Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in China (1) and the third leading cause of cancer to death worldwide (2). The diagnosis and assessments for HCC are benefited much from the existing medical imaging techniques, such as ultrasound, CT, MRI and PET. Liver biopsy is considered as the golden standard for the assessment of hepatic diseases, although it is invasive and easily affected by the sampling error (3). It is a hot topic to find a reproducible and repeatable method to assist the diagnosis of malignancy.
Medical imaging plays a crucial role in the diagnosis and identification of the HCC (4). 3.0T MR is recognized to be better than 1.5T MR and CT in characterizing soft tissue (5) or solid organ tumors (6,7). However, the MR images quality are affected by many factors, such as tissue cellularity, vascularity, necrosis and the respiratory movements, etc. It is not an easy task to decipher different MRI sequences (or parameters) for inexperienced radiologists. Radiomics is an emerging field in quantitative imaging that uses high-dimensional imaging features to objectively and quantitatively describe tumour phenotypes (8,9). Recent studies have reported that radiomic features extracted from multiple sequences MR images can be used for diagnostics (10), predicting treatment response (11) and histological grade (12) in HCC. Meanwhile, radiomics can characterize intratumour heterogeneity and can be served as a biomarkers of tumor (13). However, radiomics features are sensitive to noise and imaging protocols (14).
Diffusion weighted images (DWIs) and apparent diffusion coefficient (ADC) values with multiple b-values are often used to assist qualitative and quantitative diagnosis of malignancy from benign tumors (15,16). Radiomic features extracted from DWIs showed a good accuracy in distinguishing benign and malignant ovarian lesions (17) and appear useful for prostate cancer detection and Gleason scores (GS) assessment (18). As yet, there was no widely-accepted standardized algorithm about the b-value in HCC DW imaging, as it is easily affected by many factors. Vascular perfusion can increase the sensibility of HCC at low b-value DWI (19). Nevertheless, high b-value DWI showed a good specificity of dysplastic nodules of HCC (20), although signal to noise ratio (SNR) will decrease in high b-value (21). Therefore, due to the lack of any standardized MR imaging parameters, variability may be inevitable in DWI radiomic features of HCC.
The aim of this study was to experimentally investigate the variability of radiomic features extracted from multi-b-values DWIs in HCC and explore stable radiomic features to characterize HCC.
Informed consent was obtained from all patients, and Jinan Military General Hospital Ethics committee approved the study protocol (No: 2015-353). Fifty candidates with HCC and 12 healthy volunteers were randomly selected in this study between January 2016 and December 2017 in Jinan Military General Hospital. All cases experienced the following MR sequences scans: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), Gd-DTPA contrast-enhanced and intravoxel incoherent motion-diffusion weighted imaging-echo planar imaging (IVIM-DWI-EPI). All HCC patients were diagnosed by experienced radiologists and proved by pathology. Eligibility criteria were as follows: (I) cases were diagnosed with HCC by three radiologists; (II) at least two slices showed visible lesions. Exclusion criteria were as follows: (I) have experienced chemoradiotherapy, surgical resection or other treatment; (II) images artifacts; (III) data partial missing; (IV) ascites; (V) liver cancer metastasis. Finally, 34 HCC patients (23 males and 11 females aged from 42 to 84 years with mean age 59.8 years) and 12 healthy volunteers (7 males and 5 females aged from 31 to 78 years, with mean age 54.6 years) were included in this study.
Before scanning, all cases underwent a bolus injection of 0.02 mmol/kg body weight Gd-DTPA (Primovist, Bayer-Schering Pharma, Berlin, Germany) at a rate of 2.5 mL/s and immediately followed by a 15 mL saline flush. Every person was scanned on 3.0T scanner (GE 3.0T Discovery MR 750) using an eight-channel phase array coil. The MR sequences parameters were as follows: (I) axial T1WI: repetition time (TR) =3.7 ms, echo time (TE) =1.7 ms, slice thickness =5.2 mm, interslice gap =−2.6 mm and NEX =0.7; (II) axial T2WI: TR =6,667 ms, TE=1.7 ms, interslice gap =1 mm and NEX =2.5; (III) IVIM-DWI-EPI: TR =5,714 ms, TE =66.1 ms, slice thickness =7 mm, interslice gap =1 mm, NEX =2, MB =10 (b-values: 0, 20, 50, 100, 200, 400, 800, 1,000, 1,200, 1,500 s/mm2); (IV) axial contrast-enhanced imaging (TR =3.7 ms, TE =1.7 ms, interslice gap =−2.6 mm and NEX =0.7) and coronal contrast-enhanced imaging (TR =3.6 ms, TE =1.7 ms, interslice gap =−2.0 mm and NEX =0.7).
Volume of interests (VOIs) segmentation
VOIs were delineated manually by one radiologist with 20 years experiences in MIM software (www.mimsoftware.com): (I) for HCC trail, the expert delineated gross tumor volume (GTV) on the first DWI sequence (b=0 s/mm2) referenced by T1WI, T2WI and contrast-enhanced imaging sequence; (II) for healthy volunteer trail, avoiding great vessels, 3 VOIs with 14 mm in height and approximately 20 mm in diameter were defined in parenchyma at II/III, V/VI and VII hepatic segments; (III) VOIs in DWI (b=0 s/mm2) were mapped to the rest 9 b-values DWI using function clone in the MIM software.
Extraction of Radiomic features
Radiomic features were automatically calculated using 3D Slicer Radiomics software (
Firstly, mean value (mean) and standard deviation (SD) for each feature were calculated across ten b-values DWIs. Then, to evaluate the variability of the features, percentage coefficient of variation (%COV) was calculated for each radiomic feature in this paper. %COV <30 was considered as low variation, 30≤ %COV <50 was considered as moderate variations and %COV ≥50 was considered as large variations. The definition of %COV (22) was as follows:
Secondly, using exponential model or polynomial fit model, graphs were drawn for each feature and different b-values. The equations were as follows:
F is the feature value, F0 is the feature value at b=0 s/mm2, and c is the decay or growth constant. R-square ≥0.9 was considered a better fitting degree. Spearman correlation coefficient (r) between b-value and radiomic feature value was computed for each graph. We adopted Karlik’s quoted guidelines for interpretation for the correlation measures (23).
If the absolute value of r (|r|) was:
- Less than 0.2—negligible correlation;
- Between 0.2 and 0.4—weak correlation;
- Between 0.4 and 0.7—moderate correlation;
- Between 0.7 and 0.9—strong correlation;
- Between 0.9 and 1—very strong correlation.
where x and y are the vector of radiomic feature values derived from two b-value DWIs, ρ, σ and µ are correlation coefficient, standard deviation and mean value of vector x and y, respectively. CCC value ≥0.8 was considered as reproducible.
Finally, statistical analysis was performed using SPSS software 22.0 (SPSS, Chicago, IL, USA). Independent-sample t-test was used to compare the differences of %COV between two groups. Paired Student’s t-tests or Wilcoxon rank tests were used to compare the differences of CCC between different b-values. P<0.05 was considered statistically significant. The flow chart of this study was depicted in Figure 1.
Variation of radiomic features
The name of feature index was consistent in Figure 2 and in Table 1. Figure 2 demonstrated the %COV value of each radiomic feature in HCC group and control group. In HCC group, 26% features showed low variations (%COV <30), including 2 IH features, 7 GLCM features, 6 GLRLM features and 4 GLSZM features. Twenty-eight percent features showed moderate variations (30≤ %COV <50), including 11 IH features, 6 GLCM features, 2 GLRLM features and 2 GLSZM features. Forty-six percent features showed large variations (%COV ≥50), including 6 IH features, 10 GLCM features, 8 GLRLM features and 10 GLSZM features. In IH feature group, 17 (89%) features showed moderate or large variations, and 2 (11%) features showed low variations. In GLCM feature group, 16 (70%) features showed moderate or large variations, and 7 (30%) features showed low variations. In GLRLM feature group, 10 (62%) features showed moderate or large variations, and 6 (38%) features showed low variations. In GLSZM feature group, 12 (75%) features showed moderate or large variations, and 4 (25%) features showed low variations. In control group, 28% features showed low variations (%COV <30), including 1 IH features, 9 GLCM features, 7 GLRLM features and 4 GLSZM features. Nineteen percent features showed moderate variations (30≤ %COV <50), including 8 IH features, 3 GLCM features, 1 GLRLM features and 2 GLSZM features. Fifty-three percent features showed large variations (%COV ≥50), including 10 IH features, 11 GLCM features, 8 GLRLM features and 10 GLSZM features. In IH feature group, 18 (95%) features showed moderate or large variations, and 1 (5%) features showed low variations. In GLCM feature group, 14 (61%) features showed moderate or large variations, and 9 (39%) features showed low variations. In GLRLM feature group, 9 (56%) features showed moderate or large variations, and 7 (44%) features showed low variations. In GLSZM feature group, 12 (75%) features showed moderate or large variations, and 4 (25%) features showed low variations. The intersection of low variation features was 18 (24%) in the two sets and the mean value of each feature was listed in Table 2. Among them, 12 (16%) radiomic features showed a statistically signiﬁcant difference between HCC group and control group.
Influence of b-value
Table 3 provided the fitting degree for two sets. For HCC and control group, 66 (89%) and 48 (65%) features obtained a better fitting degree, respectively. Among them, the number of better fitting features in IH feature group, GLCM feature group, GLRLM feature group and GLSZM feature group was 17 (23%), 19 (26%), 16 (22%) and 14 (19%) for HCC sets, respectively. The number of better fitting features in IH feature group, GLCM feature group, GLRLM feature group and GLSZM feature group was 14 (19%), 13 (18%), 8 (11%), and 13 (18%) for control sets, respectively. Moreover, the intersection of better fitting features was 47 (64%) in the two sets.
Figure 3 illustrated the exponential fitting results or the polynomial fitting results between each feature and different b-values. The correlation coefficient could be seen in Table 3. In HCC group, the number of very strong correlation, strong correlation, moderate correlation, weak correlation and negligible correlation features were 0 (0%), 0 (0%), 65 (88%), 2 (3%) and 7(9%), respectively. The number of positive correlation (r>0.4) and negative correlation (r<−0.4) radiomic features were 20 (27%) and 45 (61%) in HCC group, respectively. In control group, the number of very strong correlation, strong correlation, moderate correlation, weak correlation and negligible correlation features were 0 (0%), 63 (85%), 8 (11%), 3 (4%) and 0 (0%), respectively. The number of positive correlation (r>0.4) and negative correlation (r<−0.4) radiomic features were 21 (28%) and 50 (68%), respectively. The correlation analysis results for each feature in four radiomic features groups were listed in Table 4.
Reproducibility of radiomic features extracted from nearby b-values DWIs
Figure 4 illustrated that radiomic features extracted from b=0, 20, 50, 100, 200 s/mm2, and b=1,000 s/mm2 DWIs acquired high repeatability. Meanwhile, features extracted from nearby b-value DWIs showed high repeatability. Oppositely, features extracted from faraway b-value DWIs usually showed low repeatability.
DWIs are generally used to describe the random motion of water molecules. Different histological degree of HCC on the basis of nuclear and structural atypia, high cellular density, cytotoxic edema, and high viscosity can restrict water diffusion. Therefore, most malignant lesions usually show hyperintense in DWIs. DWIs have been widely used in the diagnosis, histopathological grading and prognosis evaluation of abdominal malignant tumor (15,16,26). However, tissue parameters derived from DWIs strongly depend on b-values distributions and organ-specific (19). Due to the variety in MR imaging parameters, variability may be inevitable in imaging biomarker represented by radiomics.
In this study, we demonstrated that the value of radiomic features showed significant correlation with different b-values in DWIs. IH features describe the distribution of voxel intensities. We observed most IH features existed large variation, which indicated that IH features might be strongly influenced by b-value. It was consistent with the previous studies (26,27). In fact, signal intensity of the lesions could enhance along with the increases of b-value in DWIs, which might explain the above phenomenon. Difference with IH features, texture features described statistical interrelationships between voxels with similar contrast values and could provide a measurement of intratumor heterogeneity (22). Density of liver parenchyma, cells geometric arrangements and extracellular matrix in HCC were the most influence factors with the increases of b-values (28). It might result in the phenomenon that most texture features were low variations in different b-value DWIs.
Whether in the HCC group or the control group, we found most radiomic features values had correlation with the increases of b-value and negative correlation was the main modality. It might be attributed that the signal intensity in the vessel would cause a fast decay with increases of b-values. Besides, most features have similar tendency between two groups (Figure 3). Twelve radiomic features had statistically differences between groups and might be used as imaging biomarkers to distinguish HCC and normal liver.
It should be noted that features usually showed high repeatability in nearby b-value DWIs and low repeatability in faraway b-value DWIs (Figure 4). Moreover, we also observed reproducible radiomic features in b=1,000 s/mm2 DWIs. This indicated that nearby b-value DWIs might exist interchangeability in HCC characteristic.
To our knowledge, the reproducibility of MR-based radiomic features has seldom been investigated. Understanding the stability of MR-based radiomic features between different scanning parameters can help identifying reliable features for radiomic applications. In this research, we attempted to detect the impacts of different b-value to radiomic features in HCC DWIs. One limitation of the present study was that we had not employed the ADC maps, which would be added in the future study. Another limitation is that to acquire a reliable result, the noninvasive identification of HCC and healthy liver using imaging biomarker should involve sufficient patient data. In the future work, we will extend the study and seek dependable imaging biomarkers in distinguishing different kinds of liver nodules using radiomic technology.
In this study, we found radiomic features had variability in different b-value DWIs. The value of most features had positive or negative correlation along with the increases of b-value. Features extracted from nearby b-value DWIs usually showed high repeatability. Twelve radiomic features can be used to identify HCC and normal liver.
Funding: This research was supported by the Shandong Natural Science Foundation (No. ZR2017PH071) and the WBE Liver Fibrosis Foundation, China (Grant No. CFHPC2019027) and the National Key Research and Development Program of China (No. 2017YFC0113202) and the National Natural Science Fund of China (No. 81472811) and the Key Research and Development Program of Shandong Province (No. 2018GSF118006).
Conflicts of Interest: The authors have no conflicts of interest to declare.
Ethical Statement: Informed consent was obtained from all patients, and Jinan Military General Hospital Ethics committee approved the study protocol (No: 2015-353).
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