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

Abstract

PurposeTo 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. MethodsFrom 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. ResultsEight 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). ConclusionSkewness 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.