Background High cellularity and abnormal interstitial structures are some of the unfavorable factors that affect the treatment outcomes and survival of rhabdomyosarcoma (RMS) patients. Purpose To explore the correlation between diffusion‐weighted imaging (DWI) and intravoxel incoherent motion (IVIM) with quantitative histopathologic features in a murine model of RMS. Study Type Prospective. Animal Model Murine model of RMS (31 female BALB/c nude mice). Field Strength/Sequence 3.0 T; fast spin‐echo (FSE) T1‐weighted imaging, fast relaxation fast spin‐echo (FRFSE) T2‐weighted imaging, DWI PROPELLER FSE imaging sequence, and IVIM echo planar imaging sequence; 10 different b‐values (0, 50, 100, 150, 200, 400, 600, 800, 1000, and 1200 s/mm2). Assessment Magnetic resonance imaging (MRI) was performed after 30–45 days of implantation. The following MRI parameters were calculated: apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo‐diffusion coefficient (D*), and perfusion fraction (f). Histopathologic features, which contained nuclear, cytoplasmic, and stromal fractions, and the nuclear‐to‐cytoplasmic ratio within the tumor were measured using image‐based segmentation. Statistical Tests Pearson's correlation, multiple linear regression analysis, and receiver operating characteristic curve analysis were performed. A P < 0.05 was considered statistically significant. Results The ADC value showed moderate negative correlation with nuclear fraction (r = −0.540), and moderate positive correlation with stroma fraction (r = 0.474). The D value showed moderate negative correlation with nuclear fraction (r = −0.491), and moderate positive correlation with stroma fraction (r = 0.421). The f value showed a moderate negative correlation with stroma fraction (r = −0.423). The D value showed the best diagnostic ability. The optimal cut‐off D value of 0.460 was associated with 77.8% sensitivity and 68.2% specificity (area under the curve, 0.747). Data Conclusion The ADC, D, and f values obtained from DWI and IVIM images showed moderate correlation with the quantitative histopathologic features in a murine model of RMS. Level of Evidence 1 Technical Efficacy Stage 3

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