Diagnostic Performance of Diffusion MRI for differentiating Benign and Malignant Nonfatty Musculoskeletal Soft Tissue Tumors: A Systematic Review and Meta-analysis

Abstract

Objective: To evaluate the diagnostic performance of standard diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for differentiating benign and malignant soft tissue tumors (STTs). Materials and methods: A thorough search was carried out to identify suitable studies published up to September 2020. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic (SROC) curve were calculated using bivariate mixed effects models. A subgroup analysis was also performed to explore the heterogeneity. Results: Eighteen studies investigating 1319 patients with musculoskeletal STTs (malignant, n =623; benign, n =696) were enrolled. Thirteen standard DWI studies using the apparent diffusion coefficient (ADC) showed that the pooled SEN and SPE of ADC were 0.80 (95% CI: 0.77-0.82) and 0.63 (95% CI: 0.60-0.67), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.806. The subgroup analysis indicated that the percentage of myxoid malignant tumors, magnet strength, study design, and ROI placement were significant factors affecting heterogeneity. Four IVIM studies showed that the AUCs calculated from the SROC curves of the parameters ADC and D were 0.859 and 0.874, respectively. The AUCs for the IVIM parameters pseudo diffusion coefficient (D*) and perfusion fraction (f) calculated from the SROC curve were 0.736 and 0.573, respectively. Two DKI studies showed that the AUCs of the DKI parameter mean kurtosis (MK) were 0.97 and 0.89, respectively. Conclusion: The DWI-derived ADC value and the IVIM DWI-derived D value might be accurate tools for discriminating musculoskeletal STTs, especially for non-myxoid SSTs, using more than two b values, with maximal b value ranging from 600 to 800 s/mm 2 , additionally, a high-field strength (3.0 T) optimizes the diagnostic performance.

Features of ophthalmic, magnetic resonance imaging, and histopathology of a feline case of idiopathic sclerosing orbital pseudotumor

“…DWI and ADC maps were used in human medicine helping distinguish orbital tumour and inflammation, or differentiate benign and malignant soft tissue tumours. Increased cell density and decreased extracellular space in malignant tumours will hinder the movement of water molecules and therefore have decreased ADC values (Wang et al., 2021). Also, the tumour stroma produced by activated fibroblasts was found to be inversely correlated to ADC value (Hauge et al., 2017).…”

Magnetic Resonance Imaging Biomarkers of Bone and Soft Tissue Tumors

“…32,35 There is consensus, however, that lower ADC values are more likely to indicate malignancy. [36][37][38][39][40][41] DWI with ADC mapping offers value for MSK tumor characterization in three particular scenarios: First, showing significantly lower ADC values for malignant compared with benign lesions, DWI enhances the diagnostic accuracy of conventional MRI of soft tissue masses with indeterminate tumor characteristics by conventional imaging and clinical features. [42][43][44] Moreover, Del Grande et al reported a comparable diagnostic performance between noncontrast MRI protocols with DWI and contrast-enhanced protocols for indeterminate soft tissue masses.…”

Advances in The Application and Research of Magnetic Resonance Diffusion Kurtosis Imaging in The Musculoskeletal System

“…[6][7][8][9] DKI was first used to track changes in gray matter in the brain but has since been extensively utilized in the research on malignancies of the nervous system, digestive system, breast, prostate, and other disorders of the musculoskeletal system. [10][11][12][13][14] The equation 7 for DKI analysis of the non-Gaussian diffusivity of water is as follows:…”

“…In addition to the monoexponential model, DKI can describe other fitted diffusion data models, such as the biexponential model which distinguishes between the quick and slow diffusion rate of water molecules, and the stretched‐exponential model which categorizes water diffusion into multiple Gaussian intervals through widely distributed diffusivities, more accurately reflecting the degree of diffusion restriction and inhomogeneity of water molecules 6–9 . DKI was first used to track changes in gray matter in the brain but has since been extensively utilized in the research on malignancies of the nervous system, digestive system, breast, prostate, and other disorders of the musculoskeletal system 10–14 …”