Lower diffusion-derived vessel density (DDVD) measure of liver focal nodular hyperplasia than those of hepatocellular carcinoma and liver metastasis allows potential differential diagnosis: quantitative and semi-quantitative analyses of two-center data

Introduction

The exploration of diffusion-weighted imaging (DWI) in liver fibrosis evaluation revealed that diffusion-derived vessel density (DDVD) can reflect microvascular perfusion. For a spin-echo type echo-planar sequence, the second motion-probing gradient after the 180-degree radiofrequency pulse cannot fully refocus the flowing spins in the vessel and micro-vessels after being dephased by the first motion probing gradient before the 180-degree RF pulse. Therefore, liver blood vessels, including sub-pixel microvessels, show high signal when there is no motion probing gradient (b=0 s/mm²) and low signal when even very low b-values (such as b=1, b=2 s/mm²) are applied (1). Thus, the signal difference between images when the motion probing gradient is ‘off’ and ‘on’ reflects the extent of tissue functional vessel density in the physiological sense, and we term this as DDVD. DDVD is derived from Eq. [1]:

DDVDb2=Sb0ROIarea0−Sb2ROIarea2[unit:arbitraryunit(au)/pixel][1]

where ROI_area0 and ROI_area2 refer to the number of pixels in the selected region of interest (ROI) on b=0 and b=2 s/mm² DWI, respectively. Sb0 refers to the measured sum signal intensity within the ROI when b=0 s/mm², and Sb2 refers to the measured sum signal intensity within the ROI when b=2 s/mm²; thus, Sb/ROI_area equates to the mean signal intensity within the ROI. Sb2 and ROI_area2 can also be approximated by other low b-values (such as b=10 s/mm²) DWI. If we consider a pixel to be an individual ROI, the DDVD pixelwise map can be constructed pixel-by-pixel with this same principle (2).

The clinical usefulness of DDVD as a straightforward diffusion imaging biomarker has been recently demonstrated. DDVD is a useful parameter for distinguishing livers with and without fibrosis, and livers with severer fibrosis tend to have even lower DDVD measurements than those with milder liver fibrosis (1,3,4). Li et al. applied DDVD to assess the perfusion of hepatocellular carcinoma (HCC), showing higher perfusion of HCC relative to adjacent liver parenchyma (5). Hu et al. (6) described that liver hemangiomas, which show very high DDVD values, can be mostly differentiated from liver mass-forming lesions [HCCs and focal nodular hyperplasia (FNH)] solely based on the DDVD map. On the other hand, liver simple cysts show DDVD close to zero (7). Lu et al. (8) reported higher DDVD for rectal carcinoma than the adjacent tumor-free wall. A trend was noted that earlier clinical grades of rectal carcinoma had a higher DDVD than those of the advanced clinical grades, which is consistent with the known clinical characteristics of rectal carcinomas. He et al. (9) and Li et al. (10) reported that DDVD analysis of the placenta allowed excellent separation of normal and early preeclamptic pregnancies. Lu et al. (11) reported that placental regional DDVD was significantly higher in pregnant women with placenta accreta spectrum disorders than in women with normal placentas, and especially higher in patients with placenta percreta. Chen et al. (12) described a proof-of-concept study that a combination of DDVD map and high b-value DWI identifies the existence and the size of penumbra of acute strokes. Wang et al. (13) reported endometrial carcinoma with Ki-67 high-proliferation or aggressive histological type had higher DDVD values than those with Ki-67 low-proliferation or non-aggressive histological type. Ni et al. (14) tested DDVD analysis for isocitrate dehydrogenase (IDH) genotyping in diffuse gliomas. DDVD was lower among IDH-mutant positive gliomas than among IDH-wildtype gliomas, with an AUC of 0.823 for separating IDH-mutant positive gliomas from IDH-wildtype gliomas. Yao et al. (15) used DDVD to evaluate parotid gland pleomorphic adenomas (PAs), malignant tumors, and Warthin’s tumors. DDVD ratio (DDVDr) was DDVD of the tumor divided by DDVD of tumor-free parotid gland tissue. Perfusion parameters of PAs, malignant tumors, and Warthin’s tumors were further normalized by PA’s measure. The ratio results of malignant tumor DDVD and Warthin’s tumors DDVD were compared with the literature results. It was noted that DDVDr ratios of both malignant tumors to PA and Warthin’s tumors to PA were very similar to the mean ratio of computed tomography (CT) measured blood volume of these tumors. With DDVD analysis, Zheng et al. (16) demonstrated that per unit micro-circulation of the spleen is decreased in viral hepatitis-B liver fibrosis patients. This is consistent with, for example, the report of Gitlin et al. (17) with analysis of the washout curves of Xenon 133 injected in the splenic artery in patients with liver cirrhosis and portal hypertension. Among the patients, splenic blood flow, expressed as mL per 100 g of splenic tissue, was decreased. On the contrary, total splenic blood flow, calculated by multiplying specific splenic flow by spleen volume, was increased (17). In a recent analysis, when an echo time (TE) of 59 ms [repetition time (TR) =1,600 ms, free breathing acquisition] and b=0, 2 s/mm² were used for the DDVD calculation of 26 cases of HCC, the mean DDVD_HCC/DDVD_liver ratio was 1.42. This value agrees well with the literature results, perfusion CT blood volume HCC to liver median ratio of 1.38, while the literature results perfusion CT blood flow HCC to liver mean ratio is 1.92 (18,19).

Considering that DDVD analysis has high performance in separating liver mass lesions, liver hemangioma, and liver simple cyst (6,7), in this study, we further study the potential role of DDVD analysis for the separation of liver FNH and liver malignant mass [HCC and metastasis (Mets)].

Methods

This is a retrospective analysis of previously prospectively acquired liver DWI data. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All imaging data were acquired with institutional ethical approval (approval ID: S1709442250839 for dataset-1 and 2017-YXZDK-002 for dataset-2) and with informed consent obtained from individual participants. Dataset-1 had 8 cases of FNH, 56 cases of HCC, and 14 cases of liver metastases (7 cases from colon, 2 cases from breast, 2 cases from pancreas, 2 cases from biliary duct, and 1 case of nasopharyngeal carcinoma). Dataset-2 had 10 cases of FNH and 78 cases of HCC. All FNH and HCC had histopathological diagnoses. The diagnosis of Mets was based on histopathology or a combination of complete patient history and typical imaging features. For dataset-1, liver IVIM imaging was performed with a 3.0-T magnet (Vida Magneton, Siemens Healthineers, Erlangen, Germany). The diffusion imaging was based on a single-shot spin-echo type echo-planar sequence. The default spectral pre-saturation technique was used for fat suppression. DWI images with b-values of 0, 10, 20, 50, 80, and 100 s/mm² were utilized in this study. The TR was 2,500 ms, and the TE was 84 ms. Other parameters included slice thickness =5 mm and inter-slice gap =1 mm, matrix = 128×128, field of view (FOV) = 350 mm × 350 mm, number of excitation (NEX) =1. Data were acquired with respiratory gating. For dataset-2, liver DDVD imaging was performed with a 3.0-T magnet (Ingenia, Philips Healthcare, Best, Netherlands). The diffusion imaging was based on a single-shot spin-echo type echo-planar sequence. The default spectral pre-saturation with the inversion-recovery technique was used for fat suppression. DWI images with three b-values of 0, 2, and 10 s/mm² were acquired. The TR was 313 ms, and the TE was 38 ms. Other parameters included slice thickness =7 mm and inter-slice gap =0.7 mm, matrix =112×112, FOV =341 mm × 341 mm, and NEX =2. Breath-hold was applied with a scan duration of 9 seconds.

All image processing was implemented in a custom program developed on MATLAB (Mathworks, Natick, MA, USA). For dataset-1, based on Eq. [1], DDVD_b2, DDVD_b10, DDVD_b20, DDVD_b50, DDVD_b80, and DDVD_b100 were calculated from b=0 and b=2 s/mm² images, b=0 and b=10 s/mm² images, b=0 and b=20 s/mm² images, b=0 and b=50 s/mm² images, b=0 and b=80 s/mm² images, and b=0 and b=100 s/mm² images, respectively. The location and size of the tumor were firstly determined by the standard T2-weighted image and T1-weighted Gd-enhanced image. ITK-SNAP software (www.itksnap.org) was used to draw the ROIs. For quantitative analysis, based on DDVD maps, ROIs were drawn to cover a large portion of each lesion on each slice. Liver parenchyma ROIs were delineated at the same slice as the tumor. If multiple tumors existed, only the largest tumor was counted. ROI excluded large necrotic areas and high signal central scar in FNH if existed. For each study subject, the mean signal intensity of each ROI was weighted by the number of pixels included in each ROI, then the sum of the weighted DDVD was calculated to obtain the value for each case.

The ratios of lesion to adjacent liver tissue were taken as:

DDVDr=lesionDDVD/liverDDVD[2]

For dataset-2, HCC DDVDr values have been previously reported (5), and earlier reported values were reused in the current study. Ten cases of FNH were newly measured following the earlier approach described in reference (5). The principles of the measurement were the same as those for dataset-1, except that for dataset-1 the measurement was conducted on DDVD maps, while for dataset-2, the measurement was conducted on b=0, b=2, and b=10 s/mm² DWI images, and then DDVDr_b2 and DDVDr_b10, data were generated.

For semi-quantitative analysis on b=0 s/mm² DWI image (DWI_b0) and DDVD map (DDVD_b10 for dataset-1 and DDVD_b2 for dataset-2), relative to adjacent liver signal, a liver lesion signal was assigned to five categories: low signal (scored as ‘0’), iso-signal (scored as ‘1’), slightly high signal (scored as ‘1.5’), high signal (scored as ‘2’), and markedly high signal (scored as ‘3’). When it was difficult to sign a score of ‘2’ or ‘3’, an additional score of ‘2.5’ was signed. The scoring was conducted by three readers in consensus [a specialist radiologist (Y.X.J.W.), a senior radiology trainee (C.Y.L.), and a trained biomedical engineer (D.Q.Y.)].

All HCC cases in dataset-1, and all FNH and Mets cases had both quantitative measurement and semi-quantitative scoring. For HCC cases in dataset-2, semi-quantitative scoring was conducted for 78 cases, while quantitative measurement was conducted for 72 cases as reported in reference (5).

Statistical analysis

Statistical analysis was performed using GraphPad Prism (GraphPad Software, San Diego, CA, USA). The comparison of DDVD between the two groups was analyzed with the Mann-Whitney U test or Wilcoxon signed-rank test. A P value of less than 0.05 was considered statistically significant. Receiver operating characteristic curve analysis and the area under the curve (AUROC) were used to assess the diagnostic performance.

Results

The DDVDr values for FNH, HCC, and Mets are shown in Table 1 and Figure 1. A clear pattern was shown that FNH had lower DDVDr values than malignant lesions, both for dataset-1 and dataset-2. The difference between FNH and malignant lesions was consistent across the data of DDVD_b2, DDVD_b10, DDVD_b20, DDVDb_b50, DDVD_b80, and DDVD_b100 (Figures 1,2). For dataset-1, DDVD_b10 had an AUROC of 0.864, and a cutpoint value of >1.923 had a sensitivity of 81.4% and a specificity of 87.5% in suggesting malignancy. For dataset-2, DDVD_b2 had an AUROC of 0.912, and a cutpoint value of >1.845 had a sensitivity of 79.7% and a specificity of 90% in suggesting malignancy.

Figure 1 FNH has a lower DDVDr value than those of HCC and Mets. A scatter plot of DDVDr values for FNH, HCC, and Mets. (A) DDVDr_b10 and DDVDr_b20 for dataset-1. (B) DDVDr_b2 and DDVDr_b10 for dataset-2. A similar trend is seen in (A) and (B). The dotted horizontal line denotes DDVDr =1. HCC DDVDr values in dataset-2 are reused from Li et al. (5). DDVDr, diffusion-derived vessel density ratio; FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma; Mets, metastasis.

Figure 2 A comparison of FNH, HCC, and Mets groupwise mean DDVD values across DDVD_b10, DDVD_b20, DDVD_b50, DDVD_b80, and DDVD_b100. Data is from dataset-1. au, arbitrary unit; DDVD, diffusion-derived vessel density; FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma; Mets, metastasis.

With dataset-1 results, a comparison of HCC and Mets showed Mets had an overall higher mean DDVD_b10 and DDVD_b20 value, but when a larger second b-value was applied (i.e., DDVD_b50, DDVD_b80, DDVD_b100), HCC showed a higher mean DDVD value. However, FNH consistently showed lower DDVD compared to HCC and Mets (Figure 2).

For all FNH and HCC lesions, DDVDr values measured higher with dataset-1 than with dataset-2 (Figure 1).

Results of the semi-quantitative scoring of the liver lesions are shown in Figure 3. A feature was seen that a drop from DWI_b0 high signal or slightly high signal to DDVD iso-signal suggests the diagnosis of FNH. According to dataset-1, 62.5% (5/8) of FNH had such an appearance, and only 3.6% (2/56) of the HCC and none of the Mets had this appearance. According to dataset-2, 80% (8/10) of FNH had such an appearance, and only 7.7% (6/78) of the HCC had this appearance. Furthermore, dataset-1 showed Mets had a higher DDVD signal semiquantitative score than HCC, with markedly high signal on both DWI_b0 and DDVD map favoring the diagnosis of Mets. Five-point-four percent (3/56) of HCC had markedly high signal on DWI_b0, 14.3% (8/56) of HCC had markedly high signal on DDVD map, and 1.8% (1/56) of HCC had markedly high signal on both DWI_b0 and DDVD map. For dataset-1, 42.9% (6/14) of Mets had markedly high signal on DWI_b0, 35.7% (5/14) of Mets had markedly high signal on DDVD map, and 28.6% (4/14) of Mets had markedly high signal on both DWI_b0 and DDVD map.

Figure 3 Semi-quantitative scoring results for liver lesions. For FNH, the mean DDVD score was substantially lower than the DWI_b0 score. (A) dataset-1; (B) dataset-2. Gray bar: mean value. DDVD, diffusion-derived vessel density; DWI, diffusion-weighted imaging; FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma; Mets, metastasis.

Visualization of the difference between FNH and malignant lesions is shown in Figures 4-6.

Figure 4 Four cases of FNH (all denoted by red arrows) from dataset-1. (A1-D1) b=0 s/mm² DWI images; (A2-D2) DDVD_b10 maps. Overall, FNH lesions show slightly lower, iso-, or slightly higher signal relative to liver parenchyma on the DDVD map. The case in (C) had a central scar. The high DDVD signal for the area denoted by triangles and for the central scar in (C) is considered due to the longer T2 values of these areas and possible misalignment between b=0 and b=10 s/mm² images [see explanations in references (2,6)]. However, the FNH area denoted by asterisks shows iso- or lower DDVD signal. Note that dataset-1 did not apply breath-holding during data acquisition; thus, slight misalignment between the b=0 s/mm² image and the b=10 s/mm² image is always expected. Semi-quantitative score: A1: 0; A2: 0; B1: 2; B2: 1; C1: 2; C2: 1; D1: 1.5, D2: 1. DDVD, diffusion-derived vessel density; DWI, diffusion-weighted imaging; FNH, focal nodular hyperplasia.

Figure 5 Four cases of HCC (denoted by red arrows) and one case of multiple Mets (denoted by red asterisks) from dataset-1. (A1-D1) b=0 s/mm² DWI images; (A2-D2) DDVD_b10 maps. (E) DDVD_b10 map with a b=0 s/mm² DWI image inset. Compared with the FNH lesion in Figure 3, malignant tissues in this figure show a higher DDVD signal on the DDVD map. The areas in (A2) denoted by purple asterisks are necrotic areas. The high DDVD signal for the necrotic areas in (A2) and ascites in (E) (denoted by yellow arrows) is considered to be due to the long T2 of these areas and possible misalignment between b=0 and b=10 s/mm² images [see explanations in references (2,6)]. Note that dataset-1 did not apply breath-holding during data acquisition; thus, slight misalignment between the b=0 s/mm² image and the b=10 s/mm² image is always expected. Semi-quantitative score: A1: 2; A2: 2; B1: 1.5; B2: 2; C1: 2; C2: 2; D1: 2.5; D2: 2.5; E_DWI: 0: 2.5; E_DDVD: 2. DDVD, diffusion-derived vessel density; DWI, diffusion-weighted imaging; FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma; Mets, metastasis.

Figure 6 Six cases of FNH (A-F) and three cases of HCC (G-I, all denoted by red arrows) from dataset-2. (A1-I1) b=0 DWI images; (A2-I2) DDVD_b2 maps. Overall, FNH lesions appear iso-signal relative to liver parenchyma and become invisible on the DDVD map, while HCC lesions appear hyper-signal relative to liver parenchyma on the DDVD map. Semi-quantitative score: A1: 1.5; A2: 1; B1: 2; B2: 1; C1: 1.5; C2: 1; D1: 2; D2: 1; E1: 2; E2: 1; F1: 2; F2: 1; G1: 2; G2: 2; H1: 2; H2: 3; I1: 2.5; I2: 2. DDVD, diffusion-derived vessel density; DWI, diffusion-weighted imaging; FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma.

Discussion

With the specific DWI data acquisition parameters applied, this study shows that FNH had a lower DDVD value than that of HCC and Mets. In simple words, if we only consider the differentiation between FNH and malignancies, a ‘lower’ DDVDr favors the diagnosis of FNH, though this is not exclusive, and a ‘higher’ DDVDr (e.g., >1.923 in dataset-1 and >1.845 in dataset-2) strongly suggests the diagnosis of malignancy. Based on semi-quantitative scoring, a drop from DWI_b0 high signal or slightly high signal to DDVD iso-signal suggests the diagnosis of FNH. Mets had a higher DDVD signal than HCC, with markedly high signal on both DWI_b0 and DDVD map favoring the diagnosis of Mets. Thus, DDVD can offer an additional contrast mechanism contributing to multi-parametric analysis of liver pathologies. A comparison of dataset-1 results and dataset-2 results shows that differentiation between FNH and HCC was better achieved with dataset-1 results. (Figure3) Note that while the dataset-1 magnetic resonance imaging (MRI) acquisition parameters are more commonly applied for DWI, the dataset-2 MRI acquisition parameters were chosen to allow a breath-holding scan, which shortens the scan duration while decreasing the signal-to-noise ratio due to the very short TR. The results of this study favor the dataset-1 MRI acquisition parameters.

FNH has been conventionally noted to be a hypervascular mass, with the initial magnitude of enhancement on CT/MRI no less than malignancies (20-23). In the current study, while FNH can be quantitatively hypervascular with DDVDr >1, FNH mostly has a lower DDVDr than that of HCC and Mets. We hypothesize that the high perfusion of FNH seen on CT/MRI is more related to its associated higher blood flow speed. Blood flow speed in FNH has been reported to be higher than those of HCC and Mets (Figure 7A-7D) (22,24-26,31). On the other hand, the amount of vasculature may be lower in FNH than in HCC and Mets (32-34). For example, according to Qiu et al. (32), with ultrasound-based microvascular imaging (US-MVI) method in describing vasculature pattern in mass lesions, all their 5 cases of FNH were defined as ‘hypovascular supply’, while 7 of their 24 HCC cases were defined as ‘hypovascular supply’ and 17 of HCC cases were defined as ‘hypervascular supply’. According to Yang et al. (33), with US-MVI method, all their 12 cases of FNH were defined as ‘hypovascular supply’, while 9 of their 35 HCC cases were defined as ‘hypovascular supply’ and 26 of HCC cases were defined as ‘hypervascular supply’. Note that, with the US-MVI method used by Qiu et al. (32) and Yang et al. (33), the ‘hypovascular supply’ was not of that compared to liver parenchyma. A few radioisotope imaging studies also did not show higher blood volume for FNH (34). However, at least partially related to the difficulties in reliably separating blood flow from blood volume, controversies exist in the literature regarding whether FNH has a blood volume as high as liver malignant lesions (22,35). It should be noted that the TE for DWI in dataset-1 (i.e., TE =84 ms) was longer than the TE of around 60 ms we most commonly used, and TR for DWI in dataset-2 (i.e., TR =313 ms) was shorter than the TR of >1,500 ms we most commonly used. With such TE/TR in the current study, we recently noted that DDVDr (i.e., liver lesion to adjacent liver parenchyma) would be measured higher for lesions with longer T2 (18). T2 was not measured in our current study; however, the signal on the DWI_b0 image is related to T2, with longer T2 correlating with higher DWI_b0 signal. While DWI_b0 signal of FNH was scored lower than those of Mets and slightly lower than those of HCC, the commonly seen signal drops from DWI_b0 high signal or slightly high signal to DDVD iso-signal of FNH, but not of HCC and Mets, suggesting that this feature is not ‘T2-effect’ dominant (Figure 3).

Figure 7 FNH tends to have a faster blood flow than HCC and Mets (A-D), and Mets tend to have a longer T2 than HCC (E-H). Data are from Ippolito et al. (24), Zhu et al. (25), Schwarz et al. (26), Saito et al. (22), Ohtomo et al. (27), Cieszanowski et al. (28), Cieszanowski et al. (29), and Sun et al. (30). FNH, focal nodular hyperplasia; HCC, hepatocellular carcinoma; HG, hemangioma; Mets, metastasis; ML, malignant lesions.

With the patient data available in the current study, Mets had an average higher DDVD than HCC. The blood perfusion status of Mets is highly dependent on the origins of the tumors. On group levels, in Asia, many earlier studies showed that mixed Mets have ‘higher’ blood perfusion than HCC at a group level (36-40). Therefore, the HCC DDVD vs. Mets DDVD comparison results, as shown in Figures 1,3 are reasonable. Liver has a shorter T2 relaxation time compared with most other tissues and organs. Mets, retaining features of their original tissue, tend to have longer T2 relaxation times than HCC (as shown in Figure 7E-7H). In the current study, on DWI_b0 or DDVD map, none of the Mets in dataset-1 had a semi-quantitative signal score lower than 2.

Figure 2 shows that, when a ‘large’ second b-value was applied (i.e., DDVDr_b50, DDVDr_b80, DDVDr_b100), HCC showed a higher mean DDVD value than that of Mets. We attribute this observation to the T2 contribution to the DDVD measure. We have recently described that, with the liver as the reference, as the second b-value increases, spleen DDVD changes from being similar to (or even slightly higher than) liver DDVD to increasingly lower than liver DDVD (7). Note that, while CT and radioisotope imaging studies all showed that the liver and spleen have similar amounts of blood perfusion, the spleen has longer T2 than the liver (around 42 ms for the liver and 60 ms for the spleen at 3.0 T) (7). We advocate a very low second b-value for DDVD analysis (19), and DDVD_b50, DDVD_b80, and DDVD_b100 are not favored. On the other hand, FNH consistently showed lower DDVDr than those of HCC and Mets (Figure 2). This study shows that, if DWI image of a very low second b-value was not acquired, then DDVD using a moderate second b-value, such as b=50 or 100 s/mm², would still allow the separation of FNH and liver malignancies.

This study has the strength of the results being validated by two independent datasets. In the meantime, there are a number of limitations. The data acquisition protocol can be further improved in future studies. For example, dataset-1 did not apply breath-holding during data acquisition, and the NEX was only 1. For dataset-1, the second b-value was 10 s/mm². When DDVD was initially proposed, the proposed second b-value was 1 or 2 s/mm². Only when such a second b-value is applied, DDVD would predominantly reflect tissue perfusion. When the second b-value is 10 s/mm², T2 effect and slow diffusion will contribute to the DDVD measure, though not majorly (7,19). Due to the rather short TR used for dataset-2 acquisition, the signal-to-noise ratio was noted to be suboptimal for dataset-2. This study did not measure the T2 relaxation time for HCC and Mets; therefore, T2 contribution to the relative values of HCC DDVD and Mets DDVD can only be postulated based on literature data. This study suggests the possibility that a portion of Mets can be differentiated from HCC based on a combination of DWI_b0 and DDVD map signal. However, Mets from different organs may have different T2, blood perfusion, and diffusion features. The semi-quantitative scoring was a subjective procedure; however, visual semi-quantitative analysis is more commonly practiced in clinical radiology than precise quantitative measurement. We did not compare the differential diagnosis performance of DDVD with those of contrast-enhanced CT/MRI/ultrasound, as our goal is to provide a more cost-effective method without the need for contrast agent administration. Misalignment between b=0 and b=2 or 10 s/mm² images can lead to artificially higher DDVD signal (2,6), which could have partially contributed to that some FNH were measured quantitatively hypervascular with DDVDr >1. This study only considered the diagnosis performance based on DDVD with a limited sample size for FNH. We anticipate that the integration of lesion morphology with DDVD feature will further improve the differential diagnosis performance of non-enhanced MRI. A more comprehensive study with multi-center data will be our next step. Finally, more studies of less common pathologies, such as hepatic adenoma, intrahepatic cholangiocarcinoma, fibrolamellar HCC, etc., will be of interest.

Conclusions

In conclusion, the current study shows that FNH has a lower DDVD measure compared to those of HCC and Mets. It is likely that FNH may be associated with a faster blood flow speed, while with a limited increase in perfusion volume. A drop from DWI high signal or slightly high signal to DDVD iso-signal suggests the diagnosis of FNH. Markedly high signal on both DWI_b0 and DDVD map favors the diagnosis of Mets over HCC. Recently, a novel DWI metric, slow diffusion coefficient (SDC), has been proposed to measure the slow diffusion of in vivo tissues (41,42). An application of four DWI b-values, such as b=0, 10, 600, 800 s/mm², will allow the generation of three parameters, i.e., DDVD, SDC, and ADC (apparent diffusion coefficient). We anticipate that the majority of liver lesions, as long as they are of reasonable size, can be classified with a combination of these three biomarkers without the need for contrast agent administration.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-59/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-59/prf

Funding: This study has received funding from the Hong Kong GRF Project (No. 14112521).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2025-59/coif). Y.X.J.W. is the founder of Yingran Medicals Ltd., which develops medical image-based diagnostics software. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All imaging data were acquired with institutional ethical approval (approval ID: S1709442250839 for dataset-1 and 2017-YXZDK-002 for dataset-2) and with informed consent obtained from individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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