Relationship between contrast-enhanced ultrasound combined with ultrasound resolution microscopy imaging and histological features of hepatocellular carcinoma

Relationship between contrast-enhanced ultrasound combined with ultrasound resolution microscopy imaging and histological features of hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between contrast-enhanced ultrasound combined with ultrasound resolution microscopy imaging and histological features of hepatocellular carcinoma Feiqian Wang, Jingtong Yu, Xingqi Lu, Kazushi Numata, Litao Ruan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5513597/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2025 Read the published version in Abdominal Radiology → Version 1 posted 4 You are reading this latest preprint version Abstract Objectives Using contrast-enhanced ultrasound (CEUS) and ultrasound resolution microscopy (URM) imaging, this study aimed to evaluate the relationship between microvascular parameters of small hepatocellular carcinoma (sHCC) (≤ 3 cm) and microscopic histological features, which include vessels encapsulating tumour clusters (VETC), microvascular invasion (MVI), and histological grade. Methods Sixteen patients with solitary resected sHCC were prospectively enrolled. CEUS and URM were performed one week before resection. All “ratio” refers to comparisons between the active area (where CEUS microbubble show visible motion track by URM) and the entire lesion. Blood vessel complexity (ratio), blood vessel density (ratio), area (ratio), flow velocity, blood vessel diameter, and perfusion index (“flow velocity” × “vessel ratio”) were analysed using URM. The relationships between URM parameters and microscopic histological features (MVI, VETC, and histological grade) were analysed. Results There were 5 (31.3%), 8 (50%), and 7 (43.7%) cases of poorly differentiated, MVI-positive, and VETC-positive HCC, respectively. The mean velocity of the entire lesion was higher in the poorly differentiated group than that in the moderately differentiated group ( p = 0.026). The complexity ratio (MVI-positive: 1.07 ± 0.03, MVI-negative: 1.03 ± 0.02, p = 0.012), area ratio (MVI-positive: 0.63 ± 0.18, MVI-negative: 0.39 ± 0.16, p = 0.017), and perfusion index (MVI-positive: 8.67 ± 1.88, MVI-negative: 6.42 ± 0.94, p = 0.009) were greater in MVI-positive HCCs. The density ratio (VETC-positive: 1.30 ± 0.19, VETC-negative: 1.10 ± 0.05, p = 0.006) was larger in VETC-positive HCCs. Conclusion Higher blood flow velocity and area of HCC lesions, and higher blood vessel complexity and density may be related to microscopic histological features. This relationship might provide a strategy of using URM for preoperative non-invasive diagnostic VETC, MVI, and histological grade in the future. ultrasound resolution microscopy diagnosis hepatocellular carcinoma contrast-enhanced ultrasound microvessel Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Histological tissue specimens of hepatocellular carcinoma (HCC) lesions provide a wealth of information on microscopic histological features, such as histological grading, microvascular invasion (MVI), and vessels encapsulating tumour clusters (VETC). MVI is microscopically defined as the presence of cancer cell nests (≥ 50 cancer cells) in the vascular lumen lined with endothelial cells [ 1 ]. The VETC pattern is defined as the presence of sinusoid-like vessels that form web-like networks and encapsulate individual tumour clusters, visualized using cluster of differentiation (CD) 34 immunohistochemical staining [ 2 ]. Information on these microscopic histological features is useful for guiding clinical decision-making such as treatment planning and predicting the prognosis of patients with HCC. Patients with histologically poorly differentiated HCC, VETC-positive HCC, and MVI-positive HCC have worse survival than those with histologically well-differentiated HCC, VETC-negative HCC, and MVI-negative HCC. Patients with poorly differentiated HCC do not experience greater benefits from liver transplantation compared to resection [ 3 ]. For MVI-positive lesions, prognosis is better when anatomical hepatectomy is chosen over non-anatomical hepatectomy, a wider surgical margin > 1 cm is achieved, and postoperative adjuvant therapy is added [ 4 ]. Patients with VETC-positive HCC are more suitable for chemotherapy with sorafenib and repeated hepatectomy than those with transcatheter arterial chemoembolization and liver transplantation because of the reported differences in prognoses according to different treatment strategies [ 5 ]. However, it is currently impossible to obtain a preoperative diagnosis of these microscopic features to guide treatment. HCC is widely recognized as one of the most highly vascularised tumours, characterised by vascular heterogeneity, immaturity, high flow rate, and high permeability [ 6 ]. Generally, enhanced imaging modalities utilise these vascular features to diagnose HCC. Compared to contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), contrast-enhanced ultrasound (CEUS) offers widely recognised advantages, including non-radiation, non-invasiveness, good repeatability, real-time continuous scanning, portability, low cost, and safety, especially in patients with renal impairment or allergy to CT or MRI contrast agents [ 7 , 8 ]. The second-generation US contrast agent sulphur hexafluoride (SonoVue®, Bracco, Milan) is characterised as a pure “blood-pool” agent [ 9 ]. The microbubbles (MB) of the contrast agent mimic the behaviour of red blood cells in circulation [ 9 ]. Thus, they can accurately assess the microcirculation state. However, CEUS has limitations in evaluating the spatial structure and haemodynamic status of the microvasculature. Due to diffraction, the resolution of any US imaging system (including traditional US, CEUS, elastography US, three-dimensional US) is not less than half a wavelength (200 µm) [ 10 ], which limits the imaging of small blood vessels. In contrast to traditional CEUS, which uses MB to form images and obtain blood flow information based on their intensity, ultrasound resolution microscopy (URM) imaging detects and tracks the movement of MB signal with high frame-rate imaging and utilizes the locations of the MB to create super-resolved images [ 11 ]. In this manner, URM overcomes the diffraction limit of traditional US imaging systems and achieves unprecedented high-spatial-resolution images in living animals and human organs [ 12 , 13 ]. CEUS combined with URM can detect hepatic vessels with diameters of 153 µm in healthy humans [ 14 ] and 34 µm in HCC lesions in rats [ 15 ]. In addition, the microcirculation information provided by URM is multidimensional, including both structural and functional aspects, and can be quantified. URM data include vessel size, spatial vascular morphology, dynamic blood flow velocities, and microcirculation direction [ 16 ]. This overwhelming advantage of URM techniques makes them ideal for the study of microvasculature in human HCC lesions. However, no studies have explored the feasibility and usefulness of URM in patients with liver cancer. Improvement in patient survival depends on the detection of small HCC (sHCC) lesions, usually using imaging techniques [ 17 ]. However, sHCC are difficult to identify in cirrhotic liver with a coarse parenchymal pattern [ 18 ] and difficult to characterise even with contrast-enhanced imaging (because approximately 17% can be isodense on the arterial phase (AP) and 20–50% do not exhibit “wash-out” on the portal phase) [ 19 ]. This study aimed to explore the characteristics of the intratumoural microcirculation in sHCC lesions (largest diameter ≤ 3 cm) using CEUS and URM. In the context of this study, we aimed to determine whether URM parameters were related to the microscopic histological features of sHCC. If there is relationship between URM parameters and microscopic histological features of sHCC, it will be promising to further formulate strategies diagnose the histological grade, MVI, and VETC of sHCC using the novel URM. Materials and Methods Patient Enrolment The study protocol was approved by the Ethics Review Board of First Affiliated Hospital of the Xi’an Jiaotong University of China (approval number: No.XJTU1AF2023LSK-363; date of approval: 6 June 2023). Informed consent was obtained from all the participants. The inclusion criteria for the prospectively enrolled patients with HCC or lesions were as follows: (1) newly developed, untreated lesions; (2) solitary lesion and maximum tumour diameter not > 3 cm; (3) clear radiographic evidence of HCC; (4) Child-Pugh grade A or B; (5) agreement to undergo surgical resection for histopathological diagnosis; and (6) CEUS examination using SonoVue and URM video capture performed within one week before surgical resection. The collection was performed between January 2024 and July 2024. During this period, 87 patients with radiographic evidence of HCC underwent curative hepatectomies at our institution. The exclusion criteria were as follows: (1) a pathological diagnosis of non-HCC, including hepatocholangiocarcinoma, mixed type of liver cancer, sarcoidosis, abscess, and liver hypereosinophilic syndrome; (2) no definite diagnosis of the MVI and/or VETC pattern according to the hematoxylin–eosin (HE) and CD34 staining of surgical specimens; (3) poor quality of URM image, affecting the URM analysis; (4) incomplete preoperative serological data; (5) presence of macrovascular invasion or distant metastases diagnosed by CEUS, contrast-enhanced CT, and/or MRI; and (6) hepatectomy not performed (changed to other therapies such as radiofrequency ablation, hepatic artery embolization, and transcatheter arterial chemoembolization). The final study population comprised 16 patients (12 men and 4 women), with a mean age of 59.5 years (standard deviation: 10.7 years, range: 36 to 71 years). General baseline demographic and clinical data (including age, sex, aetiology of hepatitis, and lesion size) and preoperative serological indicators (including alpha-fetoprotein, serum albumin, total bilirubin, prothrombin time, platelets, and prothrombin induced by vitamin K absence II) were obtained by searching the electronic medical record system. The overall study design is shown in Fig. 1 . CEUS examination Grayscale US and CEUS of the liver were performed within one week prior to hepatectomy. An ULTIMUS 9E US system (VINNO, Jiangsu, China) equipped with native tissue harmonic grayscale imaging and CEUS functions was used. Convex probes with frequencies of 1–8 MHz and microconvex probes with frequencies of 3–10 MHz were used. Following grayscale US to confirm the size and position of the target hepatic lesion, SonoVue-enhanced CEUS was performed. A low mechanical index (0.06) was used for CEUS. A high concentration may cause overlap of adjacent MBs, resulting in imprecise localisation [ 16 ]. Thus, a 1 mL dose (lower than the conventional clinical dosage) of SonoVue was bolus-injected into an antecubital vein at 0.2 mL/s via a 20-gauge cannula, followed by 5 mL of 0.9% sterile sodium chloride solution. CEUS images were acquired and classified into three contrast phases: AP (10–20 to 30–50 s after injection initiation), portal phase (50–120 s after injection initiation), and delayed phase (> 120 s after injection initiation until MB disappeared). URM performance and analysis The workflow of URM is shown in Fig. 2 . After entering super-resolution CEUS mode, the “Start” button on the screen of the US system was clicked at the moment of agent injection (as described above) (Fig. 2 , step 1). After the contrast agent entered the observed image, the patient was asked to hold their breath for 10 s and the “URM acquisition” button was clicked (Fig. 2 , step 2). After location, tracking, and reconstruction of the MBs, raw URM images were obtained (Fig. 2 , step 3). The operator outlined the region of interest for subsequent analysis. In general, the region of interest should include the border of the tumour and a few surrounding areas for comparison. Subsequently, two conditions (settings) of “VSpeckle” and “Stabilizer” were adjusted. The goal of “VSpeckle” key is to adjust the balance between effective and noise signals. The smaller the Vspeckle, the greater the signal emitted and the higher the image noise. The larger the Vspeckle, the cleaner the image; however, with few supressed small-vessel signals. URM requires detection and accumulation of MB over time, assuming that the vascular structure remains stationary [ 20 ]. “Stabilizer” key is also known as “motion compensation”. To avoid the heart, breathing, and other unavoidable motion on the image interference, “Stabilizer” provides a simple correction for motion conditions, but increases the time cost [ 16 ]. Subsequently, static URM images were stored in the form of density, velocity, and direction maps, and dynamic MB motion trajectories were stored as videos in AVI format (Fig. 2 , step 4). The measured URM parameters and their significance are listed in Table S1 . Notably, considering the difference in the size of the 16 lesions, when assessing vascular complexity and density, the ratio of the active area to the entire lesion rather than the entire lesion was measured. URM parameters were independently measured by two radiologists (J.Y. and X.L., having five years of experience in abdominal imaging). The patients were unaware of their clinical histories or radiological reports. The final input of URM parameters was the average value measured by the two radiologists. Histopathological Examination In this study, the gold standard diagnoses of HCC, VETC, MVI, and histological grading were achieved based on surgical resection and observation under a light microscope. Open or laparoscopic resection was performed. Fresh surgical specimens, including paracancerous (< 1 cm from cancer tissues) and distal cancerous (approximately 5 cm from cancer tissues), were obtained from all enrolled lesions. All tissues were fixed in 10% neutral formalin, embedded in paraffin, and cut into 4 µm-thick sections. A senior pathologist (X.L., working in the field of liver pathological diagnosis for 12 years) reviewed all surgical specimens. For the diagnosis of MVI and histological grade, the sections were stained with HE, whereas VETC was diagnosed using CD34 staining. Patients with a visible VETC pattern on whole or part of the CD34 slides were identified as VETC-positive, and those without any VETC pattern were identified as VETC-negative. HCC lesions were classified into four histological grades based on the World Health Organization 4 tier system [ 21 ]: well-differentiated, moderately differentiated, poorly differentiated, and undifferentiated. This classification is mainly based on the assessment of cellular atypia and nuclear-cytoplasmic ratio. Statistical Analysis All data are expressed as mean ± standard deviation, as appropriate. Statistical comparisons of the baseline data were performed using the Mann–Whitney U test for numerical variables and the chi-square test for classification variables. The values of the URM parameters were compared using the Student’s t test. All statistical analyses were performed using SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Statistical significance was set at p < 0.05. Results The incidences of MVI and VETC were 50% (8/16) and 43.7% (7/16), respectively. No undifferentiated HCC were observed in this study. 31.25% (5/16), 37.5% (6/16), and 31.25% (5/16) HCCs were well, moderately, and poorly differentiated, respectively. Only one patient had neither cirrhosis nor a history of liver disease. Another case involved hepatitis C cirrhosis. The remaining 14 patients had hepatitis B cirrhosis. Only two lesions were located in the left lobe, all in segment 4, and the other 14 lesions were located in the right lobe (5, 5,1, and 3 lesions located in segments 5, 6, 7, and 8, respectively). There were no significant differences in baseline data (sex, alpha-fetoprotein, prothrombin time, albumin, platelet count, total bilirubin, and lesion diameter) between the MVI, VETC, and histological grading groups, except for the age of patients in the VETC group ( p = 0.018) (Table 1 ). Table 1 Demographic and serological characteristics of patients with HCC with different histological features 1 VETC MVI Histological grade Variables Total (N = 16) Positive (N = 9) Negative (N = 7) p value Positive (N = 8) Negative (N = 8) p value Well (N = 5) Moderately (N = 6) Poorly (N = 5) p value Age (year) 58.5 ± 10.7 51.7 ± 12.1 63.8 ± 5.6 0.018 59.9 ± 11.4 57.1 ± 10.4 0.623 53.0 ± 10.8 57.5 ± 12.1 62.2 ± 5.5 0.192 Gender, n (%) 0.771 0.077 2 0.837 Female 4 (25) 2 (12.5) 2 (12.5) 0 (0) 4 (25) 1 (6.3) 2 (12.5) 1 (6.3) Male 12 (75) 7 (43.8) 5 (31.3) 8 (50) 4 (25) 4 (25) 4 (25) 4 (25) AFP, n (%) 0.838 0.590 0.785 ≤ 200 ng/mL 11 (68.8) 6 (37.5) 5 (31.3) 5 (31.3) 6 (37.5) 3 (18.8) 4 (25) 4 (25) > 200 ng/mL 5 (31.3) 3 (18.8) 2 (12.5) 3 (18.8) 2 (12.5) 2 (12.5) 2 (12.5) 1 (6.3) Albumin (g/dL) 38.1 ± 4.8 39.2 ± 4.0 37.2 ± 5.4 0.425 36.4 ± 5.1 39.7 ± 4.2 0.176 41.3 ± 3.7 36.9 ± 5.8 36.3 ± 3.6 0.204 T-BIL (mg/dL) 19.3 ± 9.4 21.4 ± 12.1 17.6 ± 7.1 0.447 17.5 ± 6.2 21.1 ± 12.0 0.454 20.1 ± 6.2 19.6 ± 13.6 18.1 ± 7.8 0.950 Platelets (10^9/L) 125.7 ± 51.4 122.6 ± 69.0 128.1 ± 36.9 0.839 130.6 ± 32.8 120.8 ± 67.3 0.717 122.8 ± 54.5 138.0 ± 60.7 113.8 ± 43.7 0.757 PT (s) 13.0 ± 1.5 13.0 ± 1.7 13.0 ± 1.4 1.000 13.1 ± 1.0 12.9 ± 2.0 0.800 13.1 ± 1.2 12.7 ± 1.9 13.3 ± 1.6 0.816 PIVKA-II (mAU/mL) 227.0 ± 456.7 160.5 ± 186.7 278.8 ± 598.3 0.624 168.3 ± 219.5 285.8 ± 625.1 0.624 421.0 ± 786.7 153.9 ± 208.5 120.8 ± 199.1 0.548 Tumour diameter 3 (mm) 20.1 ± 6.7 23.1 ± 5.8 17.8 ± 6.6 0.113 21.0 ± 7.0 19.3 ± 6.7 0.618 17.8 ± 6.1 19.52 ± 6.7 23.2 ± 7.4 0.454 1 HCC, hepatocellular carcinoma; VETC, vessel-encapsulating tumour cluster; MVI, microvascular invasion; AFP, alpha-fetoprotein; T-BIL, total bilirubin; PT, prothrombin time; PIVKA-II, prothrombin induced by vitamin K absence II. 2 Since the minimum theoretical frequency (the number of female patients having MVI) is < 1, Fisher's exact probability test is used. 3 The tumour diameter indicated the largest diameter of the lesion. The mean largest diameter of the 16 lesions was 20.1 ± 6.7 mm (range: 11 to 30 mm). The minimum and maximum flow velocity measured across these lesions was 1.13 cm/s and 16.7 cm/s, respectively. The measured minimum and maximum vessel diameters were 60 µm and 1330 µm, respectively. The minimum and maximum blood vessel densities were 0.03 and 17.86, respectively. As shown in Table 2 , the mean velocity in the active area of histologically well differentiated HCCs (9.96 ± 0.93 mm/s) was lower than that of poorly differentiated HCCs (12.07 ± 1.56 mm/s) ( p = 0.025). In addition, the mean velocity of the entire lesion was higher in poorly differentiated HCCs (11.92 ± 1.84 mm/s) than in well differentiated (9.12 ± 0.81 mm/s, p = 0.003) and moderately differentiated (10.03 ± 0.89 mm/s, p = 0.026) HCCs. Table 2 The relationship between URM parameters and microscopic histological features of HCC lesions 1 Total (N = 16) VETC MVI Histological grade Variables Positive (N = 7) Negative (N = 9) p value Positive (N = 8) Negative (N = 8) p value Well (N = 5) Moderately (N = 6) Poorly (N = 5) p value Mean velocity in the active area (mm/s) 11.15 ± 1.51 11.13 ± 1.16 11.16 ± 1.81 0.977 11.75 ± 1.62 10.55 ± 1.19 0.114 9.96 ± 0.93 11.36 ± 1.37 12.07 ± 1.56 0.025 2 Mean velocity in the entire lesion (mm/s) 10.34 ± 1.64 9.63 ± 0.70 10.89 ± 2.00 0.133 11.04 ± 1.84 9.64 ± 1.13 0.087 9.12 ± 0.81 10.03 ± 0.89 11.92 ± 1.84 0.003 2 / 0.026 3 Complexity ratio 1.05 ± 0.03 1.07 ± 0.03 1.04 ± 0.03 0.097 1.07 ± 0.03 1.03 ± 0.02 0.012 1.04 ± 0.03 1.06 ± 0.03 1.03 ± 0.02 > 0.05 4 Active area (cm 2 ) 1.58 ± 1.06 2.07 ± 1.34 1.21 ± 0.64 0.157 1.84 ± 1.17 1.33 ± 0.95 0.352 1.14 ± 0.53 1.35 ± 1.11 2.31 ± 1.20 > 0.05 4 Entire lesion area (cm 2 ) 3.08 ± 1.64 3.96 ± 1.73 2.39 ± 1.26 0.053 3.00 ± 1.87 3.15 ± 1.50 0.862 2.24 ± 1.03 2.80 ± 1.55 4.24 ± 1.82 > 0.05 4 Area ratio 0.51 ± 0.21 0.47 ± 0.23 0.54 ± 0.19 0.503 0.63 ± 0.18 0.39 ± 0.16 0.017 0.55 ± 0.17 0.43 ± 0.21 0.56 ± 0.25 > 0.05 4 Perfusion index 7.54 ± 1.84 7.73 ± 2.47 7.40 ± 1.32 0.730 8.67 ± 1.88 6.42 ± 0.94 0.009 7.57 ± 2.13 7.87 ± 2.30 7.13 ± 1.11 > 0.05 4 Density ratio 1.18 ± 0.17 1.30 ± 0.19 1.10 ± 0.05 0.006 1.24 ± 0.21 1.13 ± 0.10 0.208 1.27 ± 0.24 1.20 ± 0.12 1.07 ± 0.05 > 0.05 4 Mean vessel diameters of the active area (mm) 0.31 ± 0.08 0.33 ± 0.11 0.30 ± 0.05 0.456 0.32 ± 0.07 0.31 ± 0.10 0.953 0.30 ± 0.06 0.34 ± 0.11 0.30 ± 0.05 > 0.05 4 Mean vessel diameters of the entire lesion (mm) 0.30 ± 0.07 0.27 ± 0.10 0.31 ± 0.05 0.304 0.30 ± 0.05 0.29 ± 0.09 0.695 0.29 ± 0.06 0.28 ± 0.10 0.32 ± 0.05 > 0.05 4 1 HCC, hepatocellular carcinoma; VETC, vessel-encapsulating tumour clusters; MVI, microvascular invasion; URM, ultrasound resolution microscopy. 2 This p value was yielded when analysed between well differentiated HCC and poorly differentiated HCC. 3 This p value was obtained when analysed between moderately differentiated HCC and poorly differentiated HCC. 4 When either of the two grades of these three histological grades were compared, the p value is over 0.05. The complexity ratio (MVI-positive: 1.07 ± 0.03, MVI-negative: 1.03 ± 0.02, p = 0.012) and area ratio (MVI-positive: 0.63 ± 0.18, MVI-negative: 0.39 ± 0.16, p = 0.017) were greater in MVI-positive HCCs than in MVI-negative HCCs. The perfusion index was higher in the MVI-positive group (8.67 ± 1.88) than in the MVI-negative group (6.42 ± 0.94) ( p = 0.009). The density ratio (VETC-positive: 1.30 ± 0.19, VETC-negative: 1.09 ± 0.05, p = 0.006) was larger in VETC-positive HCC than in VETC-negative HCC. However, blood vessel diameters, regardless of the maximum, minimum, or mean values, entire lesion area, and area ratio, showed no relationship with any of the three microscopic histological features. The measurement of URM parameters and microscopic histological features of the two typical cases are shown in Fig. 3 and Fig. 4 . Discussion HCC lesions typically display fine, branching patterns of increased vascularity with greater flow velocity than metastatic lesions or haemangiomas [ 22 ]. However, the current imaging modalities lack the accuracy needed to reliably detect intratumoural microvessels. Intratumoural vessels were only visible on enhanced MRI in 43% of the AP cases [ 23 ]. Colour Doppler flow US imaging has been used to visualise intratumoural blood vessels with diameters > 1 mm and flow velocities > 3–5 cm/s [ 24 ]. However, the limitation of conventional imaging techniques is the low resolution of microvessels. Using a stream-of-pulses model for CEUS signals, URM achieved detection limits of 40 µm width for the main vessel, 15 mm/s for the peak velocity, and 25 µm width for the secondary vessels [ 12 ]. In this study, URM detected intratumoural vessels in all 16 HCCs, with a minimum microvessel diameter of 60 µm and a minimum flow velocity of 11.3 mm/s, closely aligning with the limit of detection of the abiotic model reported in the literature [ 12 ]. These results preliminarily verify the feasibility of URM in patients with liver cancer. During multistep hepatocarcinogenesis, immature arterial tumour vessels develop and increase markedly, while sinusoidal capillarization opens [ 25 ]. Blood flow in the portal vein initially decreases, then reverses, and eventually increases [ 25 ]. These dramatic changes in haemodynamics synergistically contribute to an increased blood supply as HCC progresses. Consistent with this finding, a study assessing HCCs using the CEUS Maximum Intensity Projection technique concluded that well differentiated HCCs exhibited either normal or non-clearly visible intratumoural vasculature. By contrast, poorly differentiated HCCs exhibited tortuous, meandering, tapering, and interrupted intratumoural blood vessels [ 19 ]. Tumour vessels accelerate as they flex and meander. Based on CEUS and contrast enhanced CT findings, the degree of arterial vascularity in HCC is closely correlated with the degree of differentiation [ 26 ]. Therefore, abnormal vessel running and increased blood supply likely contribute to increased velocity as histological grade advances. MVI and VETC represent classical passive and newly discovered active metastatic patterns of tumour cells, respectively [ 7 ]. The significance of VETC was first recognised due to its association with MVI [ 27 ]. Eighty percent of MVI-positive lesions are also positive for VETC [ 27 ]. Therefore, some studies have preferred to combine MVI and VETC for analysis [ 28 , 29 ]. Nevertheless, based on our URM analysis, the intratumoural microvascular characteristics of MVI and VETC may differ. The presence of MVI is associated with the blood vessel complexity ratio. Compared to normal hepatic vessels, intratumoural neovascularization is characterised by aberrant structural dynamics and immature, tortuous, and hyperpermeable vessels [ 30 ]. This abnormality in tumour vessels can contribute significantly to immune system evasion and metastasis [ 31 ], leading to MVI. We also found that the area ratio, rather than the entire lesion area, was associated with the occurrence of MVI. This may be due to the widely recognised characteristics of high intratumoural heterogeneity in HCC. Haemorrhage, necrosis, and steatosis are commonly observed in HCC lesions. Compared to large HCCs, steatosis is more often observed in sHCCs. Hypoxia and insufficient tumour vessel development lead to HCC steatosis, whereas fatty hepatic tissue impairs the liver microcirculation and promotes ischemic injury [ 32 ]. A larger active area (rather than the entire area with haemorrhage, necrosis, and/or steatosis) predicts more vascular carriers that transport cancer cells and promote MVI. Our study showed that the active area of the MVI-positive group was larger than that of the MVI-negative group; however, the difference was not statistically significant, possibly because the sample size was small. The perfusion index calculated using URM images revealed differences between the MVI groups in our study. In addition to the perfusion index in our study, the perfusion parameters of triphasic CT (such as portal vein blood supply perfusion, hepatic artery perfusion index, and arterial enhancement fraction) [ 33 ] and contrast-enhanced MRI (such as portal venous flow and arterial fraction) are valuable for predicting MVI [ 34 ]. These findings could be explained by cancer cell invasion of blood vessels, which leads to the formation of arteriovenous fistulas, resulting in increased blood flow perfusion [ 35 ]. In contrast to MVI, VETC was associated with intratumoural microvessel density in the present study; however, its mechanism remains unknown. VETC-positive tumour cells express much higher levels of angiopoietin 2 (involved in anogenesis) [ 36 ] and carbonic anhydrase IX (a hypoxia marker that leads to angiogenesis) [ 27 ] than VETC-negative tumour cells. By counting the number of CD31-stained blood vessels per square millimetre of the tumour, Huang et al. confirmed that higher intratumoural microvessel density was significantly associated with the VETC pattern [ 37 ]. Nevertheless, unlike Huang’s method, which relies on surgically resected specimens, our imaging methods are non-invasive and suitable for early diagnosis. A larger tumour size (> 5 cm) is closely related to, or may even serve as an independent predictor of, the VETC pattern [ 2 , 38 ]. Unlike the previous study that included HCCs of all sizes, our study showed that for small lesions, lesion size (both the largest diameter of lesion shown in Table 1 and the entire lesion area shown in Table 2 ) had no statistical relationship with the incidence of the VETC pattern. In other words, the present study raises a new viewpoint that for sHCC, a large lesion size may not be a risk factor for VETC positivity. In this study, the vessel diameter was not associated with any of the three microscopic histological features (MVI, VETC, and histological grade). In general, the maximum flow velocity occurs at the centre of the blood flow, whereas the minimum flow velocity occurs at the inner wall of the blood vessel [ 39 ]. Moreover, with an increase in vessel diameter, resistance decreases, and blood flow increases [ 40 ]. Therefore, in theory, the wider the blood vessels, the less likely the circulating tumour cells are to be destroyed by shear stress and the immune system, leading to haematogenous metastasis. Conversely, a smaller vessel diameter increases the chance of collisions between cancer cells and host cells, such as leukocytes, erythrocytes, and endothelial cells, circulating in the blood, which may reduce the flow rate and further increase the metastatic potential of cancer cells [ 41 ]. These negative results may be attributed to the small sample size of the present study. However, some of the underlying factors may have remained unknown. This study has several limitations. First, because the image quality and accuracy of URM analysis are easily affected by the respiratory motion and penetration depth, its widespread application in clinical practice is challenging. Second, the sample size was small, and the statistical analysis was simple. In addition, no diagnostic model has been established yet. Nevertheless, this study is valuable as it represents an early attempt to analyse a real-world human population with HCC. These positive results indicate that the clinical application of URM in liver cancer is promising. As a pioneering study, it serves as a reference for future research. Conclusion The positive microvascular findings of our URM study indicate that the high density and complexity of microvessels, high flow velocity, and large vessel area of sHCC lesions may correlate with their microscopic histological features, including high histological grade, positive VETC, and MVI pattern. In the future, we plan to increase the sample size and conduct a multicentre study to further explore and validate the role of URM in HCC diagnosis. The idea of using URM technique as a noninvasive tool for the preoperative diagnosis of the microscopic histological features might be promising. Abbreviations MB, microbubble; MVI, microvascular invasion; VETC, vessels encapsulating tumour clusters; US, ultrasound; URM, ultrasound resolution microscopy; HCC, hepatocellular carcinoma; sHCC, small HCC; CEUS, contrast-enhanced ultrasound; CT, contrast-enhanced computed tomography; MRI, magnetic resonance imaging; AP, arterial phase; CD, cluster of differentiation; HE, hematoxylin–eosin. Declarations Author Contribution Litao Ruan and Feiqian Wang contributed to the design of the research. Xi Liu and Wenbin Zhang implemented the research. Xingqi Lu and Jingtong Yu curated the data and analyzed the results. Xiaojing Li and Mingxi Wan wrote the original draft of the manuscript. Kazushi Numata reviewed and edited the manuscript. Dong Zhang supervised the project. Feiqian Wang and Guanjun Zhang contributed to funding acquisition. 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Wu L, Yang C, Halim A, Rao S, Xu P, Feng W, Chen C, Ji Y, Zhu J, Zeng M: Contrast-enhanced magnetic resonance imaging perfusion can predict microvascular invasion in patients with hepatocellular carcinoma (between 1 and 5 cm) . Abdominal radiology (New York) 2022, 47 (9):3264-3275. Dong Y, Qiu Y, Yang D, Yu L, Zuo D, Zhang Q, Tian X, Wang WP, Jung EM: Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma . Clinical hemorheology and microcirculation 2021, 77 (4):461-469. Hanley KL, Feng GS: A new VETC in hepatocellular carcinoma metastasis . Hepatology (Baltimore, Md) 2015, 62 (2):343-345. Huang CW, Lin SE, Huang SF, Yu MC, Tang JH, Tsai CN, Hsu HY: The Vessels That Encapsulate Tumor Clusters (VETC) Pattern Is a Poor Prognosis Factor in Patients with Hepatocellular Carcinoma: An Analysis of Microvessel Density . Cancers 2022, 14 (21). Feng Z, Li H, Zhao H, Jiang Y, Liu Q, Chen Q, Wang W, Rong P: Preoperative CT for Characterization of Aggressive Macrotrabecular-Massive Subtype and Vessels That Encapsulate Tumor Clusters Pattern in Hepatocellular Carcinoma . Radiology 2021, 300 (1):219-229. Haowei MA, Abdul-Reda Hussein U, Haleem Al-Qaim Z, M. A. Altalbawy F, Ai_Sadi Hl, Abdulhussain Fadhil A, Mohammed Al-Taee M, Hadrawi SK, Muhsin Khalaf R, Hazim Jirjees I et al : Employing Sisko non-Newtonian model to investigate the thermal behavior of blood flow in a stenosis artery: Effects of heat flux, different severities of stenosis, and different radii of the artery . Alexandria Engineering Journal 2023, 68 :291-300. Norouzpour A, Hooshyar Z, Mehdizadeh A: Autoregulation of blood flow: Vessel diameter changes in response to different temperatures . Journal of biomedical physics & engineering 2013, 3 (2):63-66. Azevedo AS, Follain G, Patthabhiraman S, Harlepp S, Goetz JG: Metastasis of circulating tumor cells: favorable soil or suitable biomechanics, or both? Cell adhesion & migration 2015, 9 (5):345-356. Errico C, Pierre J, Pezet S, Desailly Y, Lenkei Z, Couture O, Tanter M: Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging . Nature 2015, 527 (7579):499-502. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2025 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 05 Dec, 2024 Editor assigned by journal 25 Nov, 2024 Submission checks completed at journal 25 Nov, 2024 First submitted to journal 24 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5513597","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382521733,"identity":"21330854-9384-4cf8-b6b1-b4b3b318ab73","order_by":0,"name":"Feiqian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PrQoCQRDA8RHhLAdXx6DPsLKwGARfZSyXVjFeEDGdQdRH8BV8hFVByx7WC4aziOGCRou4fgRBuLto2H8YGJhfGACb7V8LAMEBKCUUtAoS/SZllmi/MHnlVI/hOv+a7aLNWQXN2qwSbQNyFHjjCWUT3fObSiMP3Z4fk3sA1NEykwglBb+G2AlBipjwBAy7OWSfCra6G+Klok9sU4DEkierkSEoBRAVIO04FaC25hdMOZLy3dxfqnPJL2owrC3msnG93Vt1bzzNJiYHzSiNPpubd/6sfPkmNpvNZvvpAc/2ThJTFfbtAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Feiqian","middleName":"","lastName":"Wang","suffix":""},{"id":382521734,"identity":"b0660f2b-2f03-414a-9612-29c0e326f122","order_by":1,"name":"Jingtong Yu","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jingtong","middleName":"","lastName":"Yu","suffix":""},{"id":382521735,"identity":"d1c4307d-86a1-47c6-9fbc-ac1238be839b","order_by":2,"name":"Xingqi Lu","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xingqi","middleName":"","lastName":"Lu","suffix":""},{"id":382521736,"identity":"1ad30b57-00e7-4b1a-85f2-6d1d724a91e0","order_by":3,"name":"Kazushi Numata","email":"","orcid":"","institution":"Yokohama City University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kazushi","middleName":"","lastName":"Numata","suffix":""},{"id":382521737,"identity":"0c2f84f1-7e39-44dc-ad83-9cfce96f0006","order_by":4,"name":"Litao Ruan","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Litao","middleName":"","lastName":"Ruan","suffix":""},{"id":382521738,"identity":"e108b791-28ef-463a-9f58-f2319f4e2b8d","order_by":5,"name":"Dong Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Zhang","suffix":""},{"id":382521739,"identity":"f6999e07-f3d8-4fdc-8b95-d392bf2a9c6e","order_by":6,"name":"Xi Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Liu","suffix":""},{"id":382521740,"identity":"53a84d28-5a89-462d-8600-891938415df2","order_by":7,"name":"Xiaojing Li","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Li","suffix":""},{"id":382521741,"identity":"c6e570b9-adbe-4283-b5ad-ca4e792172d3","order_by":8,"name":"Mingxi Wan","email":"","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Mingxi","middleName":"","lastName":"Wan","suffix":""},{"id":382521742,"identity":"a43d5479-a26c-4c69-b6e4-c0c305c8bfd9","order_by":9,"name":"Wenbin Zhang","email":"","orcid":"","institution":"VINNO Technology Company Limited","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Zhang","suffix":""},{"id":382521743,"identity":"dd91a3de-d42b-40fd-ae3c-9dc4f5c94c1b","order_by":10,"name":"Guanjun Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Xi'an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Guanjun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-24 11:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5513597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5513597/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-025-04825-y","type":"published","date":"2025-02-10T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71895823,"identity":"3746799a-6e01-445c-b2be-46b1d6e31020","added_by":"auto","created_at":"2024-12-19 13:39:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40480266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the patient selection process.\u003c/strong\u003e HCC, hepatocellular carcinoma; VETC, vessels encapsulating tumour clusters; MVI, microvascular invasion; CEUS, contrast-enhanced ultrasound; URM, ultrasound resolution microscopy.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/56de9b1dc0b124e4b56db074.png"},{"id":71895878,"identity":"5bfffe9c-7bb6-49c9-8bb4-8158746ea7ed","added_by":"auto","created_at":"2024-12-19 13:39:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176652991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe imaging principle, method, and process of URM. \u003c/strong\u003eStep 1: Inject the contrast agent, particularly at low concentration. The aim is to achieve a sparse distribution of MB in the blood pool of circulation, for improving the echo signal of the blood, thereby increasing the signal-to-noise ratio. Step 2: Acquire raw data of MB through ultrafast CEUS imaging at high frame rates. This mechanism uses phase superposition to filter out tissue signals with linear oscillation characteristics, thereby retaining MB information. The red box indicates the bandwidth of the useful spectrum after filtering. Step 3: Localise the MBs and track their motion trajectories based on amplitude, gradient, and motion compensation. Step 4: Employ a super algorithm to obtain URM imaging maps (i.e., a microcirculatory blood vessel structure map) based on the movement trajectory, speed, and direction of the MB. MB, microbubble; URM, ultrasound resolution microscopy; CEUS, contrast-enhanced ultrasound.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/b40bc5290b8d60c8e47a1138.png"},{"id":71895875,"identity":"1ba116e8-d25f-45fe-b538-7d390b8e67e6","added_by":"auto","created_at":"2024-12-19 13:39:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83068941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eURM images and pathological photographs of a VETC (+), MVI (+), and histologically moderately differentiated case in our study. \u003c/strong\u003eThe patient was a 54 years old female with chronic hepatitis B virus as the primary disease. (a) and (b) show the lesions via the convex and linear array probes, respectively. The lesion appears as round, heterogeneous hyperechoic area with peritumoral hypoechoic halo, approximately 2.4 × 2.2 cm in size. (c) and (d) display the arterial and delayed phase of CEUS, respectively. (e) is the direction map. The active area was determined according to this map and its related video. The active area and entire lesion measured 2.5 cm\u003csup\u003e2\u003c/sup\u003e and 3.7 cm\u003csup\u003e2\u003c/sup\u003e, respectively, with an area ratio of 0.68 (mean ± standard deviation [SD] of area ratio for the MVI-positive group was 0.63 ± 0.18). (f) shows the velocity map. The mean velocity in the active area was 10.26 mm/s, whereas it was 9.83 mm/s in the entire lesion. (g–h) presents the density map, with (g) showing the measurement of complexity and density. The complexity ratio was 1.06 (1.67 ÷ 1.57) (MVI-positive group: 1.07 ± 0.03), whereas the density ratio was 1.21 (0.80 ÷ 0.67) (VETC-positive: 1.30 ± 0.19). The calculated perfusion index was 6.7 (10.26 × 0.65) (mean ± SD of perfusion index for the MVI-positive group was 8.67 ± 1.88). (i) shows the measurement of vascular diameter and distance between vessels, with a mean diameter of 0.36 mm within active area and 0.19 mm in the rest of the lesion. (j–n) are microscopic pictures of surgically resected specimen at different magnifications. (j–l) display hematoxylin–eosin (HE) staining, whereas (m–n) show CD34 immunohistochemical staining. HE staining showed obvious cancer cell and nuclear atypia, including hypercellularity, increased nuclear-cytoplasmic ratio, and enlarged, deformed nuclei, consistent with histologically moderately differentiated HCC. (k–l) and (n) are local magnifications (magnified portion of the black box portion) of (j) and (m), respectively. Green arrowheads in (k) and (l) indicate the clusters of cancer cells within the microvascular lumens adjacent to the lesion area (MVI-positive). In (n), the cancer cells are tightly arrayed in a network of sinusoidal microvessels, indicating VETC-positivity. (h) and (o) shows the gross pathology of fresh hepatectomy specimen. A gray-white tumour approximately 3.0 × 2.2 cm in size with focal haemorrhage and necrosis is observed in this section, with intact surrounding fibrous capsule. White arrowheads in (a–h) and (o) indicate the lesion location. The hand-drawn yellow irregular curve and the white circle in (g) represent the active area and the entire lesion, respectively. The dotted line in (i) indicates the section where vessel diameter and direction between vessels were measured.\u0026nbsp;\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/ece4604daa5ab0924b44a4a6.png"},{"id":71895861,"identity":"5260854f-99d7-47d4-a684-f33f8452e7e1","added_by":"auto","created_at":"2024-12-19 13:39:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52301358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eURM images and pathological photographs of a VETC (-), MVI (-), and histologically well differentiated case in our study. \u003c/strong\u003eThe patient was a 67 years old male with chronic hepatitis B virus as the primary disease. (a) shows the lesions via a linear array probe, appearing as round, with heterogeneous hypoechogenicity, and approximately 2.7 × 2.5 cm in size. (b) and (c) show the arterial and delayed phase of CEUS, respectively. (d) is the direction map. The active area was determined according to this map and its related video. The active area and entire lesion were 1.4 cm\u003csup\u003e2\u003c/sup\u003e and 4.9 cm\u003csup\u003e2\u003c/sup\u003e, respectively, with an area ratio of 0.29 (mean ± standard deviation [SD] of area ratio for the MVI-negative group was 0.39 ± 0.16). (e) shows the velocity map. The mean velocity in the active and entire areas were 10.14 mm/s and 9.29 mm/s, respectively. (f–g) display the density map. (f) shows the measurement of complexity and density. The calculated complexity ratio was 1.09 (1.90 ÷ 1.74) (mean ± SD of complexity ratio for the MVI-negative group was 0.93 ± 0.03), whereas density ratio was 1.08 (mean ± SD of density ratio for the VETC-negative group was 1.10 ± 0.05). The calculated perfusion index was 4.9 (10.14 × 0.48) (mean ± SD of perfusion index for the MVI-negative group was 6.42 ± 0.94). (g) shows the measurement of vascular diameter and distance between vessels, with a mean diameter of 0.51 mm in the active area and 0.46 mm in the rest of the lesion. The hand-drawn yellow curve and white oval in (f) show the active area and entire lesion, respectively. (h–k) are microscopic photographs of surgically resected specimen at different magnifications. (h–i) show hematoxylin–eosin (HE) staining, whereas (j–k) display CD34 immunohistochemical staining. In (i), HE staining shows slight cellular atypia, with an increased nuclear-cytoplasmic ratio and hypercellularity. The tumour cells show a clear and thin trabecular cord pattern. The trabecular structure distorted to thickness of up to two cell layers. No cancer cells were observed within the microvascular lumen adjacent to the cancer, leading to an MVI-negative diagnosis. In (k), sinusoidal microvessels lack tightly constructed network, indicating VETC-negativity. White arrowheads in (a–f) indicate the lesion location. The dotted line in (g) shows the section where vessel diameter and direction between vessels were measured.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/86df85a0ad1b498627dc9538.png"},{"id":71895565,"identity":"9cf3ffbf-f70d-4fb2-a496-30a4ac7a1fc9","added_by":"auto","created_at":"2024-12-19 13:38:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1785716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/ee394432-bba9-43e1-bf5f-809de3674237.pdf"},{"id":71896424,"identity":"9453bc76-17e0-40c4-8bed-42de42bef6c5","added_by":"auto","created_at":"2024-12-19 13:47:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":465627,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-5513597/v1/e9ed8cfba482578d24103371.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between contrast-enhanced ultrasound combined with ultrasound resolution microscopy imaging and histological features of hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHistological tissue specimens of hepatocellular carcinoma (HCC) lesions provide a wealth of information on microscopic histological features, such as histological grading, microvascular invasion (MVI), and vessels encapsulating tumour clusters (VETC). MVI is microscopically defined as the presence of cancer cell nests (\u0026ge;\u0026thinsp;50 cancer cells) in the vascular lumen lined with endothelial cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The VETC pattern is defined as the presence of sinusoid-like vessels that form web-like networks and encapsulate individual tumour clusters, visualized using cluster of differentiation (CD) 34 immunohistochemical staining [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Information on these microscopic histological features is useful for guiding clinical decision-making such as treatment planning and predicting the prognosis of patients with HCC. Patients with histologically poorly differentiated HCC, VETC-positive HCC, and MVI-positive HCC have worse survival than those with histologically well-differentiated HCC, VETC-negative HCC, and MVI-negative HCC. Patients with poorly differentiated HCC do not experience greater benefits from liver transplantation compared to resection [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For MVI-positive lesions, prognosis is better when anatomical hepatectomy is chosen over non-anatomical hepatectomy, a wider surgical margin\u0026thinsp;\u0026gt;\u0026thinsp;1 cm is achieved, and postoperative adjuvant therapy is added [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients with VETC-positive HCC are more suitable for chemotherapy with sorafenib and repeated hepatectomy than those with transcatheter arterial chemoembolization and liver transplantation because of the reported differences in prognoses according to different treatment strategies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, it is currently impossible to obtain a preoperative diagnosis of these microscopic features to guide treatment.\u003c/p\u003e \u003cp\u003eHCC is widely recognized as one of the most highly vascularised tumours, characterised by vascular heterogeneity, immaturity, high flow rate, and high permeability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Generally, enhanced imaging modalities utilise these vascular features to diagnose HCC. Compared to contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), contrast-enhanced ultrasound (CEUS) offers widely recognised advantages, including non-radiation, non-invasiveness, good repeatability, real-time continuous scanning, portability, low cost, and safety, especially in patients with renal impairment or allergy to CT or MRI contrast agents [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The second-generation US contrast agent sulphur hexafluoride (SonoVue\u0026reg;, Bracco, Milan) is characterised as a pure \u0026ldquo;blood-pool\u0026rdquo; agent [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The microbubbles (MB) of the contrast agent mimic the behaviour of red blood cells in circulation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, they can accurately assess the microcirculation state. However, CEUS has limitations in evaluating the spatial structure and haemodynamic status of the microvasculature. Due to diffraction, the resolution of any US imaging system (including traditional US, CEUS, elastography US, three-dimensional US) is not less than half a wavelength (200 \u0026micro;m) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which limits the imaging of small blood vessels. In contrast to traditional CEUS, which uses MB to form images and obtain blood flow information based on their intensity, ultrasound resolution microscopy (URM) imaging detects and tracks the movement of MB signal with high frame-rate imaging and utilizes the locations of the MB to create super-resolved images [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this manner, URM overcomes the diffraction limit of traditional US imaging systems and achieves unprecedented high-spatial-resolution images in living animals and human organs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. CEUS combined with URM can detect hepatic vessels with diameters of 153 \u0026micro;m in healthy humans [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and 34 \u0026micro;m in HCC lesions in rats [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, the microcirculation information provided by URM is multidimensional, including both structural and functional aspects, and can be quantified. URM data include vessel size, spatial vascular morphology, dynamic blood flow velocities, and microcirculation direction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This overwhelming advantage of URM techniques makes them ideal for the study of microvasculature in human HCC lesions. However, no studies have explored the feasibility and usefulness of URM in patients with liver cancer.\u003c/p\u003e \u003cp\u003eImprovement in patient survival depends on the detection of small HCC (sHCC) lesions, usually using imaging techniques [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, sHCC are difficult to identify in cirrhotic liver with a coarse parenchymal pattern [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and difficult to characterise even with contrast-enhanced imaging (because approximately 17% can be isodense on the arterial phase (AP) and 20\u0026ndash;50% do not exhibit \u0026ldquo;wash-out\u0026rdquo; on the portal phase) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study aimed to explore the characteristics of the intratumoural microcirculation in sHCC lesions (largest diameter\u0026thinsp;\u0026le;\u0026thinsp;3 cm) using CEUS and URM. In the context of this study, we aimed to determine whether URM parameters were related to the microscopic histological features of sHCC. If there is relationship between URM parameters and microscopic histological features of sHCC, it will be promising to further formulate strategies diagnose the histological grade, MVI, and VETC of sHCC using the novel URM.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Enrolment\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the Ethics Review Board of First Affiliated Hospital of the Xi\u0026rsquo;an Jiaotong University of China (approval number: No.XJTU1AF2023LSK-363; date of approval: 6 June 2023). Informed consent was obtained from all the participants. The inclusion criteria for the prospectively enrolled patients with HCC or lesions were as follows: (1) newly developed, untreated lesions; (2) solitary lesion and maximum tumour diameter not \u0026gt;\u0026thinsp;3 cm; (3) clear radiographic evidence of HCC; (4) Child-Pugh grade A or B; (5) agreement to undergo surgical resection for histopathological diagnosis; and (6) CEUS examination using SonoVue and URM video capture performed within one week before surgical resection. The collection was performed between January 2024 and July 2024. During this period, 87 patients with radiographic evidence of HCC underwent curative hepatectomies at our institution. The exclusion criteria were as follows: (1) a pathological diagnosis of non-HCC, including hepatocholangiocarcinoma, mixed type of liver cancer, sarcoidosis, abscess, and liver hypereosinophilic syndrome; (2) no definite diagnosis of the MVI and/or VETC pattern according to the hematoxylin\u0026ndash;eosin (HE) and CD34 staining of surgical specimens; (3) poor quality of URM image, affecting the URM analysis; (4) incomplete preoperative serological data; (5) presence of macrovascular invasion or distant metastases diagnosed by CEUS, contrast-enhanced CT, and/or MRI; and (6) hepatectomy not performed (changed to other therapies such as radiofrequency ablation, hepatic artery embolization, and transcatheter arterial chemoembolization).\u003c/p\u003e \u003cp\u003eThe final study population comprised 16 patients (12 men and 4 women), with a mean age of 59.5 years (standard deviation: 10.7 years, range: 36 to 71 years). General baseline demographic and clinical data (including age, sex, aetiology of hepatitis, and lesion size) and preoperative serological indicators (including alpha-fetoprotein, serum albumin, total bilirubin, prothrombin time, platelets, and prothrombin induced by vitamin K absence II) were obtained by searching the electronic medical record system. The overall study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCEUS examination\u003c/h3\u003e\n\u003cp\u003eGrayscale US and CEUS of the liver were performed within one week prior to hepatectomy. An ULTIMUS 9E US system (VINNO, Jiangsu, China) equipped with native tissue harmonic grayscale imaging and CEUS functions was used. Convex probes with frequencies of 1\u0026ndash;8 MHz and microconvex probes with frequencies of 3\u0026ndash;10 MHz were used.\u003c/p\u003e \u003cp\u003eFollowing grayscale US to confirm the size and position of the target hepatic lesion, SonoVue-enhanced CEUS was performed. A low mechanical index (0.06) was used for CEUS. A high concentration may cause overlap of adjacent MBs, resulting in imprecise localisation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, a 1 mL dose (lower than the conventional clinical dosage) of SonoVue was bolus-injected into an antecubital vein at 0.2 mL/s via a 20-gauge cannula, followed by 5 mL of 0.9% sterile sodium chloride solution. CEUS images were acquired and classified into three contrast phases: AP (10\u0026ndash;20 to 30\u0026ndash;50 s after injection initiation), portal phase (50\u0026ndash;120 s after injection initiation), and delayed phase (\u0026gt;\u0026thinsp;120 s after injection initiation until MB disappeared).\u003c/p\u003e\n\u003ch3\u003eURM performance and analysis\u003c/h3\u003e\n\u003cp\u003eThe workflow of URM is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After entering super-resolution CEUS mode, the \u0026ldquo;Start\u0026rdquo; button on the screen of the US system was clicked at the moment of agent injection (as described above) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, step 1). After the contrast agent entered the observed image, the patient was asked to hold their breath for 10 s and the \u0026ldquo;URM acquisition\u0026rdquo; button was clicked (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, step 2). After location, tracking, and reconstruction of the MBs, raw URM images were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, step 3). The operator outlined the region of interest for subsequent analysis. In general, the region of interest should include the border of the tumour and a few surrounding areas for comparison. Subsequently, two conditions (settings) of \u0026ldquo;VSpeckle\u0026rdquo; and \u0026ldquo;Stabilizer\u0026rdquo; were adjusted. The goal of \u0026ldquo;VSpeckle\u0026rdquo; key is to adjust the balance between effective and noise signals. The smaller the Vspeckle, the greater the signal emitted and the higher the image noise. The larger the Vspeckle, the cleaner the image; however, with few supressed small-vessel signals. URM requires detection and accumulation of MB over time, assuming that the vascular structure remains stationary [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. \u0026ldquo;Stabilizer\u0026rdquo; key is also known as \u0026ldquo;motion compensation\u0026rdquo;. To avoid the heart, breathing, and other unavoidable motion on the image interference, \u0026ldquo;Stabilizer\u0026rdquo; provides a simple correction for motion conditions, but increases the time cost [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Subsequently, static URM images were stored in the form of density, velocity, and direction maps, and dynamic MB motion trajectories were stored as videos in AVI format (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, step 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe measured URM parameters and their significance are listed in \u003cb\u003eTable S1\u003c/b\u003e. Notably, considering the difference in the size of the 16 lesions, when assessing vascular complexity and density, the ratio of the active area to the entire lesion rather than the entire lesion was measured. URM parameters were independently measured by two radiologists (J.Y. and X.L., having five years of experience in abdominal imaging). The patients were unaware of their clinical histories or radiological reports. The final input of URM parameters was the average value measured by the two radiologists.\u003c/p\u003e\n\u003ch3\u003eHistopathological Examination\u003c/h3\u003e\n\u003cp\u003eIn this study, the gold standard diagnoses of HCC, VETC, MVI, and histological grading were achieved based on surgical resection and observation under a light microscope. Open or laparoscopic resection was performed. Fresh surgical specimens, including paracancerous (\u0026lt;\u0026thinsp;1 cm from cancer tissues) and distal cancerous (approximately 5 cm from cancer tissues), were obtained from all enrolled lesions. All tissues were fixed in 10% neutral formalin, embedded in paraffin, and cut into 4 \u0026micro;m-thick sections. A senior pathologist (X.L., working in the field of liver pathological diagnosis for 12 years) reviewed all surgical specimens.\u003c/p\u003e \u003cp\u003eFor the diagnosis of MVI and histological grade, the sections were stained with HE, whereas VETC was diagnosed using CD34 staining. Patients with a visible VETC pattern on whole or part of the CD34 slides were identified as VETC-positive, and those without any VETC pattern were identified as VETC-negative. HCC lesions were classified into four histological grades based on the World Health Organization 4 tier system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: well-differentiated, moderately differentiated, poorly differentiated, and undifferentiated. This classification is mainly based on the assessment of cellular atypia and nuclear-cytoplasmic ratio.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, as appropriate. Statistical comparisons of the baseline data were performed using the Mann\u0026ndash;Whitney U test for numerical variables and the chi-square test for classification variables. The values of the URM parameters were compared using the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test. All statistical analyses were performed using SPSS version 24.0 (IBM Corp., Armonk, NY, USA). Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe incidences of MVI and VETC were 50% (8/16) and 43.7% (7/16), respectively. No undifferentiated HCC were observed in this study. 31.25% (5/16), 37.5% (6/16), and 31.25% (5/16) HCCs were well, moderately, and poorly differentiated, respectively.\u003c/p\u003e\n\u003cp\u003eOnly one patient had neither cirrhosis nor a history of liver disease. Another case involved hepatitis C cirrhosis. The remaining 14 patients had hepatitis B cirrhosis. Only two lesions were located in the left lobe, all in segment 4, and the other 14 lesions were located in the right lobe (5, 5,1, and 3 lesions located in segments 5, 6, 7, and 8, respectively).\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in baseline data (sex, alpha-fetoprotein, prothrombin time, albumin, platelet count, total bilirubin, and lesion diameter) between the MVI, VETC, and histological grading groups, except for the age of patients in the VETC group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographic and serological characteristics of patients with HCC with different histological features\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"25\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVETC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMVI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e51.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e57.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.077\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7 (43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAFP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;200 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e11 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;200 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e41.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e36.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eT-BIL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePlatelets (10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e125.7\u0026thinsp;\u0026plusmn;\u0026thinsp;51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e122.6\u0026thinsp;\u0026plusmn;\u0026thinsp;69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e128.1\u0026thinsp;\u0026plusmn;\u0026thinsp;36.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e130.6\u0026thinsp;\u0026plusmn;\u0026thinsp;32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e120.8\u0026thinsp;\u0026plusmn;\u0026thinsp;67.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e122.8\u0026thinsp;\u0026plusmn;\u0026thinsp;54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e138.0\u0026thinsp;\u0026plusmn;\u0026thinsp;60.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e113.8\u0026thinsp;\u0026plusmn;\u0026thinsp;43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePIVKA-II (mAU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e227.0\u0026thinsp;\u0026plusmn;\u0026thinsp;456.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e160.5\u0026thinsp;\u0026plusmn;\u0026thinsp;186.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e278.8\u0026thinsp;\u0026plusmn;\u0026thinsp;598.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e168.3\u0026thinsp;\u0026plusmn;\u0026thinsp;219.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e285.8\u0026thinsp;\u0026plusmn;\u0026thinsp;625.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e421.0\u0026thinsp;\u0026plusmn;\u0026thinsp;786.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e153.9\u0026thinsp;\u0026plusmn;\u0026thinsp;208.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e120.8\u0026thinsp;\u0026plusmn;\u0026thinsp;199.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTumour diameter\u003csup\u003e3\u003c/sup\u003e (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"25\"\u003e\u003csup\u003e1\u003c/sup\u003eHCC, hepatocellular carcinoma; VETC, vessel-encapsulating tumour cluster; MVI, microvascular invasion; AFP, alpha-fetoprotein; T-BIL, total bilirubin; PT, prothrombin time; PIVKA-II, prothrombin induced by vitamin K absence II.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"25\"\u003e\u003csup\u003e2\u003c/sup\u003e Since the minimum theoretical frequency (the number of female patients having MVI) is \u0026lt;\u0026thinsp;1, Fisher\u0026apos;s exact probability test is used.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"25\"\u003e\u003csup\u003e3\u003c/sup\u003e The tumour diameter indicated the largest diameter of the lesion.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe mean largest diameter of the 16 lesions was 20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7 mm (range: 11 to 30 mm). The minimum and maximum flow velocity measured across these lesions was 1.13 cm/s and 16.7 cm/s, respectively. The measured minimum and maximum vessel diameters were 60 \u0026micro;m and 1330 \u0026micro;m, respectively. The minimum and maximum blood vessel densities were 0.03 and 17.86, respectively.\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e, the mean velocity in the active area of histologically well differentiated HCCs (9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93 mm/s) was lower than that of poorly differentiated HCCs (12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56 mm/s) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025). In addition, the mean velocity of the entire lesion was higher in poorly differentiated HCCs (11.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84 mm/s) than in well differentiated (9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mm/s, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and moderately differentiated (10.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89 mm/s, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) HCCs.\u003c/p\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe relationship between URM parameters and microscopic histological features of HCC lesions\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVETC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMVI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eHistological grade\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWell\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerately\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoorly\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean velocity in the active area (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.55\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean velocity in the entire lesion (mm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003csup\u003e2\u003c/sup\u003e/\u003c/p\u003e\n \u003cp\u003e0.026\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplexity ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActive area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntire lesion area (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerfusion index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDensity ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean vessel diameters of the active area (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean vessel diameters of the entire lesion (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003e1\u003c/sup\u003e HCC, hepatocellular carcinoma; VETC, vessel-encapsulating tumour clusters; MVI, microvascular invasion; URM, ultrasound resolution microscopy.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003e2\u003c/sup\u003e This \u003cem\u003ep\u003c/em\u003e value was yielded when analysed between well differentiated HCC and poorly differentiated HCC.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003e3\u003c/sup\u003e This \u003cem\u003ep\u003c/em\u003e value was obtained when analysed between moderately differentiated HCC and poorly differentiated HCC.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003e4\u003c/sup\u003e When either of the two grades of these three histological grades were compared, the \u003cem\u003ep\u003c/em\u003e value is over 0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe complexity ratio (MVI-positive: 1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, MVI-negative: 1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) and area ratio (MVI-positive: 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, MVI-negative: 0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) were greater in MVI-positive HCCs than in MVI-negative HCCs. The perfusion index was higher in the MVI-positive group (8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88) than in the MVI-negative group (6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e\n\u003cp\u003eThe density ratio (VETC-positive: 1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19, VETC-negative: 1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) was larger in VETC-positive HCC than in VETC-negative HCC.\u003c/p\u003e\n\u003cp\u003eHowever, blood vessel diameters, regardless of the maximum, minimum, or mean values, entire lesion area, and area ratio, showed no relationship with any of the three microscopic histological features.\u003c/p\u003e\n\u003cp\u003eThe measurement of URM parameters and microscopic histological features of the two typical cases are shown in Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHCC lesions typically display fine, branching patterns of increased vascularity with greater flow velocity than metastatic lesions or haemangiomas [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the current imaging modalities lack the accuracy needed to reliably detect intratumoural microvessels. Intratumoural vessels were only visible on enhanced MRI in 43% of the AP cases [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Colour Doppler flow US imaging has been used to visualise intratumoural blood vessels with diameters\u0026thinsp;\u0026gt;\u0026thinsp;1 mm and flow velocities\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026ndash;5 cm/s [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, the limitation of conventional imaging techniques is the low resolution of microvessels. Using a stream-of-pulses model for CEUS signals, URM achieved detection limits of 40 \u0026micro;m width for the main vessel, 15 mm/s for the peak velocity, and 25 \u0026micro;m width for the secondary vessels [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this study, URM detected intratumoural vessels in all 16 HCCs, with a minimum microvessel diameter of 60 \u0026micro;m and a minimum flow velocity of 11.3 mm/s, closely aligning with the limit of detection of the abiotic model reported in the literature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These results preliminarily verify the feasibility of URM in patients with liver cancer.\u003c/p\u003e \u003cp\u003eDuring multistep hepatocarcinogenesis, immature arterial tumour vessels develop and increase markedly, while sinusoidal capillarization opens [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Blood flow in the portal vein initially decreases, then reverses, and eventually increases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These dramatic changes in haemodynamics synergistically contribute to an increased blood supply as HCC progresses. Consistent with this finding, a study assessing HCCs using the CEUS Maximum Intensity Projection technique concluded that well differentiated HCCs exhibited either normal or non-clearly visible intratumoural vasculature. By contrast, poorly differentiated HCCs exhibited tortuous, meandering, tapering, and interrupted intratumoural blood vessels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Tumour vessels accelerate as they flex and meander. Based on CEUS and contrast enhanced CT findings, the degree of arterial vascularity in HCC is closely correlated with the degree of differentiation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Therefore, abnormal vessel running and increased blood supply likely contribute to increased velocity as histological grade advances.\u003c/p\u003e \u003cp\u003eMVI and VETC represent classical passive and newly discovered active metastatic patterns of tumour cells, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The significance of VETC was first recognised due to its association with MVI [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Eighty percent of MVI-positive lesions are also positive for VETC [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, some studies have preferred to combine MVI and VETC for analysis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Nevertheless, based on our URM analysis, the intratumoural microvascular characteristics of MVI and VETC may differ.\u003c/p\u003e \u003cp\u003eThe presence of MVI is associated with the blood vessel complexity ratio. Compared to normal hepatic vessels, intratumoural neovascularization is characterised by aberrant structural dynamics and immature, tortuous, and hyperpermeable vessels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This abnormality in tumour vessels can contribute significantly to immune system evasion and metastasis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], leading to MVI. We also found that the area ratio, rather than the entire lesion area, was associated with the occurrence of MVI. This may be due to the widely recognised characteristics of high intratumoural heterogeneity in HCC. Haemorrhage, necrosis, and steatosis are commonly observed in HCC lesions. Compared to large HCCs, steatosis is more often observed in sHCCs. Hypoxia and insufficient tumour vessel development lead to HCC steatosis, whereas fatty hepatic tissue impairs the liver microcirculation and promotes ischemic injury [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A larger active area (rather than the entire area with haemorrhage, necrosis, and/or steatosis) predicts more vascular carriers that transport cancer cells and promote MVI. Our study showed that the active area of the MVI-positive group was larger than that of the MVI-negative group; however, the difference was not statistically significant, possibly because the sample size was small. The perfusion index calculated using URM images revealed differences between the MVI groups in our study. In addition to the perfusion index in our study, the perfusion parameters of triphasic CT (such as portal vein blood supply perfusion, hepatic artery perfusion index, and arterial enhancement fraction) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and contrast-enhanced MRI (such as portal venous flow and arterial fraction) are valuable for predicting MVI [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings could be explained by cancer cell invasion of blood vessels, which leads to the formation of arteriovenous fistulas, resulting in increased blood flow perfusion [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to MVI, VETC was associated with intratumoural microvessel density in the present study; however, its mechanism remains unknown. VETC-positive tumour cells express much higher levels of angiopoietin 2 (involved in anogenesis) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and carbonic anhydrase IX (a hypoxia marker that leads to angiogenesis) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] than VETC-negative tumour cells. By counting the number of CD31-stained blood vessels per square millimetre of the tumour, Huang et al. confirmed that higher intratumoural microvessel density was significantly associated with the VETC pattern [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Nevertheless, unlike Huang\u0026rsquo;s method, which relies on surgically resected specimens, our imaging methods are non-invasive and suitable for early diagnosis. A larger tumour size (\u0026gt;\u0026thinsp;5 cm) is closely related to, or may even serve as an independent predictor of, the VETC pattern [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Unlike the previous study that included HCCs of all sizes, our study showed that for small lesions, lesion size (both the largest diameter of lesion shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the entire lesion area shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) had no statistical relationship with the incidence of the VETC pattern. In other words, the present study raises a new viewpoint that for sHCC, a large lesion size may not be a risk factor for VETC positivity.\u003c/p\u003e \u003cp\u003eIn this study, the vessel diameter was not associated with any of the three microscopic histological features (MVI, VETC, and histological grade). In general, the maximum flow velocity occurs at the centre of the blood flow, whereas the minimum flow velocity occurs at the inner wall of the blood vessel [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Moreover, with an increase in vessel diameter, resistance decreases, and blood flow increases [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, in theory, the wider the blood vessels, the less likely the circulating tumour cells are to be destroyed by shear stress and the immune system, leading to haematogenous metastasis. Conversely, a smaller vessel diameter increases the chance of collisions between cancer cells and host cells, such as leukocytes, erythrocytes, and endothelial cells, circulating in the blood, which may reduce the flow rate and further increase the metastatic potential of cancer cells [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These negative results may be attributed to the small sample size of the present study. However, some of the underlying factors may have remained unknown.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, because the image quality and accuracy of URM analysis are easily affected by the respiratory motion and penetration depth, its widespread application in clinical practice is challenging. Second, the sample size was small, and the statistical analysis was simple. In addition, no diagnostic model has been established yet. Nevertheless, this study is valuable as it represents an early attempt to analyse a real-world human population with HCC. These positive results indicate that the clinical application of URM in liver cancer is promising. As a pioneering study, it serves as a reference for future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe positive microvascular findings of our URM study indicate that the high density and complexity of microvessels, high flow velocity, and large vessel area of sHCC lesions may correlate with their microscopic histological features, including high histological grade, positive VETC, and MVI pattern. In the future, we plan to increase the sample size and conduct a multicentre study to further explore and validate the role of URM in HCC diagnosis. The idea of using URM technique as a noninvasive tool for the preoperative diagnosis of the microscopic histological features might be promising.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMB, microbubble; MVI, microvascular invasion; VETC, vessels encapsulating tumour clusters; US, ultrasound; URM, ultrasound resolution microscopy; HCC, hepatocellular carcinoma; sHCC, small HCC; CEUS, contrast-enhanced ultrasound; CT, contrast-enhanced computed tomography; MRI, magnetic resonance imaging; AP, arterial phase; CD, cluster of differentiation; HE, hematoxylin\u0026ndash;eosin.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLitao Ruan and Feiqian Wang contributed to the design of the research. Xi Liu and Wenbin Zhang implemented the research. Xingqi Lu and Jingtong Yu curated the data and analyzed the results. Xiaojing Li and Mingxi Wan wrote the original draft of the manuscript. Kazushi Numata reviewed and edited the manuscript. Dong Zhang supervised the project. Feiqian Wang and Guanjun Zhang contributed to funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun B, Ji WD, Wang WC, Chen L, Ma JY, Tang EJ, Lin MB, Zhang XF: \u003cstrong\u003eCirculating tumor cells participate in the formation of microvascular invasion and impact on clinical outcomes in hepatocellular carcinoma\u003c/strong\u003e. \u003cem\u003eFrontiers in genetics \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1265866.\u003c/li\u003e\n\u003cli\u003eRenne SL, Woo HY, Allegra S, Rudini N, Yano H, Donadon M, Vigan\u0026ograve; L, Akiba J, Lee HS, Rhee H\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eVessels Encapsulating Tumor Clusters (VETC) Is a Powerful Predictor of Aggressive Hepatocellular Carcinoma\u003c/strong\u003e. \u003cem\u003eHepatology (Baltimore, Md) \u003c/em\u003e2020, \u003cstrong\u003e71\u003c/strong\u003e(1):183-195.\u003c/li\u003e\n\u003cli\u003eSalehi O, Vega EA, Kutlu OC, Lunsford K, Freeman R, Ladin K, Alarcon SV, Kazakova V, Conrad C: \u003cstrong\u003ePoorly differentiated hepatocellular carcinoma: resection is equivalent to transplantation in patients with low liver fibrosis\u003c/strong\u003e. \u003cem\u003eHPB : the official journal of the International Hepato Pancreato Biliary Association \u003c/em\u003e2022, \u003cstrong\u003e24\u003c/strong\u003e(7):1100-1109.\u003c/li\u003e\n\u003cli\u003eWang F, Numata K, Funaoka A, Kumamoto T, Takeda K, Chuma M, Nozaki A, Ruan L, Maeda S: \u003cstrong\u003eConstruction of a nomogram combining CEUS and MRI imaging for preoperative diagnosis of microvascular invasion in hepatocellular carcinoma\u003c/strong\u003e. \u003cem\u003eEuropean journal of radiology open \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e:100587.\u003c/li\u003e\n\u003cli\u003eRuan L, Yu J, Lu X, Numata K, Zhang D, Liu X, Li X, Zhang M, Wang F: \u003cstrong\u003eA Nomogram Based on Features of Ultrasonography and Contrast-Enhanced CT to Predict Vessels Encapsulating Tumor Clusters Pattern of Hepatocellular Carcinoma\u003c/strong\u003e. \u003cem\u003eUltrasound in medicine \u0026amp; 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CEUS and URM were performed one week before resection. All \u0026ldquo;ratio\u0026rdquo; refers to comparisons between the active area (where CEUS microbubble show visible motion track by URM) and the entire lesion. Blood vessel complexity (ratio), blood vessel density (ratio), area (ratio), flow velocity, blood vessel diameter, and perfusion index (\u0026ldquo;flow velocity\u0026rdquo; \u0026times; \u0026ldquo;vessel ratio\u0026rdquo;) were analysed using URM. The relationships between URM parameters and microscopic histological features (MVI, VETC, and histological grade) were analysed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were 5 (31.3%), 8 (50%), and 7 (43.7%) cases of poorly differentiated, MVI-positive, and VETC-positive HCC, respectively. The mean velocity of the entire lesion was higher in the poorly differentiated group than that in the moderately differentiated group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). The complexity ratio (MVI-positive: 1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03, MVI-negative: 1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), area ratio (MVI-positive: 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, MVI-negative: 0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), and perfusion index (MVI-positive: 8.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88, MVI-negative: 6.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) were greater in MVI-positive HCCs. The density ratio (VETC-positive: 1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19, VETC-negative: 1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) was larger in VETC-positive HCCs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHigher blood flow velocity and area of HCC lesions, and higher blood vessel complexity and density may be related to microscopic histological features. This relationship might provide a strategy of using URM for preoperative non-invasive diagnostic VETC, MVI, and histological grade in the future.\u003c/p\u003e","manuscriptTitle":"Relationship between contrast-enhanced ultrasound combined with ultrasound resolution microscopy imaging and histological features of hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-19 13:38:18","doi":"10.21203/rs.3.rs-5513597/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-06T01:59:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-25T13:43:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-25T13:40:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2024-11-24T11:02:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8e50d19c-b4ee-4967-b9ae-6ddc2b3a6fbb","owner":[],"postedDate":"December 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:00:56+00:00","versionOfRecord":{"articleIdentity":"rs-5513597","link":"https://doi.org/10.1007/s00261-025-04825-y","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2025-02-10 15:57:24","publishedOnDateReadable":"February 10th, 2025"},"versionCreatedAt":"2024-12-19 13:38:18","video":"","vorDoi":"10.1007/s00261-025-04825-y","vorDoiUrl":"https://doi.org/10.1007/s00261-025-04825-y","workflowStages":[]},"version":"v1","identity":"rs-5513597","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5513597","identity":"rs-5513597","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}