Published online Feb 28, 2018. doi: 10.3748/wjg.v24.i8.929
Peer-review started: December 21, 2017
First decision: January 3, 2018
Revised: January 11, 2018
Accepted: January 18, 2018
Article in press: January 18, 2018
Published online: February 28, 2018
Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver. It is the fifth most important cancer worldwide and the third leading cause of cancer-related death. The pathological grade of HCC is heavily associated with the prognosis. However, it is difficult to provide a prediction of accurate preoperative pathological grade of HCC using routine imaging modalities.
Diffusion-weighted imaging (DWI) is a noninvasive approach to sensitively evaluate the small-scale motion of water molecules at the microscopic level. However, it is limited in that the ADC fails to evaluate the water molecular diffusion in tissues precisely. The IVIM approach uses a bi-exponential function to describe the DWI data and it is possible to obtain additional quantitative parameters that describe water diffusivity, perfusion (pseudodiffusion coefficient), and the perfusion fraction of tissues. Recently, IVIM-DWI has been used to investigate the correlation between the parameters involved in the histologic grade of HCC. This study can determine a new imaging technique to assess the histological grade of HCC and predict the patient’s prognosis in clinical practice.
This study investigated the value of the IVIM-derived parameters and conventional DWI-derived parameters for predicting the histological grade of HCC, evaluated the diagnostic efficiency of the parameters in distinguishing the pathological grades of HCC, and assessed the correlation between the parameters and the histological grades. With the help of the parameters, we can determine the pathological grade of HCC without the pathological results of surgery. The pathological grade of HCC is heavily associated with the prognosis. Therefore, we can assess the pathological grade of HCC and predict the patient prognosis, simultaneously.
The present study was to compare IVIM-derived parameters with conventional DWI-derived ADC values for determining the histologic grades of HCC and evaluated the correlation between the parameters and the histological grades. The parameters derived from IVIM-DWI and conventional DWI showed statistical significance in different histologic grades of HCC, and they will have diagnostic value in differentiating the pathological grades, as the correlation was observed between the parameters and grades. The results showed that these parameters are of great significance in the diagnosis of the pathological grades of HCC. DWI-MR will be a new imaging technique to assess the pathological grade of an HCC, which might be helpful in predicting the patient prognosis.
A retrospective study was performed. Sixty-two patients (50 men and 12 women; mean age, 54.31 ± 9.36 years; range, 30-76 years) with surgically confirmed HCC underwent diffusion-weighted magnetic resonance imaging with twelve b values (10-1200 s/mm2). All the tumors were histologically classified according to the major Edmondson-Steiner grade on the final pathologic reports as follows: grade 1 (n = 14), grade 2 (n = 24), grade 3 (n = 24), and grade 4 (n = 0). The apparent diffusion coefficient (ADC), pseudo-diffusion coefficient (D*), pure diffusion coefficient (D), and perfusion fraction (f) were calculated by two radiologists. The IVIM and conventional DWI parameters were compared among different grades by using analysis of variance (ANOVA) and the Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic efficiency of distinguishing between low-grade (G1) and high-grade (G2 and G3) HCC. The correlations between the parameters and the histological grades were assessed by using the Spearman correlation test. Bland-Altman analysis was used to evaluate the reproducibility of the two radiologists’ measurements. The measurement reproducibility between the two observers shows the reliability of parameters, and it makes sure that the results for the study objectives are more persuasive, which is the characteristics and novelty.
The differences in the ADC and D values among the groups with G1, G2, and G3 histological grades of HCC were statistically significant (P < 0.001). The D* and f values had no significant differences among the different histological grades of HCC (P > 0.05). The ROC analyses demonstrated that the D and ADC values had better diagnostic performance in differentiating the low-grade HCC from the high-grade HCC, with areas under the curve (AUCs) of 0.909 and 0.843, respectively, measured by radiologist 1 and of 0.911 and 0.852, respectively, measured by radiologist 2. The following significant correlations were obtained between the ADC, D, and D* values and the histological grades: r = -0.619 (P < 0.001), r = -0.628 (P < 0.001), and r = -0.299 (P = 0.018), respectively, as measured by radiologist 1; r = -0.622 (P < 0.001), r = -0.633 (P < 0.001), and r = -0.303 (P = 0.017), respectively, as measured by radiologist 2. The intra-class correlation coefficient (ICC) values between the two observers were 0.996 for ADC, 0.997 for D, 0.996 for D*, and 0.992 for the f values, which indicated excellent inter-observer agreement in the measurements between the two observers.
The problem that remains to be solved is that the significance of the perfusion parameters (D* and f) among different histological grades of HCC and the correlation between perfusion parameters and the histological grade are in disagreement with previous results, which remains controversial. At the same time, further studies should investigate the cor relation between the IVIM perfusion parameters and the DCE-MRI parameters to reflect the perfusion characteristics of tumor.
DWI-MR imaging can be used as a noninvasive quantitative imaging method in discriminating different histological grades of HCC. DWI-MR is a noninvasive approach to sensitively evaluate the small-scale motion of water molecules at the microscopic level, reflect tumor microenvironment, show changes in the microcirculation of tissues, and provide the pathology and physiology information. IVIM-DWI parameters and conventional DWI parameters might be useful in assessing the differentiation grades of carcinoma, which might be helpful in predicting the patient prognosis. The study showed that the perfusion parameters (D* and f) were not statistically significant in differentiating the histological grades of HCC, and showed significant lower diagnostic performance in differentiating low-grade (G1) from high-grade (G2 and G3) HCCs. These results may be related to the location of the lesion and the feeding artery. IVIM-derived parameters and conventional DWI parameters can predict the histological grade of HCC and the correlation was observed between the parameters and the histological grades. The measurement reproducibility of the parameters was excellent between the two radiologists. The measured values of the D* and f existed instability in all patients, and the diagnostic value of the D*and f remains controversial. The IVIM-derived D and ADC values showed better diagnostic performance in differentiating high-grade HCC from low-grade HCC, and a moderate to good correlation was observed between the ADC and D values and the histological grades. IVIM-DWI parameters and conventional DWI parameter might be useful in evaluating the differentiation grades of carcinomas before operation, which might be helpful in predicting the patient prognosis in clinical practice.
In the MR examination, the total scanning time for the IVIM was related to the respiratory condition of the patient. The faster the breathing, the shorter the scanning time. The total scanning time of all the patients is approximately 8-13 min, and the average time is about 10 min. In the process of the measurement, it should be repeated to ensure the stability and reliability of the parameters. Further studies to validate the IVIM perfusion parameters are essential to be correlated with the DCE-MRI parameters and reflect the perfusion characteristics of tumor. At the same time, the texture information of tumors can be analyzed, which can reflect the essential characteristics of the mass. Radiomics is a new research method for tumors. According to the heterogeneity of tumors, a large number of high dimensional quantitative image features are extracted from MRI, PET, and CT images and analyzed. By extracting and analyzing the characteristics of medical images, it can evaluate the diagnosis and prognosis of patients.