumes were manually segmented on morphologic T2-weighted sequences. The same region of interest (ROI) was used to perform radiomic analysis on ADC map. A total of 1702 radiomic features was considered. Feature stability was assessed through small geometrical transformations of the ROIs mimicking multiple manual delineations. Intraclass correlation coefficient (ICC) quantified feature stability. Feature selection consisted of stability-based (ICC > 0.75) and significance-based selections (ranking features by decreasing Mann-Whitney p-value). Class balancing was performed to oversample the minority (i.e., benign) class. Selected features were used to train and test a support vector machine (SVM) to discriminate benign from malignant spine tumors using tenfold cross-validation.

RESULTS

A total of 76.4% radiomic features were stable. The quality metrics for the SVM were evaluated as a function of the number of selected features. The radiomic model with the best performance and the lowest number of features for classifying tumor types included 8 features. The metrics were 78% sensitivity, 68% specificity, 76% accuracy and AUC 0.78.

CONCLUSION

SVM classifiers based on radiomic features extracted from T2- and diffusion-weighted imaging with ADC map are promising for classification of spine bone tumors. Radiomic features of spine bone tumors show good reproducibility rates.

Review of diffusion-weighted imaging and dynamic contrast-enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma)

The last decades, increasing research has been conducted on dynamic contrast-enhanced and diffusion-weighted MRI techniques in multiple myeloma and its precursors. Apart from anatomical sequences which are prone to interpretation errors due to anatomical variants, other pathologies and subjective evaluation of signal intensities, dynamic contrast-enhanced and diffusion-weighted MRI provide additional information on microenvironmental changes in bone marrow and are helpful in the diagnosis, staging and follow-up of plasma cell dyscrasias. Diffusion-weighted imaging provides information on diffusion (restriction) of water molecules in bone marrow and in malignant infiltration. Qualitative evaluation by visually assessing images with different diffusion sensitising gradients and quantitative evaluation of the apparent diffusion coefficient are studied extensively. Dynamic contrast-enhanced imaging provides information on bone marrow vascularisation, perfusion, capillary resistance, vascular permeability and interstitial space, which are systematically altered in different disease stages and can be evaluated in a qualitative and a (semi-)quantitative manner. Both diffusion restriction and abnormal dynamic contrast-enhanced MRI parameters are early biomarkers of malignancy or disease progression in focal lesions or in regions with diffuse abnormal signal intensities. The added value for both techniques lies in better detection and/or characterisation of abnormal bone marrow otherwise missed or misdiagnosed on anatomical MRI sequences. Increased detection rates of focal lesions or diffuse bone marrow infiltration upstage patients to higher disease stages, provide earlier access to therapy and slower disease progression and allow closer monitoring of high-risk patients. Despite promising results, variations in imaging protocols, scanner types and post-processing methods are large, thus hampering universal applicability and reproducibility of quantitative imaging parameters. The myeloma response assessment and diagnosis system and the international myeloma working group provide a systematic multicentre approach on imaging and propose which parameters to use in multiple myeloma and its precursors in an attempt to overcome the pitfalls of dynamic contrast-enhanced and diffusion-weighted imaging.Single sentence summary statementDiffusion-weighted imaging and dynamic contrast-enhanced MRI provide important additional information to standard anatomical MRI techniques for diagnosis, staging and follow-up of patients with plasma cell dyscrasias, although some precautions should be taken on standardisation of imaging protocols to improve reproducibility and application in multiple centres.

Whole-body magnetic resonance imaging (WBMRI) versus whole-body computed tomography (WBCT) for myeloma imaging and staging

Myeloma-associated bone disease (MBD) develops in about 80-90% of patients and severely affects their quality of life, as it accounts for the majority of mortality and morbidity. Imaging in multiple myeloma (MM) and MBD is of utmost importance in order to detect bone and bone marrow lesions as well as extraosseous soft-tissue masses and complications before the initiation of treatment. It is required for determination of the stage of disease and aids in the assessment of treatment response. Whole-body low-dose computed tomography (WBLDCT) is the key modality to establish the initial diagnosis of MM and is now recommended as reference standard procedure for the detection of lytic destruction in MBD. In contrast, whole-body magnetic resonance imaging (WBMRI) has higher sensitivity for the detection of focal and diffuse plasma cell infiltration patterns of the bone marrow and identifies them prior to osteolytic destruction. It is recommended for the evaluation of spinal and vertebral lesions, while functional, diffusion-weighted MRI (DWI-MRI) is a promising tool for the assessment of treatment response. This review addresses the current improvements and limitations of WBCT and WBMRI for diagnosis and staging in MM, underlining the fact that both modalities offer complementary information. It further summarizes the corresponding radiological findings and novel technological aspects of both modalities.

3D MR fingerprinting (MRF) for simultaneous T1 and T2 quantification of the bone metastasis: Initial validation in prostate cancer patients

PURPOSE

To investigate the feasibility of using 3-dimensional MRF for bone marrow evaluation in the field of view of prostate MRI for T1 and T2 quantification of prostate cancer bone metastases, as well as comparing it to the ADC value.

METHODS

In this retrospective study, 30 prostate MRIs were included: 14 cases with prostate cancer bone metastasis and 16 cases without prostate cancer (control). MRF was obtained twice before (nonenhanced [NE] MRF) and after contrast injection (contrast-enhanced [CE] MRF), and T1 and T2 maps were generated from each MRF. Two radiologists independently drew regions of interest (ROIs) on the MRF maps and the ADC maps. Mann-Whitney U tests and the area under the receiver operating characteristic curve (AUROC) evaluated the two-reader means of T1, T2 and ADC values between bone metastasis and normal bone.

RESULTS

There were 83 ROIs, including 39 bone metastases and 44 normal bone. The two-reader average ADC, NE T2 and CE T2 values were significantly lower and NE T1 and CE T1 values were significantly higher in metastatic bone compared with normal bone (P < 0.001). The AUROC of the ADC was lowest (0.685), which was significantly lower than those of NE T1 (1.0, P = 0.001), NE T2 (0.932, P = 0.004), and CE T2 (0.876, P = 0.031).

CONCLUSION

MRF to assess the pelvic bone during a prostate gland evaluation provides a reliable parametric map for skeletal work-up. With higher diagnostic performance than the ADC value, NE MRF is a potential alternative for quantifying bone marrow metastases in prostate cancer patients.

Single shot zonal oblique multislice SE-EPI diffusion-weighted imaging with low to ultra-high b-values for the differentiation of benign and malignant vertebral spinal fractures

Purpose

To investigate the diagnostic yield of low to ultra-high b-values for the differentiation of benign from malignant vertebral fractures using a state-of-the-art single-shot zonal-oblique-multislice spin-echo echo-planar diffusion-weighted imaging sequence (SShot ZOOM SE-EPI DWI).

Materials and Methods

66 patients (34 malignant, 32 benign) were examined on 1.5 T MR scanners. ADC maps were generated from b-values of 0,400; 0,1000 and 0,2000s/mm². ROIs were placed into the fracture of interest on ADC maps and trace images and into adjacent normal vertebral bodies on trace images. The ADC of fractures and the Signal-Intensity-Ratio (SIR) of fractures relative to normal vertebral bodies on trace images were considered quantitative metrics. The appearance of the fracture of interest was graded qualitatively as iso-, hypo-, or hyperintense relative to normal vertebrae.

Results

ADC achieved an area under the curve (AUC) of 0.785/0.698/0.592 for b = 0,400/0,1000/0,2000s/mm² ADC maps respectively. SIR achieved an AUC of 0.841/0.919/0.917 for b = 400/1000/2000s/mm² trace images respectively. In qualitative analyses, only b = 2000s/mm² trace images were diagnostically valuable (sensitivity:1, specificity:0.794). Machine learning models incorporating all qualitative and quantitative metrics achieved an AUC of 0.95/0.98/0.98 for b-values of 400/1000/2000s/mm² respectively. The model incorporating only qualitative metrics from b = 2000s/mm² achieved an AUC of 0.97.

Conclusion

By using quantitative and qualitative metrics from SShot ZOOM SE-EPI DWI, benign and malignant vertebral fractures can be differentiated with high diagnostic accuracy. Importantly qualitative analysis of ultra-high b-value images may suffice for differentiation as well.

Diagnostic Performance of Diffusion-Weighted Imaging for Differentiating Malignant From Benign Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Systematic Review and Meta-Analysis

Objectives

To assess the diagnostic accuracy of diffusion-weighted imaging (DWI) in predicting the malignant potential in patients with intraductal papillary mucinous neoplasms (IPMNs) of the pancreas.

Methods

A systematic search of articles investigating the diagnostic performance of DWI for prediction of malignant potential in IPMNs was conducted from PubMed, Embase, and Web of Science from January 1997 to 10 February 2020. QUADAS-2 tool was used to evaluate the study quality. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratios (PLR), negative likelihood ratios (NLR), and their 95% confidence intervals (CIs) were calculated. The summary receiver operating characteristic (SROC) curve was then plotted, and meta-regression was also performed to explore the heterogeneity.

Results

Five articles with 307 patients were included. The pooled sensitivity and specificity of DWI were 0.74 (95% CI: 0.65, 0.82) and 0.94 (95% CI: 0.78, 0.99), in evaluating the malignant potential of IPMNs. The PLR was 13.5 (95% CI: 3.1, 58.7), the NLR was 0.27 (95% CI: 0.20, 0.37), and DOR was 50.0 (95% CI: 11.0, 224.0). The area under the curve (AUC) of SROC curve was 0.84 (95% CI: 0.80, 0.87). The meta-regression showed that the slice thickness of DWI (p = 0.02) and DWI parameter (p= 0.01) were significant factors affecting the heterogeneity.

Conclusions

DWI is an effective modality for the differential diagnosis between benign and malignant IPMNs. The slice thickness of DWI and DWI parameter were the main factors influencing diagnostic specificity.

Assessment of diffusion tensor imaging in differentiation between pyogenic and tuberculous spondylitis

PURPOSE

to assess diffusion tensor imaging (DTI); an emerging technique for differentiation between pyogenic and tuberculous spondylitis.

PATIENTS AND METHODS

The study was carried out on 33 patients with infective spondylitis performing conventional MRI and DTI. The mean diffusivity (MD) and fractional anisotropy (FA) of the affected vertebral body were calculated by two readers.

RESULTS

The MD of pyogenic spondylitis of both readers (1.48 ± 0.09 and 1.47 ± 0.08 × 10^-3 mm²/s) were significantly higher values (P = 0.001) than tuberculous spondylitis (1.11 ± 0.15 and 1.18 ± 0.08 × 10^-3 mm²/s). The FA of pyogenic spondylitis of both readers (0.18 ± 0.09 and 0.20 ± 0.08) were significantly lower values (P = 0.001) than tuberculous spondylitis (0.30 ± 0.05 and 0.32 ± 0.03). There was a strong inter-reader agreement between both readers using MD (K = 0.963) and FA (K = 0.858). The thresholds MD and FA used for differentiating pyogenic and tuberculous spondylitis of both readers were 1.37 and 1.33 × 10^-3 mm²/s and 0.21 and 0.25 with the area under the curve (AUC) of 0.927 and 0.831 respectively. Combined MD and FA revealed increased AUC to 0.97 and 0.98 of both readers respectively.

CONCLUSION

DTI with its parameters can be considered a noninvasive beneficial quantitative method that can help in differentiation between pyogenic and tuberculous spondylitis.

An assessment of fluid-fluid levels on magnetic resonance imaging of spinal tumours

OBJECTIVE

To assess the degree of fluid-fluid levels on MRI in spinal tumours compared with final diagnosis, and the outcome of needle biopsy in such cases.

MATERIALS AND METHODS

Retrospective review of patients with a spinal tumour that contained fluid-fluid levels. Data collected included age, sex, spinal location, and final diagnosis. The outcome of needle biopsy was investigated.

RESULTS

Forty-two patients were included (19 males; 24 females; mean age 27.5 years, range 5-80 years), the commonest diagnoses being aneurysmal bone cyst (n = 25; 59.5%) and metastasis (n = 5; 11.9%). All patients with a malignant diagnosis were > 50 years of age apart from 2 who had metastases from a known primary cancer, while all patients apart from 1 with aneurysmal bone cyst were < 35 years of age. Needle biopsy was undertaken in 29 cases (69%) and diagnostic in 18 (62%). Patients with FFL occupying > 2/3 of the lesion were significantly more likely to have an aneurysmal bone cyst (p = 0.008) while those with FFL occupying < 2/3 of the lesion were more likely to have a malignant tumour (p = 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of > 2/3 FFLs occupying the lesion were 97.1%, 75%, 94.3%, 85.7%, and 92.9% respectively for differentiating a benign from a malignant spinal tumour.

CONCLUSIONS

Children and younger adults with spinal lesions containing > 2/3 FFLs were very unlikely to have malignancy. However, in patients > 50 years of age or those with lesions containing < 2/3 FFLs, a malignant lesion is much more likely.

Diagnostic Accuracy of Quantitative Imaging Biomarkers in the Differentiation of Benign and Malignant Vertebral Lesions : Combination of Diffusion-Weighted and Proton Density Fat Fraction Spine MRI

Purpose

To compare and combine the diagnostic performance of the apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI) and proton density fat fraction (PDFF) derived from chemical-shift encoding (CSE)-based water-fat magnetic resonance imaging (MRI) for distinguishing benign and malignant vertebral bone marrow lesions (VBML).

Methods

A total of 55 consecutive patients with 53 benign (traumatic, inflammatory and primary) and 36 malignant (metastatic and hematologic) previously untreated VBMLs were prospectively enrolled in this IRB-approved study and underwent sagittal DWI (single-shot spin-echo echo-planar with multi-slice short TI inversion recovery fat suppression) and CSE-based MRI (gradient-echo 6‑point modified Dixon) in addition to routine clinical spine MRI at 1.5 T or 3.0 T. Diagnostic reference standard was established according to histopathology or imaging follow-up. The ADC = ADC (0, 800) and PDFF = fat / (water + fat) were calculated voxel-wise and examined for differences between benign and malignant lesions.

Results

The ADC and PDFF values of malignant lesions were significantly lower compared to benign lesions (mean ADC 861 × 10⁻⁶ mm²/s vs. 1323 × 10⁻⁶ mm²/s, p < 0.001; mean PDFF 3.1% vs. 28.2%, p < 0.001). The areas under the curve (AUC) and diagnostic accuracies were 0.847 (p < 0.001) and 85.4% (cut-off at 1084.4 × 10⁻⁶ mm²/s) for ADC and 0.940 (p < 0.001) and 89.9% for PDFF (cut-off at 7.8%), respectively. The combined use of ADC and PDFF improved the diagnostic accuracy to 96.6% (malignancy if ADC ≤ 1118.2 × 10⁻⁶ mm²/s and PDFF ≤ 20.0%, otherwise benign).

Conclusion

Quantitative evaluation of both ADC and PDFF was useful in differentiating benign VBMLs from malignancy. The combination of ADC and PDFF improved the diagnostic performance and yielded high diagnostic accuracy for the differentiation of benign and malignant VBMLs.

Diagnostic performance of apparent diffusion coefficient (ADC) for differentiating endometrial carcinoma from benign lesions: a systematic review and meta-analysis

To determine the diagnostic performance of mean ADC values in the characterization of endometrial carcinoma (EC) from benign lesions by systematic review of the literature and performing meta-analysis. A systematic search of major electronic bibliographic databases was performed to find studies that used ADC values for differentiating EC from benign lesions. Two reviewers independently screened the titles and abstracts of the search results and then by reading the full texts selected the pertinent studies for final analyses. A bivariate random-effects model with pooled sensitivity and specificity values with 95% CI (confidence interval) was used. Summary receiver operating characteristic (SROC) curve and area under curve (AUC) were created. Between-study heterogeneity was measured using I squared (I2) index. Eleven studies including 269 ECs and 208 benign lesions were analyzed. Pooled average (95% CI) ADC in EC and benign lesions groups were, respectively, 0.82 (0.77-0.87) × 10-3 mm2/s and 1.41 (1.29-1.52) × 10-3 mm2/s. The combined (95% CI) sensitivity and specificity of mean ADC values for differentiating EC from benign lesions were 93% (87-96%; I2 = 41.19%) and 94% (88-97%; I2 = 46.91%), respectively. The AUC (95% CI) of the SROC curve was 98% (96-99%). ADC values had good diagnostic accuracy for differentiating EC from benign lesions. In order to recommend ADC measurement for detecting endometrial lesions in routine clinical practice, more primary studies, especially trials and comparative studies including hysteroscopically-guided biopsy method, with larger sample sizes are still required.

Review article: the current status of CT-guided needle biopsy of the spine

CT-guided percutaneous needle biopsy of the spine is a well-described technique for determining the nature of indeterminate vertebral lesions or establishing a diagnosis of spinal infection, the high diagnostic accuracy and the safety of the procedure having been extensively documented. The purpose of the current article is to review the literature to date on CT-guided spinal biopsy. Specifically, indications for spinal biopsy, techniques for optimising yield, detail of the approaches for various spinal levels which is dependent upon both the region within the spinal column and lesion location within the vertebra (body vs. neural arch), determinants of biopsy outcome and complications are covered. It is hoped that the review will be of particular benefit to junior radiologists who are required to perform this procedure.

Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence

Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal radiology. Molecular imaging refers to the ability of various methods to in vivo characterize and quantify biological processes, at a molecular level. The extracted information provides the tools to understand the pathophysiology of diseases and thus to early detect, to accurately evaluate the extend and to apply and evaluate targeted treatments. At present, molecular imaging mainly involves CT, MRI, radionuclide, US, and optical imaging and has been reported in many clinical and preclinical studies. Although originally MI techniques targeted at central nervous system disorders, later on their value on musculoskeletal disorders was also studied in depth. Meaningful exploitation of the large volume of imaging data generated by molecular and conventional imaging techniques, requires state-of-the-art computational methods that enable rapid handling of large volumes of information. AI allows end-to-end training of computer algorithms to perform tasks encountered in everyday clinical practice including diagnosis, disease severity classification and image optimization. Notably, the development of deep learning algorithms has offered novel methods that enable intelligent processing of large imaging datasets in an attempt to automate decision-making in a wide variety of settings related to musculoskeletal trauma. Current applications of AI include the diagnosis of bone and soft tissue injuries, monitoring of the healing process and prediction of injuries in the professional sports setting. This review presents the current applications of novel MI techniques and methods and the emerging role of AI regarding the diagnosis and evaluation of musculoskeletal trauma.

Use of proton density fat fraction MRI to predict the radiographic progression of osteoporotic vertebral compression fracture

OBJECTIVE

This study evaluated the diagnostic performance of the proton density fat fraction (PDFF) in predicting the progression of osteoporotic vertebral compression fractures (OVCFs).

METHODS

The cohort in this retrospective study consisted of 48 patients with OVCFs who underwent spine MRI that included PDFF between December 2016 and June 2018. The patients were divided into two groups (with versus without OVCF progression, based on the radiographic results obtained at the 6-month follow-up examination). Two musculoskeletal radiologists independently calculated the PDFF of the fracture and the PDFF ratio (fracture PDFF/normal vertebrae PDFF) using regions of interest. The mean values of these parameters were compared between the two groups, and the receiver operating characteristic curves were analysed.

RESULTS

The mean age was significantly higher in the group with OVCF progression (71.6 ± 8.4 years) than in the group without (64.8 ± 10.5 years) (p = 0.018). According to reader 1, the PDFF ratio was significantly lower in the group with OVCF progression versus that without OVCF progression (0.38 ± 0.13 vs 0.51 ± 0.20; p = 0.009), whereas the difference in the PDFF itself was not statistically significant. The PDFF ratio [area under the curve (AUC) = 0.723; 95% confidence interval (CI), 0.575-0.842] had a larger AUC than did the PDFF (AUC = 0.667; 95% CI, 0.516-0.796). The optimal cut-off value of the PDFF ratio for predicting OVCF progression was 0.42; this threshold corresponded to sensitivity, specificity, and accuracy values of 84.0%, 60.9%, and 72.9%, respectively.

CONCLUSION

The age and PDFF ratio can be used to predict OVCF progression.

KEY POINTS

• Chemical shift-encoded magnetic resonance imaging provides quantitative parameters for predicting OVCF progression. • The PDFF ratio is significantly lower in patients with OVCF progression. • The PDFF ratio is superior to the PDFF for predicting OVCF progression.

Radiation-induced occult insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The nomogram based on quantitative apparent diffusion coefficients for discrimination

Background

Radiation-induced insufficiency fractures (IF) is frequently occult without fracture line, which may be mistaken as metastasis. Quantitative apparent diffusion coefficient (ADC) shows potential value for characterization of benign and malignant bone marrow diseases. The purpose of this study was to develop a nomogram based on multi-parametric ADCs in the differntiation of occult IF from bone metastasis after radiotherapy (RT) for cervical cancer.

Methods

This study included forty-seven patients with cervical cancer that showed emerging new bone lesions in RT field during the follow-up. Multi-parametric quantitative ADC values were measured for each lesion by manually setting region of interests (ROIs) on ADC maps, and the ROIs were copied to adjacent normal muscle and bone marrow. Six parameters were calculated, including ADC_mean, ADC_min, ADC_max, ADC_std, ADC_mean ratio (lesion/normal bone) and ADC_mean ratio (lesion/muscle). For univariate analysis, receiver operating characteristic curve (ROC) analysis was performed to assess the performance. For combined diagnosis, a nomogram model was developed by using a multivariate logistic regression analysis.

Results

A total of 75 bone lesions were identified, including 48 occult IFs and 27 bone metastases. There were significant differences in the six ADC parameters between occult IFs and bone metastases (p < 0.05), the ADC ratio (lesion/ muscle) showed an optimal diagnostic efficacy, with an area under ROC (AUC) of 0.887, the sensitivity of 95.8%, the specificity of 81.5%, respectively. Regarding combined diagnosis, ADC_std and ADC_mean ratio (lesion/muscle) were identified as independent factors and were selected to generate a nomogram model. The nomogram model showed a better performance, yielded an AUC of 0.92, the sensitivity of 91.7%, the specificity of 96.3%, positive predictive value (PPV) of 97.8% and negative predictive value (NPV) of 86.7%, respectively.

Conclusions

Multi-parametric ADC values demonstrate potential value for differentiating occult IFs from bone metastasis, a nomogram based on the combination of ADC_std and ADC_mean ratio (lesion/muscle) may provide an improved classification performance.

Differentiating atypical hemangiomas and vertebral metastases: a field-of-view (FOV) and FOCUS intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) study

PURPOSE

Some atypical vertebral hemangiomas (VHs) may mimic metastases on routine MRI and can result in misdiagnosis and ultimately to additional imaging, biopsy and unnecessary costs. The purpose of this study is to assess the utility of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) on account of field-of-view optimized and constrained undistorted single shot (FOCUS) in distinguishing atypical VHs and vertebral metastases.

METHODS

A total of 25 patients with vertebral metastases and 25 patients with atypical VHs were confirmed by clinical follow-up or pathology. IVIM-DWI imaging was performed at different b values (0, 30, 50, 100, 150, 200, 400, 600, 800, 1000 mm²/s). IVIM parameters [the true diffusion coefficient (D), pseudodiffusion coefficient (D*), standard apparent diffusion coefficient (ADC), and perfusion fraction (f)] were calculated and compared between two groups by using Student's t test. A receiver operating characteristic analysis was performed.

RESULTS

Quantitative analysis of standard ADC and D parameters showed significantly lower values in vertebral metastases when compared to atypical hemangiomas [ADC value: (0.70 ± 0.12) × 10^-3 mm²/s vs (1.14 ± 0.28) × 10^-3 mm²/s; D value: (0.47 ± 0.07) × 10^-3 mm²/s vs (0.76 ± 0.14) × 10^-3 mm²/s, all P < 0.01]. The sensitivity and specificity of D value were 93.8% and 92.3%, respectively.

CONCLUSION

The standard ADC value and D value may be used as an indicator to distinguish vertebral metastases from atypical VHs. FOCUS IVIM-derived parameters provide potential value in the quantitatively differentiating vertebral metastases from vertebral atypical hemangiomas.

Dixon or DWI - Comparing the utility of fat fraction and apparent diffusion coefficient to distinguish between malignant and acute osteoporotic vertebral fractures

PURPOSE

To compare fat fraction (FF) and apparent diffusion coefficient (ADC) as discriminators distinguishing malignant from acute/subacute osteoporotic vertebral fractures.

METHOD

1.5 T MRIs of 42 malignant and 27 acute/subacute osteoporotic vertebral fractures (38 patients) were retrospectively reviewed. Two readers independently classified fractures as malignant or osteoporotic based on conventional imaging morphology. Diagnostic reader confidence was rated as confident or not confident. FF was derived from axial T1 gradient-echo 2-point Dixon MRI. ADC maps were calculated from axial b50 and b900 images. Both readers independently performed ROI measurements of mean FF and ADC of the same fractured vertebrae. FF and ADC values, corresponding ROC curves and optimized cut-off value performance were compared. Inter-reader agreement was analysed by calculation of intraclass correlation coefficients (ICCs). A p-value < 0.05 was deemed significant.

RESULTS

Mean FF and ADC were significantly lower in malignant (9.5 % and 1.05 × 10^-3 mm²/s) compared to osteoporotic fractures (32 % and 1.34 × 10^-3 mm²/s, all p < 0.001). The optimal cut-off FF was 11.5 %, detecting malignant fractures with 86 %/89 % sensitivity/specificity. The optimal ADC cut-off of 1.04 × 10^-3 mm/s² yielded 62 %/96 % sensitivity/specificity. FF AUC (0.93) was significantly larger than ADC AUC (0.82, p = 0.03). In the subgroup of nine cases reported with low expert reader confidence, the optimized cut-off specificities of FF (83 %) and ADC (83 %) exceeded reader specificity (50 %). There was excellent inter-reader agreement for mean FF (ICC = 0.99) and good agreement for mean ADC (ICC = 0.86) measurements.

CONCLUSION

FF and ADC can improve reader specificity to distinguish between malignant and acute or subacute osteoporotic vertebral fractures. As single discriminator, FF was superior to ADC.

Role of diffusion-weighted magnetic resonance imaging in the evaluation of vertebral bone marrow lesions

Purpose

To evaluate the role of diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating vertebral marrow pathologies. To determine the sensitivity, specificity, and threshold apparent diffusion coefficient (ADC) values that can aid in the differentiation of malignant from benign bone marrow lesions.

Material and methods

This observational study included 100 patients, who underwent MRI examination with a 1.5 Tesla scanner. The ADC values of normal and pathological vertebrae were estimated, and the threshold ADC values were computed by receiver operating characteristic (ROC) analysis. The results were correlated with histopathological diagnosis, clinical follow-up, and other investigations. Statistical analysis was done by employing unpaired two-tailed Student’s t-test and the p-value of < 0.05 was deemed as statistically significant.

Results

Vertebral bone marrow lesions had a male predominance and there was a predilection towards thoracic and lumbar vertebrae, with L4 being the commonest. Metastasis was the commonest lesion, followed by spondylodiscitis. The mean ADC value of benign pathologies was significantly greater than malignant pathologies (p < 0.05). The threshold value for the demarcation between benign and malignant pathologies was computed to be 1.21 × 10^-3 mm²/s. DW imaging had sensitivity of 100%, specificity of 92.31%, positive predictive value of 87.5%, and negative predictive value of 100%.

Conclusions

Vertebral marrow lesions can be differentiated as benign or malignant with good sensitivity and specificity with the help of DW-ADC maps.

Synthetic MRI of the lumbar spine at 3.0 T: feasibility and image quality comparison with conventional MRI

Background

Synthetic magnetic resonance imaging (MRI), which can generate multiple morphologic MR images as well as quantitative maps from a single sequence, is not widely used in the spine at 3.0 T.

Purpose

To investigate the feasibility of synthetic MRI of the lumbar spine in clinical practice at 3.0 T.

Material and Methods

Eighty-four patients with lumbar diseases underwent conventional T1-weighted images, T2-weighted images, short-tau inversion recovery (STIR) images, and synthetic MRI of the lumbar spine at 3.0 T. The quantitative and qualitative image quality and agreement for detection of spinal lesions between conventional and synthetic MRI were compared by two radiologists.

Results

The signal-to-noise ratios of synthetic MRI showed an inferior image quality in the vertebrae and disc, whereas were higher for spinal canal and fat on the synthetic T1-weighted, T2-weighted, and STIR images. The contrast-to-noise ratios of the synthetic MRI was superior to conventional sequences, except for the vertebrae–disc contrast-to-noise ratio on T1-weighted imaging ( P =  0.005). Image quality assessments showed that synthetic MRI had greater STIR fat suppression ( P <  0.001) and fluid brightness ( P =  0.014), as well as higher degree of artifacts ( P <  0.001) and worse spatial resolution ( P =  0.002). The inter-method agreements for detection of spinal lesions were substantial to perfect (kappa, 0.614–0.925).

Conclusion

Synthetic MRI is a feasible method for lumbar spine imaging in a clinical setting at 3.0-T MR. It provides morphologic sequences with acceptable image quality, good agreement with conventional MRI for detection of spinal lesions and quantitative image maps with a slightly shorter acquisition time compared with conventional MRI.

Diffusion-Weighted Magnetic Resonance Imaging of Spine

척추영상에 사용되는 확산강조영상의 기술적 측면, 양성과 악성 척추체 압박골절의 구분, 다발성골수종이나 전이암의 발견과 감별진단, 그리고 항암화학요법이나 방사선치료 후 반응 평가에 대해 기술하고자 한다.

Pelvic insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The added value of DWI for characterization

OBJECTIVES

We sought to determine the added value of diffusion-weighted magnetic resonance imaging (DWI) in the differentiation of pelvic insufficiency fracture (PIF) from bone metastasis after radiotherapy in cervical cancer patients.

METHODS

In the present study, 42 cervical cancer patients after radiotherapy with 61 bone lesions (n = 40, PIFs; n = 21, bone metastasis) were included. Conventional MRI and DWI were performed in all patients. For qualitative imaging diagnosis, two sets of images were reviewed independently by three observers, including a conventional MRI set (unenhanced T1-weighted, T2-weighted, and enhanced T1-weighted images) and a DWI set (conventional MRIs, DW images, and ADC maps). The mean ADC value of each lesson was measured on ADC maps. The diagnostic performance was assessed by using the area under the receiver operating characteristic curve (A_z), and sensitivity and specificity were determined.

RESULTS

For all observers, the A_z value and sensitivity of the DWI set showed improvement compared with the conventional MRI set. The observer who had the least experience (3 years) demonstrated significant improvement in diagnostic performance with the addition of DWI; A_z value increased from 0.804 to 0.915 (p = 0.042) and sensitivity increased from 75.0 to 92.5% (p = 0.035). The mean ADCs of the PIFs were significantly higher than the bone metastases (p < 0.001); ADC values > 0.97 × 10^-3 mm²/s yielded an A_z of 0.887, a sensitivity of 92.5%, and a specificity of 76.2%.

CONCLUSIONS

The addition of DWI to conventional MRI improved the differentiation of PIF from bone metastasis after RT in patients with cervical cancer.

KEY POINTS

• DWI showed additive value to conventional MRI in the differentiation of PIF from bone metastasis after RT. • For qualitative diagnosis, the addition of DWI can improve diagnostic performance compared with conventional MRI alone and can particularly improve the sensitivity. • Quantitative ADC assessment showed potential value for identifying PIF from bone metastasis.

Comparison of proton density fat fraction, simultaneous R2*, and apparent diffusion coefficient for assessment of focal vertebral bone marrow lesions

AIM

To investigate the diagnostic performance of proton density fat fraction (PDFF) and simultaneous R2* for focal vertebral bone marrow lesion (VBML) assessment, compared with the apparent diffusion coefficient (ADC).

MATERIALS AND METHODS

One hundred and ninety-two spinal magnetic resonance imaging (MRI) examinations performed in 126 patients with focal VBMLs from March 2016 to November 2018 were reviewed retrospectively. The lesions were divided into metastases and benign VBMLs. The protocol consisted of routine morphological MRI sequences, followed by complex-based chemical shift imaging (CSE)-MRI and diffusion-weighted (DW)-MRI with a 1.5 T system. PDFF, R2*, and the ADC values were compared using the Mann-Whitney U-test. Receiver operating characteristic curve analysis was carried out to assess the diagnostic performance for differentiating metastases from focal benign VBMLs.

RESULTS

PDFF, R2*, and mean ADC values in metastases were significantly lower than those in benign VBMLs (p<0.05). The PDFF (area under the curve [AUC]= 0.968; 95% confidence interval [CI]=0.932-0.988) showed a significantly larger AUC compared with R2* (AUC=0.670; 95% CI=0.599-0.736) and ADC (AUC=0.801; 95% CI=0.738-0.855). The optimal cut-off value of the PDFF for predicting metastases was 9%; this threshold corresponded to a sensitivity of 96.67%, specificity of 90.28%, and accuracy of 94.27%.

CONCLUSION

PDFF is significantly more accurate than ADC and R2* for differentiating focal benign VMBLs from metastases.

Characterization of Insufficiency Fracture and Bone Metastasis After Radiotherapy in Patients With Cervical Cancer Detected by Bone Scan: Role of Magnetic Resonance Imaging

Background: Insufficiency fracture (IF) can show increased uptake on a bone scan (BS). IFs are often misinterpreted as bone metastases if the characteristic "Honda sign" (H-sign) is invisible. The purpose of the present study was to evaluate the utility of magnetic resonance imaging (MRI) alone for the characterization of IF and bone metastasis after radiotherapy in patients with cervical cancer detected by BS. Materials and Methods: Our study included 40 patients with cervical cancer after radiotherapy that showed pelvic emerging increased uptake on a BS during follow-up. Then further MRI examination was performed in all patients. Two radiologists independently reviewed the MR images, and the sensitivity, specificity and accuracy were calculated based on the mean scores. Diagnostic validity of the inter-observer was calculated by using kappa statistics. The gold standard was based on radiologic findings, clinical data and follow-up at least 12 months. Results: A total of 57 emerging bone lesions detected at BS were identified in the reference standard, including 43 IFs and 14 bone metastases. Only 20 patients showed a "H-sign" on the BS images. Using MRI analysis, all lesions detected by BS were found in MRI by both radiologists. On average, the sensitivity, specificity, and accuracy for distinguishing IFs from bone metastases were 95.3% (41/43), 92.8% (13/14), and 94.7% (54/57), respectively. The inter-observer variability was determined to be very good (kappa value = 0.962). Conclusions: MRI is a reliable diagnostic technique for the further characterization of emerging lesions detected by BS, MRI shows great diagnostic efficiency in the differentiation of IF and bone metastasis.

Guidelines for Acquisition, Interpretation, and Reporting of Whole-Body MRI in Myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS)

Acknowledging the increasingly important role of whole-body MRI for directing patient care in myeloma, a multidisciplinary, international, and expert panel of radiologists, medical physicists, and hematologists with specific expertise in whole-body MRI in myeloma convened to discuss the technical performance standards, merits, and limitations of currently available imaging methods. Following guidance from the International Myeloma Working Group and the National Institute for Clinical Excellence in the United Kingdom, the Myeloma Response Assessment and Diagnosis System (or MY-RADS) imaging recommendations are designed to promote standardization and diminish variations in the acquisition, interpretation, and reporting of whole-body MRI in myeloma and allow response assessment. This consensus proposes a core clinical protocol for whole-body MRI and an extended protocol for advanced assessments. Published under a CC BY 4.0 license. Online supplemental material is available for this article.