Original Article
Variability of radiomic features extracted from multi-b-value diffusion-weighted images in hepatocellular carcinoma
Abstract
Background: 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).
Methods: The research population was composed of 34 HCC patients and 12 healthy volunteers. At 3.0 T 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.
Results: 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. 12 radiomic features can be used to identify HCC and normal liver.
Conclusions: 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.
Methods: The research population was composed of 34 HCC patients and 12 healthy volunteers. At 3.0 T 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.
Results: 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. 12 radiomic features can be used to identify HCC and normal liver.
Conclusions: 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.
