Background: To date, there are inconsistent data about relationships between diffusion-weighted imaging (DWI) and tumor grading/microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Our purpose was to systematize the reported results regarding the role of DWI in prediction of tumor grading/MVI in HCC. Method: MEDLINE library, Scopus, and Embase data bases were screened up to December 2019. Overall, 29 studies with 2,715 tumors were included into the analysis. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC. Results: In 21 studies (1,799 tumors), mean apparent diffusion coefficient (ADC) values (ADCmean) were used for distinguishing HCCs. ADCmean of G1–3 lesions overlapped significantly. In 4 studies (461 lesions), minimum ADC (ADCmin) was used. ADCmin values in G1/2 lesions were over 0.80 × 10−3 mm2/s and in G3 tumors below 0.80 × 10−3 mm2/s. In 4 studies (241 tumors), true diffusion (D) was reported. A significant overlapping of D values between G1, G2, and G3 groups was found. ADCmean and MVI were analyzed in 9 studies (1,059 HCCs). ADCmean values of MIV+/MVI− lesions overlapped significantly. ADCmin was used in 4 studies (672 lesions). ADCmin values of MVI+ tumors were in the area under 1.00 × 10−3 mm2/s. In 3 studies (227 tumors), D was used. Also, D values of MVI+ lesions were predominantly in the area under 1.00 × 10−3 mm2/s. Conclusion: ADCmin reflects tumor grading, and ADCmin and D predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADCmean cannot predict tumor grading/MVI in HCC.

Hepatocellular carcinoma (HCC) is the most common primary malignant neoplasm of the liver [1]. Histologically, HCCs are classified according to the Edmondson-Steiner classification into 4 grades [2]. The pathological grade of HCC is associated with the prognosis [2]. Poorly differentiated HCC has higher recurrence rate and poorer prognosis after surgical resection in comparison with well- and moderately differentiated tumors [3].

Similarly, microvascular invasion (MVI) is another important histopathological feature in HCC. MVI correlates with early recurrence and worse outcomes [4]. Therefore, prediction of the histological grade and/or MVI would provide great benefit in preoperative treatment planning. These tumor factors can be obtained only by histopathological examination. However, preoperative biopsy is not indicated for HCC. First, HCCs have typical radiological features on computed tomography (CT) and/or magnetic resonance imaging (MRI). Second, biopsy is an invasive approach. According to the current guidelines, for example, according to the American Association for the Study of Liver Diseases, biopsy is not needed for tumors with typical MRI or CT findings [5, 6]. Therefore, accurate pretreatment prediction of histological grade and MVI of HCCs based on MR images is very important.

However, it is difficult to define accurate preoperative grade of HCC using routine imaging modalities. Typical HCC features on MRI after administration of gadolinium-based contrast agents are already used in clinical practice to differentiate HCC from benign findings. However, contrast-enhancing MRI cannot provide histological information.

Some reports analyzed the role of diffusion-weighted imaging (DWI) as a predictor of histopathological features in HCC [7, 8]. DWI is an imaging modality, which characterizes random water movement or diffusion in tissues [9, 10]. Water diffusion can be quantified by apparent diffusion coefficient (ADC) [10]. Different ADC values such as mean ADC (ADCmean), minimal ADC (ADCmin), maximal ADC (ADCmax), and so-called true diffusion (D) can be calculated [11]. Most frequently, ADCmean is used. According to the literature, ADC can reflect cell count and proliferation activity in different tumors [12, 13]. However, published data regarding the role of DWI in prediction of tumor grade and/or MVI in HCC were inconsistent. Furthermore, the number of investigated patients/tumors in the studies was relatively small. Therefore, the aim of the present meta-analysis was to systematize the reported data regarding associations between DWI and clinically relevant histopathological parameters such as tumor grading and MVI in HCC.

Data Acquisition

MEDLINE library, Embase data base, and Scopus data base were screened for associations between DWI and tumor grading and MVI up to December 2019 (Fig. 1).

Fig. 1.

PRISMA flowchart of the data acquisition. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement; DWI, diffusion-weighted imaging.

Fig. 1.

PRISMA flowchart of the data acquisition. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement; DWI, diffusion-weighted imaging.

Close modal

For data acquisition, the following search criteria were used:

  • DWI and tumor grading: DWI OR diffusion weighted imaging OR diffusion OR magnetic resonance imaging OR ADC or apparent diffusion coefficient AND grading OR grade AND hepatocellular carcinoma

  • DWI and MVI: DWI OR diffusion weighted imaging OR diffusion OR magnetic resonance imaging OR ADC or apparent diffusion coefficient AND microvessel invasion OR microvascular invasion AND hepatocellular carcinoma

After the primary search, secondary references were also analyzed. Duplicate articles, review articles, experimental animal and in vitro studies, case reports, and non-English publications were excluded. In the next step, articles without statistical data regarding DWI parameters (mean values and/or standard deviation [SD]) were also excluded. Overall, 29 studies with 2,715 tumors were included into the analysis [14-42]. There were 20 studies regarding DWI and tumor grading, 8 studies about DWI and MVI, and 1 study investigated DWI, tumor grading, and MVI in HCC.

The following data were extracted from the literature: authors, year of publication, number of HCC lesions, tumor grade, presence of MVI, and mean and standard deviation of the reported DWI parameters. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research [43].

Meta-Analysis

The methodological quality of the acquired studies was checked by 1 observer (A.S.) using the Quality Assessment of Diagnostic Studies (QUADAS) instrument [44]. Figure 2 displays the results of QUADAS proving.

Fig. 2.

QUADAS-2 quality assessment of the included studies. QUADAS, Quality Assessment of Diagnostic Studies.

Fig. 2.

QUADAS-2 quality assessment of the included studies. QUADAS, Quality Assessment of Diagnostic Studies.

Close modal

The meta-analysis was undertaken by using RevMan 5.3 (computer program, version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014). Heterogeneity was calculated by means of the inconsistency index I2 [45, 46]. Also, DerSimonian and Laird [47] random-effects models with inverse-variance weights were performed without any further correction. Finally, heterogeneity between studies was evaluated by the I2 statistic, which describes the percentage of variation across studies that is due to heterogeneity rather than chance [48].

The acquired 29 studies comprised 2,715 tumors. Of the included 29 studies, 5 were prospective and 24 were retrospective (Table 1). Different 3.0T scanners were used in 10 studies and diverse 1.5T scanners in 18 reports. In 1 study, both scanners (1.5T and 3.0T) were used. In all studies, DWI was performed by using a single-shot echo-planar imaging (EPI) sequence. Technical DWI parameters varied among the studies (Table 2). The collected studies investigated different DWI parameters such as mean ADC (ADCmean), minimum ADC (ADCmin), and D. The funnel plots (Fig. 3a–c) show no evidence for publication bias regarding the analyzed DWI parameters.

Table 1.

Overview of the involved studies

Overview of the involved studies
Overview of the involved studies
Table 2.

DWI techniques in the included studies

DWI techniques in the included studies
DWI techniques in the included studies
Fig. 3.

Funnel plots show no evidence for publication bias. a Funnel plot for publications regarding ADCmean. b Funnel plot for publications regarding ADCmin. c Funnel plot for publications regarding D. ADC, apparent diffusion coefficient; D, true diffusion.

Fig. 3.

Funnel plots show no evidence for publication bias. a Funnel plot for publications regarding ADCmean. b Funnel plot for publications regarding ADCmin. c Funnel plot for publications regarding D. ADC, apparent diffusion coefficient; D, true diffusion.

Close modal

DWI versus Tumor Grade

In 21 studies (1,799 tumors), mean ADC values (ADCmean) were used for distinguishing different HCC lesions. ADCmean values (×10−3 mm2/s) of the lesions were as follows: grade 1 (G1, n = 364): 1.28, 95% CI: 1.14–1.41; grade 2 (G2, n = 1,063): 1.16, 95% CI: 1.09–1.24; and grade 3 (G3, n = 360): 1.09, 95% CI: 0.74–1.43 (Fig. 4a–c). Figure 4d shows the distribution of ADCmean values in different tumor grades. The ADCmean values of the groups overlapped significantly.

Fig. 4.

a Forrest plots of ADCmean values reported for grade 1 HCC lesions. b Forrest plots of ADCmean values reported for grade 2 HCC lesions. c Forrest plots of ADCmean values reported for grade 3 HCC lesions. d Comparison of ADCmean values between grade 1, 2, and 3 HCCs. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma.

Fig. 4.

a Forrest plots of ADCmean values reported for grade 1 HCC lesions. b Forrest plots of ADCmean values reported for grade 2 HCC lesions. c Forrest plots of ADCmean values reported for grade 3 HCC lesions. d Comparison of ADCmean values between grade 1, 2, and 3 HCCs. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma.

Close modal

Furthermore, in 4 studies (461 lesions), minimum ADC values (ADCmin) were estimated and used for the differentiation of HCCs. The distribution of ADCmin (×10−3 mm2/s) in dependency on tumor grade was as follows: G1 (n = 52): 0.92, 95% CI: 0.72–1.13; G2 (n = 351): 0.81, 95% CI: 0.64–0.98; and G3 (n = 58): 0.59, 95% CI 0.29–0.88 (Fig. 5a–c). ADCmin values of G1 and G2 lesions overlapped significantly (Fig. 5d). Predominantly, G1 and G2 lesions did not have ADCmin values under 1.00 × 10−3 mm2/s. ADCmin values in G3 HCCs were below the threshold of 0.80 × 10−3 mm2/s.

Fig. 5.

a Forrest plots of ADCmin values reported for grade 1 HCC lesions. b Forrest plots of ADCmin values reported for grade 2 HCC lesions. c Forrest plots of ADCmin values reported for grade 3 HCC lesions. d Graphical distribution of ADCmin values between different tumor grades in HCC. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma.

Fig. 5.

a Forrest plots of ADCmin values reported for grade 1 HCC lesions. b Forrest plots of ADCmin values reported for grade 2 HCC lesions. c Forrest plots of ADCmin values reported for grade 3 HCC lesions. d Graphical distribution of ADCmin values between different tumor grades in HCC. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma.

Close modal

In 4 studies (241 tumors), D was reported. D values (×10−3 mm2/s) of the lesions were as follows: G1 (n = 47): 1.20, 95% CI: 0.80–1.61; G2 (n = 115): 1.04, 95% CI: 0.86–1.21; G3 (n = 79): 1.17, 95% CI: 0.92–1.41 (Fig. 6a–c). A significant overlapping of D values between the groups was shown (Fig. 6d).

Fig. 6.

a Forrest plots of D values reported for grade 1 HCC lesions. b Forrest plots of D values reported for grade 2 HCC lesions. c Forrest plots of D values reported for grade 3 HCC lesions. d Comparison of D values between grade 1, 2, and 3 HCCs. D, true diffusion; HCC, hepatocellular carcinoma.

Fig. 6.

a Forrest plots of D values reported for grade 1 HCC lesions. b Forrest plots of D values reported for grade 2 HCC lesions. c Forrest plots of D values reported for grade 3 HCC lesions. d Comparison of D values between grade 1, 2, and 3 HCCs. D, true diffusion; HCC, hepatocellular carcinoma.

Close modal

DWI versus MVI

Associations between ADCmean and MVI were investigated in 9 studies (1,059 HCCs). MVI-positive (n = 494) and MVI-negative (n = 565) tumors had comparable ADCmean values: 1.20 (95% CI: 1.11–1.30) and 1.35 (95% CI: 1.41–1.46), respectively. The ADCmean values of the groups overlapped significantly (Fig. 7a–c).

Fig. 7.

a Forrest plots of ADCmean values reported for HCC lesions with MVI. b Forrest plots of ADCmean values reported for HCC lesions without MVI. c Comparison of ADCmean values between HCCs with and without MVI. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Fig. 7.

a Forrest plots of ADCmean values reported for HCC lesions with MVI. b Forrest plots of ADCmean values reported for HCC lesions without MVI. c Comparison of ADCmean values between HCCs with and without MVI. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Close modal

Relationships between ADCmin and MVI were reported in 4 studies (672 lesions). MVI-positive tumors (n = 335) showed lower ADCmin values, 0.81 (95% CI: 0.69–0.93), than MVI-negative tumors, 1.02 (95% CI: 0.91–1.13). Furthermore, ADCmin values of MVI-positive tumors were in the area under 1.00 × 10−3 mm2/s (Fig. 8a–c)

Fig. 8.

a Forrest plots of ADCmin values reported for HCC lesions with MVI. b Forrest plots of ADCmin values reported for HCC lesions without MVI. c Comparison of ADCmin values between HCCs with and without MVI. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Fig. 8.

a Forrest plots of ADCmin values reported for HCC lesions with MVI. b Forrest plots of ADCmin values reported for HCC lesions without MVI. c Comparison of ADCmin values between HCCs with and without MVI. ADC, apparent diffusion coefficient; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Close modal

In 3 studies (227 tumors), D was used for distinguishing different HCC lesions. MVI-positive tumors (n = 94) had lower D values, 0.84 (95% CI: 0.74–0.94), than MVI-negative tumors, 1.09 (95% CI: 0.97–1.21). D values of MVI-negative lesions were predominantly in the area over 1.00 × 10−3 mm2/s, and D values of MVI-positive lesions were under 1.00 × 10−3 mm2/s (Fig. 9a–c).

Fig. 9.

a Forrest plots of D values reported for HCC lesions with MVI. b Forrest plots of D values reported for HCC lesions without MVI. c Comparison of D values between HCCs with and without MVI. D, true diffusion; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Fig. 9.

a Forrest plots of D values reported for HCC lesions with MVI. b Forrest plots of D values reported for HCC lesions without MVI. c Comparison of D values between HCCs with and without MVI. D, true diffusion; HCC, hepatocellular carcinoma; MVI, microvascular invasion.

Close modal

To the best of our knowledge, this is the first meta-analysis regarding associations between DWI and tumor grade/MVI in HCC based on a large sample. DWI shows a great diagnostic potential and is widely used in oncological MRI. DWI, namely, ADCmean, can differentiate between malignant and benign lesions in different regions [49, 50]. Typically, malignant tumors have lower ADCmean values in comparison to benign lesions [49, 50]. So far, recently, a large series showed that ADCmean was lower with the threshold of 1.00 in breast cancer and higher in benign breast lesions [49]. Similar results were reported for liver lesions [50].

Furthermore, according to the literature, DWI is associated with tissue microstructure and can reflect several histopathological features [9, 10, 12, 13]. For example, it has been shown that ADCmean and ADCmin correlated with cellularity and proliferation index Ki-67 in several malignant and benign tumors [12, 13]. Similar results were also reported for D values [11]. Some reports indicated that different DWI parameters can also reflect stromal fraction and nucleic size/nucleic-cytoplasmic ratio in tumors [9-11].

Presumably, DWI may also be helpful to characterize HCC lesions, namely, tumor differentiation and MVI. Prediction of histopathological features based on imaging is of high clinical relevance. Previously, some studies investigated this question with contradictory results. So, Chang et al. [14] found that highly differentiated HCCs showed statistically significant higher ADCmean values (2.04 ± 0.41 × 10−3 mm2/s) in comparison to moderately differentiated (1.62 ± 0.3 × 10−3 mm2/s) and poorly differentiated tumors (1.26 ± 0.21 × 10−3 mm2/s). Similar results were reported also in other studies [16, 21]. However, other authors did not find significant differences of ADCmean values between G1, G2, and G3 lesions [26, 29]. For example, according to Nasu et al. [26], ADCmean values (×10−3 mm2/s) were 1.45 ± 0.35 in G1, 1.46 ± 0.32 in G2, and 1.36 ± 0.29 in G3 tumors.

As already mentioned, DWI let retrieve different parameters. In most studies, ADCmean was used. Less frequently, other DWI parameters, such as ADCmin and D, were analyzed. Presumably, different DWI parameters may reflect different histopathological features in HCC. In fact, the present results confirmed our hypothesis. As shown, ADCmean and D cannot discriminate HCC with different tumor differentiation. Therefore, these DWI parameters cannot be used as a surrogate marker for tumor grading in HCC. However, our data showed that ADCmin can predict grade of HCC lesions. In fact, G1 and G2 HCCs had ADCmin values above 0.80 × 10−3 mm2/s and G3 HCCs had ADCmin values in the area below 0.80 × 10−3 mm2/s.

Only in 1 study, ADCmin values of G1 and G2 tumors were also in the area under the threshold of 0.80 × 10−3[23]. In this study, all ADC values were lower in comparison to other reports, also ADCmean. It may be explained by the measure method. In fact, ADC values were calculated in the study using three-dimensional histograms [23]. In other studies, region-of-interest-based measurements were performed.

The indicated ADCmin value of 0.80 × 10−3 can be used as thresholds for estimation of tumor grading. This finding may be related to the fact that ADCmin is more sensitive to reflect tumor cell count. Furthermore, some reports suggested that different DWI parameters were associated with different histopathological findings [11]. For example, in meningiomas, ADCmean correlated significantly with proliferation index Ki-67 and nucleic size/nucleic area of tumor cells, but not with cell count [11]. ADCmin and D correlated significantly with cell count and total nucleic area, but not with Ki-67 [11]. Tumor grading in HCC is based on the morphological features of tumor cells and nucleic content such as nucleic size. Grade 1 tumors have cells with abundant cytoplasm and minimal nuclear irregularity. Grade 2 lesions are characterized by greater nuclear irregularity and prominent nucleoli. Grade 3 HCCs show increased nuclear pleomorphism and angulation of the nuclei. In addition, tumor giant cells are also more commonly seen. Finally, grade 4 tumors are poorly differentiated lesions with marked nuclear pleomorphism, hyperchromatism, and anaplasia [2]. As shown, there are histopathological features which are associated with ADCmin but not with ADCmean. Therefore, our finding that ADCmin is sensitive to discriminate different tumor grades in HCC is plausible.

Another important aspect of the present study is the fact that ADCmin and D can identify lesions with MVI. As shown, both parameters were in the area under 1.00 × 10−3 mm2/s in MVI-positive tumors. Furthermore, our study identified that ADCmean cannot predict MVI. Owing to the fact that pretreatment visualization of tumor MVI is very beneficial in clinical setting, ADCmin and/or D should be estimated in each HCC lesion to predict MVI and tumor prognosis.

The results of the present analysis are based on a large cohort and, therefore, provide evident data regarding associations between DWI and tumor grading/MVI in HCC. However, there are several limitations to address. First, most of the acquired studies were retrospective. Second, according the QUADAS criteria, some of the involved studies showed clinical review bias, patient selection bias, and diagnostic review bias. Third, different MR equipment, Tesla strength, DWI sequences, and b values were used in the collected studies. However, our data reflect a real clinical situation with different technical and other details.

Finally, our statement about use of ADCmin is based on 4 studies. Therefore, further studies and/or meta-analyses regarding some DWI parameters are needed to prove our results.

In conclusion, ADCmin reflects tumor grading in HCC. ADCmin and D can predict MVI in HCC. Therefore, these DWI parameters should be estimated for every HCC lesion for pretreatment tumor stratification. ADCmean does not predict tumor grading and/or MVI in HCC.

This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The paper is exempt from ethical committee approval because it is a meta-analysis.

The authors declare no conflicts of interest.

The authors did not receive any funding.

Alexey Surov: study concept and design; acquisition, analysis, and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content. Maciej Pech: critical revision of the manuscript for important intellectual content; technical and material support; study supervision. Jazan Omari: acquisition of data; drafting of the manuscript; technical or material support. Frank Fischbach, Robert Damm, Katharina Fischbach, Maciej Powerski, and Borna Relja: drafting of the manuscript and technical or material support. Andreas Wienke: analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis; study supervision.

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