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

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

To investigate the correlation between DWI, intravoxel incoherent motion (IVIM), and hypoxia-inducible factor 1-alpha (HIF-1α) expression in a nude mouse model of rhabdomyosarcoma based on imaging and pathological comparisons. Methods: Human rhabdomyosarcoma-derived (RD) cells were inoculated into the right thigh muscle of 20 BALB/c female nude mice. Mice were imaged using 3.0 Tesla MRI system. T 1 -weighted imaging, T 2 -weighted imaging, DWI, and IVIM images were obtained. ADW4.7 (GE Healthcare, ChicagoAQ34, IL, USA) was used for image processing of ADC, D slow , D fast , and f values. All parameter values were independently analyzed by 2 observers. Immunohistochemistry of HIF-1α was performed. We used a specific image-pathology comparison method to ensure correct overlap between the image plane and the pathological section. Mann-Whitney U test or independent sample t test, Pearson or Spearman correlation test, the intragroup correlation coefficient, Kolmogorov-Smirnov test, and receiver operating characteristic curve were used. The correlation between DWI and intravoxel incoherent motion parameter values and HIF-1α expression was determined. Results: There were 10 mice in the low-expression group and 7 in the high-expression group. The ADC and D slow values were negatively correlated with HIF-1α with correlation coefficients of −0.491 and − 0.702 (P = 0.045 and 0.002). The f value positively correlated with HIF-1α expression (r = 0.485, P = 0.048). ADC, D slow , and f were significantly different between the high-HIF-1α expression tumors and the low-HIF-1α expression tumors. ADC showed the best predictive performance among all parameters (area under the curve = 0.652, sensitivity = 83.3%, specificity = 63.6%). Conclusion:The parameter values of DWI and intravoxel incoherent motion can be used to evaluate the expression of HIF-1α in rhabdomyosarcoma. ADC, D slow , and f value showed correlation with the expression of HIF-1α.

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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.