Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study

The present study received ethical approval from the Medical Ethics Review Committee of our institution and the relevant informed consent form was obtained in accordance with the Declaration of Helsinki. One hundred four participants were enrolled from 2015 to 2019, according to the following criteria:1) pathologically proved as HCC after hepatectomy; 2) inpatients who have comprehensive clinic materials; 3) performed preoperative Gd-EOB-DTPA-enhanced MRI. The clinic data of the 104 participants were recorded in the Table 1, containing age, gender, alpha fetoprotein (AFP), alamine aminotransferase (ALT), aspartate transaminase (AST), ALT\AST, total bilirubin (TBIL), direct bilirubin and indirect bilirubin.

Exclusion criteria included:1) participants have been treated (transplantation, resection, ablation or embolization) before MR examination; 2) clinical data (AFP, ALT, AST, TBIL, direct bilirubin and indirect bilirubin) or pathological results were incomplete; 3) the lesions were not clearly displayed on the images due to the artifact.

All MRI examinations were performed using 3.0 T MRI machine (Siemens Magnetom Verio 3.0 T; Siemens Magnetom Skyra 3.0 T; GE Signa HDxt 3.0 T) with a dedicated phased-array body coil. A standard abdominal MRI protocol containing following sequences were acquired: 1) Axial T2-weighted: TR = 3260 ms, TE = 105 ms, slice thickness 7 mm, intersection gap 1.4 mm, field of view (FOV) 210 mm × 380 mm; 2) In-phase and out-of-phase axial T1-weighted imaging: TR = 4.16 ms, TE = 2.58 ms (in-phase), TE = 1.35 ms (out-phase), slice thickness 5 mm, intersection gap 1 mm, FOV 210 mm × 380 mm; 3) Diffusion-weighted imaging (DWI, b = 50, 800 s/mm²) performed with a free-breathing single-shot echo-planar technique, TR 5300 ms, TE 57 ms, slice thickness 7 mm, intersection gap 1.4 mm, FOV 210 mm × 380 mm; corresponding ADC maps were calculated automatically by a built-in software; and 4) Contrast enhanced MRI, a three-dimensional (3D) gradient echo sequence with volumetric interpolated breath-hold examination (VIBE): TR 4.18 ms, TE 1.93 ms, slice thickness 3 mm without intersection gap, FOV 210 mm × 380 mm. Gd-EOB-DTPA (Primovist, Bayer Healthcare, Berlin, Germany) was used by 0.2 ml/kg with an injection rate of 2 ml/sec. Hepatic arterial phase (AP), portal venous phase (PVP), equilibrium phase (EP) and hepatobiliary phase (HBP) images were obtained.

The MRI images were reviewed in the picture archiving and communication system (PACS). Experienced radiologists, who were blinded to the pathological results, evaluated the MRI imaging features of the HCC. The imaging features of MRI (arterial enhancement, capsule appearance, the intensity of HBP, the margin and diameter of the tumor, intralesional fat, intratumoral vessel and etc.) were selected referring to the Liver Imaging-Reporting and Data System (LI-RADS 2017) (https://​www.​acr.​org/​Clinical-Resources/​Reporting-and-Data-Systems/​LI-RADS) [19].

MaZda software (version 4.6, quantitative texture analysis software, available from http://​www.​eletel.​p.​lodz.​pl/​mazda/​) was used for texture analysis. All images were transformed into Bitmap (BMP) format considering for the application compatibility of MaZda. An experienced radiologist manually portrayed the region of interest (ROI) of the lesion on the slice which contained the maximum proportion of tumor. One hundred four ROIs (one ROI for each patient) on HBP images were analyzed firstly. Subsequently, the ROIs were copied onto T2, AP and EP images. Then, texture features were extracted and analyzed. The texture features could be grouped into grey-level histogram, the grey-level co-occurrence matrix (GLCOM), the grey-level run-length matrix (GLRLM) and wavelet transform. A grey-level histogram indicated how many pixels of an image shared the same grey level. GLCOM was a statistical method of examining image texture, considering the spatial relationship, by calculating how often pairs of pixel with specific values, which could not provide information about shape. The GLRLM gave the size of homogeneous runs for each grey level. Wavelet transforms were a mathematical means for performing signal analysis when signal frequency varied over time. Wavelet transform coefficients could be computed. More detailed texture features were listed in Table 2. Feature selection algorithms included Fisher coefficient, mutual information [MI], and classification error probability combined with average correlation coefficients [POE + ACC]. Ten texture features were extracted by each of these algorithms. In order to enhance the discriminability, these three methods were combined, called “FPM”, by which 30 texture features were extracted in total.

Histopathological evaluation was available after hepatectomy for the lesions. The specimens were routinely prepared with 4% formaldehyde. The specimens were evaluated by two experienced pathologists who were blind to MRI information. The eight slices of each lesion were analyzed and evaluated with slices ranging from 0.3 cm to 2.0 cm depending on the size of the lesion. The Edmondson-Steiner grade was used to categorize all the specimens. According to the differentiation degree of tumor cells, HCC were categorized into grades I to IV. Edmonson grade I and part of grade II was corresponding with well-differentiated HCC, Edmonson grade II and part of grade III was corresponding with moderately-differentiated HCC, grade III and part of grade IV was poorly-differentiated HCC, and grade IV was undifferentiated HCC. The specimens were stained with Glypican-3 (GPC-3) antibodies. The results of immunohistochemical staining were considered positive if more than 10% of the tumor cells showed cytoplasmic staining, otherwise the results were considered negative.

The statistical analysis was performed using Statistical Product and Service Software (SPSS ver. 20.0, Chicago, IL). In present study, the group differences of continuous variables in abnormal distribution, such as age, ALT, AST, ALT\AST and texture features, were analyzed by Mann-Whitney U test. The difference of texture features between poorly-, moderately- and well-differentiated HCC were analyzed by Kruskal-Wallis H test. The group differences of categorical variables were analyzed by Pearson Test when the sample size was over 40 and the minimal expected frequency was over 5. Otherwise, the correction formula of chi-squared test would be chosen. And the R × C table was used when the dependent variable was over 2. In order to evaluate the diagnostic accuracy of texture features derived from T2, HBP, AP, and EP, the receiver operating characteristic (ROC) analysis was performed and the area under the curve (AUC) was calculated by MedCalc (MedCalc statistical software, ver.15.8). The correlation between texture features and differentiated degree of HCC was analyzed by Spearman’s correlation coefficient. A p value less than 0.05 was considered statistically significant. And Bonferroni correction was used to adjust p values in multiple comparisons.

The B11, a module of MaZda (version 4.6), provided four analyzing ways - principal component analysis (PCA), linear discriminant analysis (LDA), nonlinear discriminant analysis (NDA) and raw data analysis (RDA), to classify and analyze the texture features. The B11 implemented 1-NN classifier for non-linear supervised classification [20]. The misclassification rate was defined as total false samples divided by the total samples and the ratio indicated that the estimated group was different from the observed group. According to the misclassification rate, the classification results were separate into four levels: excellent (misclassification rates ≤10%), good (10% < misclassification rates ≤20%), moderate (20% < misclassification rates ≤30%), fair (30% < misclassification rates ≤40%), and poor (misclassification rates > 40%) [21].

There were 37 patients with poorly-differentiated HCC, 43 with moderately-differentiated HCC, and 24 with well-differentiated HCC in present study. As showed in Table 1, there were no significant differences for age and gender among the groups (p > 0.05). There were significant differences for AFP and ALT value between the poor- and well-differentiated HCC (p = 0.001, 0.006, respectively). The ALT was statistically different between well- and moderately-differentiated HCC (p = 0.008). Fifty-one participants were with GPC-3, among which, 20 were with poorly-differentiated HCC, 20 with moderately and 11 with well-differentiated HCC. There was no significant difference of GPC-3 expression among poorly-, well- and moderately-differentiated HCC, as Table 1 showed (p > 0.05).

The MRI imaging features of l04 patients were demonstrated in Table 3. As the table showed, the tumor size was statistically different between poorly- and moderately-HCC (p = 0.014). However, no statistical differences were found in the margin and the capsule status of the tumor, liver cirrhosis, the HBP hypointensity, intratumoral vessel, intralesional fat, rim-enhancement AP and lymphadenectasis, among poorly-, moderately- and well-differentiated HCC. A typical case of poorly-differentiated HCC was showed in Fig. 1.

As showed in Tables 4, 262 texture features derived from T2, HBP, AP and EP images were obtained and categorized into histogram (n = 10), GLCOM (n = 220), GLRLM (n = 20) and wavelet transform (n = 12). The frequency of each feature category of T2-weighted images and each phase of Gd-EOB-DTPA enhanced images extracted by FPM was showed among poorly-differentiated, well-differentiated and moderately-differentiated HCC. The GLCOM-based texture features were most frequently extracted with three phases for poorly- verse well-differentiated HCC, poorly- verse moderately-differentiated HCC and well- verse moderately-differentiated HCC.

The tissue classification results were demonstrated across the T2, AP, EP and HBP in Table 5. The misclassification rate of NDA was excellent for each phase of the three groups, with the misclassification rate ranging from 3.33 to 14.93%. The misclassification rate of LDA was rank secondly to NDA, with the classification rate range from 4.92 to 33.75%. Both of the misclassification results of RDA and PCA were fair or poor.

The AUC of each texture feature was calculated. The ROC curves of the best combined diagnoses were demonstrated in Figs. 2, 3 and 4. As showed in Fig. 2, the combine AUC value (combining texture features from T2, AP and EP) was 0.812, higher than that of any single texture feature from each phase, to differentiate poorly- from well-differentiated HCC (accuracy = 0.77). As showed in Fig. 3, the combine AUC value was 0.879 (accuracy = 0.85), to differentiate poorly- from moderately-differentiated HCC, and as showed in Fig. 4, the combined AUC value was 0.808 (accuracy = 0.746) to differentiate moderately- from well-differentiated HCC.

The ROC analyses of combined tumor size and texture features were demonstrated in Table 6. “COMBINE” presented the combination of texture features derived from different phases. As showed in the Table 6, the AUC of tumor size was the lowest and the COMBINE AUC value was the highest. With combining tumor size and texture features, the COMBINE AUC values were the same as those without combining tumor size, in poorly- verse moderately-differentiated HCC and poorly- verse well-differentiated HCC, while the COMBINE AUC value was increased from 0.808 to 0.833 in moderately- with well-differentiated HCC (p = 0.314).

Perc.10% was positively correlated with the differentiated degree of HCC in AP (r = 0.276, p = 0.005), while 135dr_ShrtREmp was negatively correlated with the differentiated degree of HCC in EP phase (r = − 0.305, p = 0.002) and S(3,0) SumEntrp was negatively correlated with the differentiated degree of HCC in T2 phase (r = − 0.306, p = 0.02).