Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma

Reliable and meaningful radiomic features is extremely crucial to characterize tumor phenotypes. This study was designed to experimentally evaluate the variability of radiomic features extracted from different b-values diffusion-weighted images (DWIs) in hepatocellular carcinoma (HCC).

The research population was composed of 34 HCC patients and 12 healthy volunteers. At 3.0T MR scanner, with the identical imaging protocols, all cases underwent the following sequences at 10 b-values ranging from 0 to 1,500 s/mm²: T1WI, T2WI, multiple phases contrast-enhanced and intravoxel incoherent motion-DWI scans. For HCC trail, gross tumor volume (GTV) were manually delineated by an experienced radiologist at the b=0 s/mm² DWI sequence. For healthy volunteers trail, 3 cylindric regions of interest (ROIs) with 14 mm in height and approximately 20 mm in diameter were defined in parenchyma at II/III, V/VI and VII hepatic segments. Using 3D Slicer Radiomics software (www.slicer.org), we extracted 74 radiomic features, including 19 first-order statistical features and 55 texture features for each case sequence. Percentage coefficient of variation (%COV) was applied to evaluate the stability of each feature and %COV <30 was considered as low variation. Furthermore, to observe the trend for radiomic features value in various b-values DWIs, an exponential or polynomial model was used. Finally, concordance correlation coefficient (CCC) was applied to assess the reproducibility of radiomic features between different b-values DWIs.

The value of intensity histogram features and texture features derived from DWIs showed a dependency on the b-values in HCC. The low variations (%COV <30), moderate variations (30≤ %COV <50) and large variations (%COV ≥50) radiomic features accounted for about 26%, 28%, and 46%, respectively. The exponential and polynomial model indicated that about 70% radiomic features showed positive or negative dependence on b-values and about 4% radiomic features showed little dependence. We acquired a better fitting results in HCC group (the mean value and standard deviation of R-square were 0.958±0.096 and 0.896±0.071, P<0.05). Moreover, we found radiomic features extracted from nearby b-values (b=0, 20, 50, 100, 200 s/mm² and b=1,000 s/mm²) of DWIs showed a high reproducibility. Twelve radiomic features can be used to identify HCC and normal liver.

Being influenced by different b-values, radiomic features tested here exist variability in HCC DWIs. Most features are unstable and extremely dependent on b-values in DWIs. Meanwhile, the research revealed that reproducible features can be extracted by nearby b-values DWIs.