pH and ROS Dual-Sensitive Nanocarriers for the Targeted Co-Delivery and On-Demand Sequential Release of Tofacitinib and Glucosamine for Synergistic Rheumatoid Arthritis Treatment.

Rheumatoid arthritis (RA) progression involves multiple cell types, and sequential drug action on target cells is necessary for RA treatment. Nanocarriers are widely used for RA treatment; however, the targeted delivery and on-demand release of multiple drugs remains challenging. Therefore, in this study, a dual-sensitive polymer is developed using chondroitin sulfate (CS) for the co-delivery of the cartilage repair agent, glucosamine (GlcN), and anti-inflammatory drug, tofacitinib (Tof). In the joint cavity, acidic pH facilitates the cleavage of GlcN from CS polymer to repair the cartilage damage. Subsequently, macrophage uptake via CS-CD44 binding and intracellular reactive oxygen species (ROS) mediate conversion of (methylsulfanyl)propylamine to a hydrophilic segment jointly triggered rapid Tof/GlcN release via micelle disassembly. The combined effects of Tof, GlcN, and ROS depletion promote the M1-to-M2 polarization shift to attenuate inflammation. The synergistic effects of these agents against RA are confirmed in vitro and in vivo. Overall, the dual pH/ROS-sensitive CS nanoplatform simultaneously delivers GlcN and Tof, providing a multifunctional approach for RA treatment with synergistic drug effects.

Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. PURPOSE: To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. STUDY TYPE: Retrospective. POPULATION: A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. FIELD STRENGTH/SEQUENCE: 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). ASSESSMENT: Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. RESULTS: Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). CONCLUSION: MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Dual-Energy CT Deep Learning Radiomics to Predict Macrotrabecular-Massive Hepatocellular Carcinoma.

Background It is unknown whether the additional information provided by multiparametric dual-energy CT (DECT) could improve the noninvasive diagnosis of the aggressive macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC). Purpose To evaluate the diagnostic performance of dual-phase contrast-enhanced multiparametric DECT for predicting MTM HCC. Materials and Methods Patients with histopathologic examination-confirmed HCC who underwent contrast-enhanced DECT between June 2019 and June 2022 were retrospectively recruited from three independent centers (center 1, training and internal test data set; centers 2 and 3, external test data set). Radiologic features were visually analyzed and combined with clinical information to establish a clinical-radiologic model. Deep learning (DL) radiomics models were based on DL features and handcrafted features extracted from virtual monoenergetic images and material composition images on dual phase using binary least absolute shrinkage and selection operators. A DL radiomics nomogram was developed using multivariable logistic regression analysis. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC), and the log-rank test was used to analyze recurrence-free survival. Results A total of 262 patients were included (mean age, 54 years ± 12 [SD]; 225 men [86%]; training data set, n = 146 [56%]; internal test data set, n = 35 [13%]; external test data set, n = 81 [31%]). The DL radiomics nomogram better predicted MTM than the clinical-radiologic model (AUC = 0.91 vs 0.77, respectively, for the training set [P < .001], 0.87 vs 0.72 for the internal test data set [P = .04], and 0.89 vs 0.79 for the external test data set [P = .02]), with similar sensitivity (80% vs 87%, respectively; P = .63) and higher specificity (90% vs 63%; P < .001) in the external test data set. The predicted positive MTM groups based on the DL radiomics nomogram had shorter recurrence-free survival than predicted negative MTM groups in all three data sets (training data set, P = .04; internal test data set, P = .01; and external test data set, P = .03). Conclusion A DL radiomics nomogram derived from multiparametric DECT accurately predicted the MTM subtype in patients with HCC. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

The value of quantitative MR elastography-based stiffness for assessing the microvascular invasion grade in hepatocellular carcinoma.

OBJECTIVES: To evaluate the potential diagnostic value of MR elastography (MRE)-based stiffness to noninvasively predict the microvascular invasion (MVI) grade in hepatocellular carcinoma (HCC). METHODS: One hundred eighty-five patients with histopathology-proven HCC who underwent MRI and MRE examinations before hepatectomy were retrospectively enrolled. According to the three-tiered MVI grading system, the MVI was divided into negative-MVI (n = 89) and positive-MVI (n = 96) groups, and the latter group was categorized into mild-MVI (n = 49) and severe-MVI (n = 47) subgroups. Logistic regression and area under the receiver operating characteristic curve (AUC) analyses were used to determine the predictors associated with MVI grade and analyze their performances, respectively. RESULTS: Among the 185 patients, tumor size ≥ 50 mm (p = 0.031), tumor stiffness (TS)/liver stiffness (LS) > 1.47 (p = 0.001), TS > 4.33 kPa (p < 0.001), and nonsmooth tumor margin (p = 0.006) were significant independent predictors for positive-MVI. Further analyzing the subgroups, tumor size ≥ 50 mm (p < 0.001), TS > 5.35 kPa (p = 0.001), and AFP level > 400 ng/mL (p = 0.044) were independently associated with severe-MVI. The models incorporating MRE and clinical-radiological features together performed better for evaluating positive-MVI (AUC: 0.846) and severe-MVI (AUC: 0.802) than the models using clinical-radiological predictors alone (AUC: positive-/severe-MVI, 0.737/0.743). Analysis of recurrence-free survival and overall survival showed the predicted positive-MVI/severe-MVI groups based on combined models had significantly poorer prognoses than predicted negative-MVI/mild-MVI groups, respectively (all p < 0.05). CONCLUSIONS: MRE-based stiffness was an independent predictor for both the positive-MVI and severe-MVI. The combination of MRE and clinical-radiological models might be a useful tool for evaluating HCC patients' prognoses underwent hepatectomy by preoperatively predicting the MVI grade. KEY POINTS: • The severe-microvascular invasion (MVI) grade had the highest tumor stiffness (TS), followed by mild-MVI and non-MVI, and there were significances among the three different MVI grades. • MR elastography (MRE)-based stiffness value was an independent predictor of positive-MVI and severe-MVI in hepatocellular carcinoma (HCC) preoperatively. • When combined with clinical-radiological models, MRE could significantly improve the predictive performance for MVI grade. Patients with predicted positive-MVI/severe-MVI based on the combined models had worse recurrence-free survival and overall survival than those with negative-MVI/mild-MVI, respectively.

Comparison of the diagnostic performance of 2D and 3D MR elastography in staging liver fibrosis.

OBJECTIVES: To compare the diagnostic performance and image quality of state-of-the-art 2D MR elastography (MRE) and 3D MRE in the basic application of liver fibrosis staging. METHODS: This retrospective study assessed data from 293 patients who underwent 2D and 3D MRE examinations. MRE image quality was assessed with a qualitative 2-point grading system by evaluating artifacts. Two experienced analysts independently measured mean liver stiffness values. The interobserver agreement of liver stiffness measurement was assessed by the intraclass correlation coefficient (ICC). The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance of 2D and 3D MRE and blood-based markers for fibrosis staging using the pathology-proven liver fibrosis stage as the gold standard. RESULTS: The image quality provided by 3D MRE was graded as significantly higher than that obtained with the 2D MRE method (p < 0.01). Interobserver agreement in liver stiffness measurements was higher for 3D MRE (ICC: 3D 0.979 vs 2D 0.955). The AUC values for discriminating ≥ F1, ≥ F2, ≥ F3, and F4 fibrosis for 3D MRE (0.89, 0.92, 0.95, and 0.93) were similar to those for 2D MRE (0.89, 0.91, 0.94, and 0.92). Both the 2D and 3D MRE methods provided superior accuracy to the blood-based biomarkers, including APRI, FIB-4, and Forns index, especially for ≥ F2, ≥ F3, and F4 fibrosis stages (all p < 0.01). CONCLUSIONS: While 3D MRE offers certain advantages and opportunities for new applications of MRE, current widely deployed 2D MRE technology has comparable performance in the basic application of detecting and staging liver fibrosis. KEY POINTS: • 2D MRE and 3D MRE have comparable diagnostic performance in detecting and staging liver fibrosis. • 3D MRE has superior image quality and interobserver agreement compared to 2D MRE.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer.

BACKGROUND: Increasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness. PURPOSE: To investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC). STUDY TYPE: Retrospective. POPULATION: Twenty-five histopathology-proven CC patients and 7 healthy participants. FIELD STRENGTH/SEQUENCE: 3.0T, magnetic resonance imaging (MRI) (LAVA-flex) and MRE with a three-dimensional spin-echo echo-planar imaging. ASSESSMENT: The regions of interest (ROIs) were manually drawn by two observers in tumors to measure mean TS, storage modulus (G'), loss modulus (G″) and damping ratio (DR) values. Surgical specimens were evaluated for tumor grades and subtypes. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) was expressed in terms of inter-observer agreements. t-test or Mann-Whitney nonparametric test was used to compare the complex modulus and apparent diffusion coefficient (ADC) values between different tumor groups. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic performance. RESULTS: The TS of endocervical adenocarcinoma (ECA) group was significantly higher than that in squamous cell carcinoma (SCC) group (5.27 kPa vs. 3.44 kPa, P = 0.042). The TS also showed significant difference between poorly and well/moderately differentiated CC (5.21 kPa vs. 3.47 kPa, P = 0.038), CC patients and healthy participants (4.18 kPa vs. 1.99 kPa, P < 0.001). The cutoff value of TS to discriminate ECA from SCC was 4.10 kPa (AUC: 0.80), while it was 4.42 kPa to discriminate poorly from well/moderately differentiated CC (AUC: 0.83), and 2.25 kPa to distinguish normal cervix from CC (AUC: 0.88), respectively. There were no significant difference in G″, DR and ADC values between any subgroups except for comparison of healthy participants and CC patients (P = 0.001, P = 0.004, P < 0.001, respectively). DATA CONCLUSION: 3D MRE-assessed TS shows promise as a potential biomarker to preoperatively assess tumor grade and subtype of CC.

A component-optimized chemo-dynamic nanoagent for enhanced tumour cell-selective chemo-dynamic therapy with minimal side effects in a glioma mouse model.

Although CuO-deposited bovine serum albumin (CuO-BSA) and glucose oxidase (GOx) were combined to achieve H2O2 self-supplied chemo-dynamic therapy (CDT) and glucose consumption-based starvation therapy, the uses of copper and GOx have not been optimized to enhance tumour-selective reactive oxygen species (ROS) generation and minimize toxicity to normal cells as well. Here, chemo-dynamic nanoparticles (CBGP NPs) were prepared through a facile biomineralization process and subsequent coatings with GOx and the cationic polymer PEG2k-PEI1.8k. Through optimizing the use of copper, GOx, and PEG2k-PEI1.8k, the CBGP NPs showed high cellular uptake efficiency, enhanced tumour-selective ROS generation, and minimal side effects toward normal cells. The CBGP NP-mediated glucose consumption, GSH-depletion, and ˙OH generation synergistically induced tumour cell apoptosis both in vitro and in vivo. It is believed that the optimized CBGP NPs can be a promising nanoplatform for effective tumour therapy with minimal side effects.

Diagnostic accuracy of 3D magnetic resonance elastography for assessing histologic grade of hepatocellular carcinoma: comparison of three methods for positioning region of interest.

PURPOSE: To assess the influence of region of interest (ROI) placement on the predictive value of 3D MRE in differentiating the histologic grade of HCC. METHODS: 85 patients with pathologically confirmed HCCs were analyzed using 3D MRE imaging, two radiologists measured the tumor stiffness with three different ROI positioning methods. Intraclass correlation coefficient (ICC) was expressed in terms of inter- and intra-observer agreements. Kruskal-Wallis rank test or one-way ANOVA was used to compare the difference in MRE stiffness across the three-ROI positioning methods. Receiver operating characteristic curve analysis (ROC) was performed, and the area under curve (AUC) was measured to evaluate the diagnostic performance. RESULTS: There were 64 (75%) well-or-moderately differentiated HCCs and 21(25%) poorly differentiated HCCs included finally. Almost excellent inter- and intra-observer agreements (all ICC > 0.82) were observed for all three-ROI methods, the volumetric method has the highest values (inter-observer ICC 0.967, intra-observer ICC 0.919, 0.926, respectively). The mean stiffnesses of poorly differentiated HCC obtained by two readers were significantly higher than well-or-moderately differentiated HCC with volumetric method (7.07 ± 1.57 Kpa, 5.00 ± 1.49 Kpa, and 6.85 ± 1.49 Kpa, 4.94 ± 1.48 Kpa, respectively) and three-ROI method (6.14 ± 1.71 Kpa, 4.91 ± 1.56 Kpa and 5.94 ± 1.61 Kpa, 4.84 ± 1.54 Kpa, respectively) but not on single-ROI method (p > 0.005), for the diagnostic performance, the highest area under the curve (AUC) with a value of 0.837, 0.812 by using the volumetric method, followed by the three-ROI method (0.713, 0.754) and single-ROI method. CONCLUSION: Different ROI positioning methods significantly affect HCC tumor stiffness measurements. The whole tumor volumetric analysis is superior to ROI-based methods for predicting the grade of HCC.

Increasing the sensitivity of LI-RADS v2018 for diagnosis of small (10-19 mm) HCC on extracellular contrast-enhanced MRI.

PURPOSE: To evaluate whether the LI-RADS v2018 LR-5 criteria can be modified to increase sensitivity without reducing specificity for diagnosing small (10-19 mm) HCC. METHODS: 167 consecutive high-risk patients with 174 small observations reported clinically on extracellular contrast-enhanced MRI from 2014 to 2018 were retrospectively studied. The best available reference standard was applied for each observation. Blinded to the reference standard, two radiologists scored LI-RADS imaging features retrospectively and assigned each observation a LI-RADS category using LI-RADS v2018 and each of four modified LI-RADS versions (mLI-RADS I to IV) with successively more expansive LR-5 criteria. Per-observation sensitivity and specificity of LR-5 for small HCC using each version were assessed. Each modified version was compared to v2018 (McNemar test). RESULTS: The 174 observations included 135 HCC, 8 non-HCC malignancies, and 31 benign entities. Using LI-RADS v2018, LR-5 provided 70% (both readers) sensitivity and 95% (both readers) specificity for small HCC. Expanding the LR-5 criteria to include nonrim APHE plus at least one additional major feature (mLI-RADS I) or no APHE plus at least two additional major features (mLI-RADS II) significantly increased sensitivity (reader 1/reader 2: 75%/75% vs. 70%, p = 0.016/0.031; 78%/79% vs. 70%, p = 0.001/0.001) without significantly reducing specificity (reader 1/reader 2: 90%/92% vs. 95%, p = 0.500/1.000 for both). mLI-RADS III and IV further increased sensitivity (reader 1/reader 2: 80%/81% vs. 70%, p < 0.001/< 0.001; 94%/92% vs. 70, p < 0.001/< 0.001) but with trend-level (reader 1/reader 2: 85%/80% vs. 95%, p = 0.125/0.063) or significant (reader 1/reader 2: 64%/62% vs. 95%, p < 0.001/< 0.001) specificity reductions. CONCLUSIONS: Expanding the v2018 LR-5 criteria to include nonrim APHE plus at least one additional major feature or no APHE plus at least two additional major features significantly increases sensitivity without significantly reducing specificity for small HCC. Confirmation is warranted in multi-center prospective studies.

Tumor stiffness measured by 3D magnetic resonance elastography can help predict the aggressiveness of endometrial carcinoma: preliminary findings.

BACKGROUND: Preoperative evaluation of aggressiveness, including tumor histological subtype, grade of differentiation, Federation International of Gynecology and Obstetrics (FIGO) stage, and depth of myometrial invasion, is significant for treatment planning and prognosis in endometrial carcinoma (EC). The purpose of this study was to evaluate whether three-dimensional (3D) magnetic resonance elastography (MRE) can help predict the aggressiveness of EC. METHODS: From August 2015 to January 2019, 82 consecutive patients with suspected uterine tumors underwent pelvic MRI and MRE scans, and 15 patients with confirmed EC after surgical resection were enrolled. According to pathological results (tumor grade, histological subtype, FIGO stage, and myometrial invasiveness), the patients were divided into two subgroups. The independent-samples t-test or Mann-Whitney U test was used to compare the stiffness between different groups. The diagnostic performance was determined with receiver operating characteristic (ROC) curve analysis. RESULTS: The stiffness of EC with ≥ 50 % (n = 6) myometrial invasion was significantly higher than that with < 50 % (n = 9) myometrial invasion (3.68 ± 0.59 kPa vs. 2.61 ± 0.72 kPa, p = 0.009). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 77.8 % specificity for differentiating ≥ 50 % myometrial invasion from < 50 % myometrial invasion of EC. The stiffness of poorly differentiated EC (n = 8) was significantly higher than that of well/moderately differentiated EC (n = 7) (3.47 ± 0.64 kPa vs. 2.55 ± 0.82 kPa, p = 0.028). Using a stiffness of 3.04 kPa as a cutoff value resulted in 75 % sensitivity and 71.4 % specificity for differentiating poorly differentiated from well/moderately differentiated EC. The stiffness of FIGO stage II/III EC was significantly higher than that of FIGO stage I EC (3.69 ± 0.65 kPa vs. 2.72 ± 0.76 kPa, p = 0.030). Using a stiffness of 3.04 kPa as a cutoff value resulted in 100 % sensitivity and 70 % specificity for differentiating FIGO stage I EC from FIGO stage II/III EC. The tumor stiffness value in type II (n = 3) EC was higher than that in type I (n = 12) EC (3.67 ± 0.59 kPa vs. 2.88 ± 0.85 kPa), but the difference was not significant (p = 0.136). CONCLUSIONS: Tumor stiffness measured by 3D MRE may be potentially useful for predicting tumor grade, FIGO stage and myometrial invasion of EC and can aid in the preoperative risk stratification of EC.

Evaluation of MR elastography for prediction of lymph node metastasis in prostate cancer.

PURPOSE: To assess the relationship between MRE stiffness of prostate cancer (PCa) and the extent of lymph node metastasis (LNM) in patients with PCa undergoing radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). MATERIALS: The local institutional review board approved this retrospective study. We retrospectively analyzed 49 patients, who had undergone MRE, mpMRI and pelvic MRI on a 3.0 T MRI scanner, with histopathological confirmed PCa after RP (from June 2015 to December 2019). For each patient, preoperative clinical data and characteristics of MRE, mpMRI and pelvic MRI were recorded. Independent-samples t test, univariate and multivariate logistic regression analyses were performed. And receiver operating characteristic (ROC) analysis were performed to compare the diagnostic performances of multivariate models with the Briganti 2019 nomogram. RESULTS: PCa MRE stiffness and maximum diameter were independent predictors of LNM. When PCa MRE stiffness at 60 Hz (odds ratio [OR] = 20.223, P = 0.013) and maximum diameter (OR = 4.575, P = 0.046) were combined, the sensitivity and specificity were 100% and 91.9% to predict LNM. When PCa MRE stiffness at 90 Hz (OR = 7.920, P = 0.013) and maximum diameter (OR = 2.810, P = 0.045) were combined, the sensitivity and specificity were 100% and 86.5% to predict LNM. The areas under curves (AUCs) of the combinations were higher than the AUC of the Briganti 2019 nomogram (0.982 vs. 0.904, P = 0.040 [60 Hz]; 0.975 vs. 0.904, P = 0.060 [90 Hz], respectively). CONCLUSIONS: MRE-based assessment of PCa stiffness may be useful for predicting LNM of PCa preoperatively and noninvasively.

The Role of Preoperative Dynamic Contrast-enhanced 3.0-T MR Imaging in Predicting Early Recurrence in Patients With Early-Stage Hepatocellular Carcinomas After Curative Resection.

Objectives: Liver resection is potentially curative for early-stage hepatocellular carcinoma (eHCC) in patients with well-preserved liver function. The prognosis of these patients after resection is still unsatisfactory because of frequent early recurrence (ER). Therefore, we investigated the role of preoperative dynamic contrast-enhanced 3.0-T MR imaging in predicting ER of eHCC after curative resection. Methods From May 2014 to October 2017, we retrospectively analyzed 82 patients with eHCC who underwent dynamic MR imaging and subsequently underwent curative resection. Liver Imaging Reporting and Data System (LI-RADS) v2018 major and ancillary imaging features, as well as two non-LI-RADS MR imaging features (irregular tumor margin and tumor number), were evaluated. A multivariate Cox regression analysis was used to identify independent predictors, and two models (preoperative and postoperative prediction models) were developed. Results ER was observed in 25 patients (25/82, 30.5%). In the univariate analyses, preoperative alpha-fetoprotein (AFP) level >200 ng/ml, three MR imaging features (multifocal tumors, corona enhancement, and irregular tumor margin), and microvascular invasion (MVI) were associated with ER. In the multivariate analysis, corona enhancement (hazard ratio [HR]: 2.970; p = 0.013) and irregular tumor margin (HR: 2.377; p = 0.048) were independent predictors in the preoperative prediction model, and preoperative AFP level >200 ng/ml (HR: 2.493; p = 0.044) plus corona enhancement (HR: 3.046; p = 0.014) were independent predictors in the postoperative prediction model (microvascular invasion [MVI] was not; p = 0.061). When combined with both predictors, the specificity for ER in the preoperative prediction model was 98.2% (56/57), which was comparable to that of the postoperative prediction model [96.7% (55/57)]. Conclusions Our results demonstrated that preoperative MR imaging features (corona enhancement and irregular tumor margin) have the potential to preoperatively identify high-risk ER patients with eHCC, with a specificity >90%.