Tumor grading of soft tissue sarcomas: Assessment with whole-tumor histogram analysis of apparent diffusion coefficient

Purpose: To evaluate the usefulness of whole-tumor ADC histogram analysis based on entire tumor volume in determining the histologic grade of STS (soft tissue sarcoma)s. Methods: From January 2015 to December 2020, 53 patients with STS who underwent preoperative magnetic resonance imaging, including diffusion weighted imaging and ADC maps (b = 0 and 1400 s/mm²), within 1 month before surgical resection were included in the study. Regions of interest were drawn on every section of the ADC map containing tumor and were summated to derive volume-based histogram data of the entire tumor. Histogram parameters were correlated with histologic tumor grade using Kruskal-Wallis test and compared between high-(grade II and III) and low-grade STSs (grade I) using Mann-Whitney U test. Multivariable logistic regression analysis was applied to identify significant histogram parameters for high-grade STS prediction, and receiver operating characteristic curves (AUC) were constructed to determine optimum threshold. Results: Eight patients with low-grade STS (15.1%) and 45 with high-grade STS (26.4% [14/53] for grade II; 58.5% [31/53] for grade III) were included. High-grade STS showed positive skewness and low-grade STS showed negative skewness (0.503 vs −0.726, p=.001). High-grade STS showed lower mean ADC (p =.03) and 5th to 50th percentile values (p ≤. 03) than those of low-grade STS. Positive skewness was an independent predictor of high-grade STS (odds ratio: 6.704, p=.002) with 84.4% sensitivity and 87.5% specificity (cut-off values > -0.1757, AUC = 0.842). Conclusion: Skewness is the most promising histogram parameter for discriminating high-grade from low-grade STS. The mean ADC values and lower half of percentile values are helpful for differentiating high from low-grade STSs.