Magnetic resonance diffusion kurtosis imaging (DKI) is an emerging magnetic resonance imaging (MRI) technique that can reflect microstructural changes in tissue through non‐Gaussian diffusion of water molecules. Compared to traditional diffusion weighted imaging (DWI), the DKI model has shown greater sensitivity for diagnosis of musculoskeletal diseases and can help formulate more reasonable treatment plans. Moreover, DKI is an important auxiliary examination for evaluation of the motor function of the musculoskeletal system. This article briefly introduces the basic principles of DKI and reviews the application and research of DKI in the evaluation of disorders of the musculoskeletal system (including bone tumors, soft tissue tumors, spinal lesions, chronic musculoskeletal diseases, musculoskeletal trauma, and developmental disorders) as well as the normal musculoskeletal tissues.
Evidence Level
5
Technical Efficacy
1
Over the past two decades, considerable efforts have been made to develop non‐invasive methods for determining tumor grade or surrogates for predicting the biological behavior, aiding early treatment decisions, and providing prognostic information. The development of new imaging tools, such as diffusion‐weighted imaging, diffusion kurtosis imaging, perfusion imaging, and magnetic resonance spectroscopy have provided leverage in the diagnosis of soft tissue sarcomas. Artificial intelligence is a new technology used to study and simulate human thinking and abilities, which can extract and analyze advanced and quantitative image features from medical images with high throughput for an in‐depth characterization of the spatial heterogeneity of tumor tissues. This article reviews the current imaging modalities used to predict the histopathological grade of soft tissue sarcomas and highlights the advantages and limitations of each modality.
Level of Evidence
5
Technical Efficacy
Stage 2
Background: Reactive stroma is recognized as one of the independent prognostic factors in prostate cancer (PCa). Intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) may be useful for assessing the reactive stromal grade (RSG). Purpose: To investigate whether IVIM and DKI models can evaluate RSG in PCa patients. Study Type: Retrospective. Subjects: A total of 56 PCa patients aged 73 years on average confirmed by MRI and transrectal ultrasound (MRI/TRUS) fusion biopsy divided into two subgroups (18 high RSG and 38 low RSG).
Background: Diffusion-weighted imaging (DWI) has demonstrated great potential in predicting the expression of tumor cell proliferation and apoptosis indexes. Purpose: To evaluate the impact of four region of interest (ROI) methods on interobserver variability and apparent diffusion coefficient (ADC) values and to examine the correlation of ADC values with Ki-67, Bcl-2, and P53 labeling indexes (LIs) in a murine model of fibrosarcoma. Study Type: Prospective, animal model. Animal Model: A total of 22 female BALB/c mice bearing intramuscular fibrosarcoma xenografts. Field Strength/Sequence: A 3.0 T/T1-weighted fast spin-echo (FSE), T2-weighted fast relaxation fast spin-echo, and DWI PROPELLER FSE sequences. Assessment: Four radiologists measured ADC values using four ROI methods (oval, freehand, small-sample, and wholevolume). Immunohistochemical assessment of Ki-67, Bcl-2, and P53 LIs was performed. Statistical Tests: Interclass correlation coefficient (ICC), one-way analysis of variance followed by LSD-t post hoc analysis, and Pearson correlation test were performed. The statistical threshold was defined as a P-value of <0.05. Results: All ROI methods for ADC measurements showed excellent interobserver agreement (ICC range, 0.832-0.986). The ADC values demonstrated significant differences among the four ROI methods. The ADC values for oval, freehand, small-sample, and whole-volume ROI methods showed a moderately negative correlation with Ki-67 (r = À0.623; r = À0.629; r = À0.642, and r = À0.431) and Bcl-2 (r = À0.590; r = À0.597; r = À0.659, and r = À0.425) LIs, but no correlation with P53 LI (r = 0.364, P = 0.104; r = 0.350, P = 0.120; r = 0.379, P = 0.091; r = 0.390, P = 0.080).
Data Conclusion:The ADC value can be used to evaluate cell proliferation and apoptosis indexes in a murine model of fibrosarcoma, employing the small-sample ROI as a reliable method.
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