Evaluation of Glypican-3 Expression in Hepatocellular Carcinoma by Using IDEAL IQ Magnetic Resonance Imaging

Assessment and integration of multiparametric MRI for liver fibrosis staging in rat non-alcoholic steatohepatitis: Evaluation of diagnostic efficiency and model interpretation

OBJECTIVES

Multiparametric magnetic resonance imaging (mpMRI) techniques, including intravoxel incoherence motion (IVIM), iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantification sequence (IDEAL IQ), T2* mapping and T2 mapping, were employed to develop and validate a predictive model for non-alcoholic steatohepatitis (NASH) diagnosis and liver fibrosis (LF) staging in rats. The combined model was interpreted using SHapley Additive exPlanations (SHAP) values for model interpretation.

MATERIALS AND METHODS

160 healthy Sprague-Dawley (SD) rats were divided into control (n = 24) and experimental (n = 136) groups, and the 12-week and 16-week groups were injected intraperitoneally with carbon tetrachloride (CCl4) for 4 weeks, one month before the final feeding period. All rats were subjected to pathological examination to determine LF stage. Upon the study's completion, 147 SD rats were assessed for liver fibrosis.

RESULTS

84 SD rats were diagnosed with NASH and 31, 10, and 43 rats were histologically diagnosed with no fibrosis (F0), early LF (F1-F2), and advanced LF (F3-F4). For diagnosis of NASH and staging of liver fibrosis associated with NASH, a combined mpMRI prediction model has a higher area under the receiver operating characteristic(ROC) curve (AUC) than uniparameters, especially in advanced stages of fibrosis, with an AUC of 0.929 for the combined model. In SHAP, the fat fraction(FF) value contributes most to the model for diagnosing NASH and advanced liver fibrosis, while the T2 value contributes most for diagnosing liver fibrosis and the apparent diffusion coefficient (ADC) value contributes most for diagnosing liver cirrhosis.

CONCLUSIONS

The mpMRI could be used to evaluate the severity of liver fibrosis in the context of NASH. Combined with SHAP value analysis, this approach can help to understand the contribution of each mpMRI feature to the predictive model.

Beyond Cancer Cells: How the Tumor Microenvironment Drives Cancer Progression

Liver cancer represents a substantial global health challenge, contributing significantly to worldwide morbidity and mortality. It has long been understood that tumors are not composed solely of cancerous cells, but also include a variety of normal cells within their structure. These tumor-associated normal cells encompass vascular endothelial cells, fibroblasts, and various inflammatory cells, including neutrophils, monocytes, macrophages, mast cells, eosinophils, and lymphocytes. Additionally, tumor cells engage in complex interactions with stromal cells and elements of the extracellular matrix (ECM). Initially, the components of what is now known as the tumor microenvironment (TME) were thought to be passive bystanders in the processes of tumor proliferation and local invasion. However, recent research has significantly advanced our understanding of the TME's active role in tumor growth and metastasis. Tumor progression is now known to be driven by an intricate imbalance of positive and negative regulatory signals, primarily influenced by specific growth factors produced by both inflammatory and neoplastic cells. This review article explores the latest developments and future directions in understanding how the TME modulates liver cancer, with the aim of informing the design of novel therapies that target critical components of the TME.

Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma

Objectives

The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators.

Methods

As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility.

Results

Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75th percentile ADC value < 1.48 ×103 mm2/s and R2* value ≥ 38.6 sec-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness.

Conclusions

The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.

The expression of Ki-67 and Glypican -3 in hepatocellular carcinoma was evaluated by comparing DWI and 18F-FDG PET/CT

Objective

The value of DWI and 18F-FDG PET/CT in evaluating the expression of Ki-67 and GPC-3 in HCC was compared.

Materials and methods

Ninety-four patients with primary HCC confirmed by pathology were retrospectively divided into high- and low-Ki-67-expression groups and positive- and negative- GPC-3 groups. The ADC and SUVmax values of the lesions in both groups were measured. ROC curves were used to evaluate the identification efficiency of parameters with significant differences for each group of lesions, and AUCwas calculated. The combined ADC and SUVmax values were analyzed by binary logistic regression. The Delong test was used to compare the AUC values of the combined and single parameters. Pearson (in line with normal distribution) or Spearman (in line with abnormal distribution) correlation analysis was used to analyze the correlation.

Results

The ADC value of the high-Ki-67-expression group was lower than that of the low-Ki-67-expression group (P<0.05), and the SUVmax value of the high-expression group was higher than that of the low-expression group (P<0.05). The ADC value of the positive-GPC-3 group was lower than that of the negative group (P<0.0.tive group (P<0.05). The combined ADC and SUVmax values in the GPC-3 group were better than those of a single parameter (P<0.05). There was a strong negative correlation between the SUVmax value and ADC value in the Ki-67 group (R=-0.578, P<0.001) and a weak negative correlation between the SUVmax value and ADC value in the GPC-3 group (R=-0.279, P=0.006). The SUVmax value was strongly positively correlated with the Ki-67 expression index (R=0.733, P<0.001), while the ADC value was strongly negatively correlated with the Ki-67 expression index (R=-0.687, P<0.001).

Conclusion

DWI and 18F-FDG PET/CT can be used to evaluate the expression of Ki-67 and GPC-3 in HCC, and there is a certain correlation between the ADC value and SUVmax. Combined DWI and 18F-FDG PET/CT is superior to a single technique in evaluating the expression of GPC-3 in HCC patients. However, the combined model did not benefit the Ki-67 group.

Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging

Introduction

The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature.

Methods

We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC).

Results

In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively.

Conclusion

HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

A nomogram based on LI-RADS features, clinical indicators and quantitative contrast-enhanced MRI parameters for predicting glypican-3 expression in hepatocellular carcinoma

Purpose

Noninvasively assessing the tumor biology and microenvironment before treatment is greatly important, and glypican-3 (GPC-3) is a new-generation immunotherapy target for hepatocellular carcinoma (HCC). This study investigated the application value of a nomogram based on LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators in the noninvasive preoperative prediction of GPC-3 expression in HCC.

Methods and materials

We retrospectively reviewed 127 patients with pathologically confirmed solitary HCC who underwent Gd-EOB-DTPA MRI examinations and related laboratory tests. Quantitative contrast-enhanced MRI parameters and clinical indicators were collected by an abdominal radiologist, and LI-RADS features were independently assessed and recorded by three trained intermediate- and senior-level radiologists. The pathological and immunohistochemical results of HCC were determined by two senior pathologists. All patients were divided into a training cohort (88 cases) and validation cohort (39 cases). Univariate analysis and multivariate logistic regression were performed to identify independent predictors of GPC-3 expression in HCC, and a nomogram model was established in the training cohort. The performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve in the training cohort and validation cohort, respectively.

Results

Blood products in mass, nodule-in-nodule architecture, mosaic architecture, contrast enhancement ratio (CER), transition phase lesion-liver parenchyma signal ratio (TP-LNR), and serum ferritin (Fer) were independent predictors of GPC-3 expression, with odds ratios (ORs) of 5.437, 10.682, 5.477, 11.788, 0.028, and 1.005, respectively. Nomogram based on LI-RADS features (blood products in mass, nodule-in-nodule architecture and mosaic architecture), quantitative contrast-enhanced MRI parameters (CER and TP-LNR) and clinical indicators (Fer) for predicting GPC-3 expression in HCC was established successfully. The nomogram showed good discrimination (AUC of 0.925 in the training cohort and 0.908 in the validation cohort) and favorable calibration. The diagnostic sensitivity and specificity were 76.9% and 92.3% in the training cohort, 76.8% and 93.8% in the validation cohort respectively.

Conclusion

The nomogram constructed from LI-RADS features, quantitative contrast-enhanced MRI parameters and clinical indicators has high application value, can accurately predict GPC-3 expression in HCC and may help noninvasively identify potential patients for GPC-3 immunotherapy.

Preoperative prediction of glypican-3 positive expression in solitary hepatocellular carcinoma on gadoxetate-disodium enhanced magnetic resonance imaging

Purpose

As a coreceptor in Wnt and HGF signaling, glypican-3 (GPC-3) promotes the progression of tumor and is associated with a poor prognosis in hepatocellular carcinoma (HCC). GPC-3 has evolved as a target molecule in various immunotherapies, including chimeric antigen receptor T cell. However, its evaluation still relies on invasive histopathologic examination. Therefore, we aimed to develop an easy-to-use and noninvasive risk score integrating preoperative gadoxetic acid–enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators to predict positive GPC-3 expression in HCC.

Methods and materials

Consecutive patients with surgically-confirmed solitary HCC who underwent preoperative EOB-MRI between January 2016 and November 2021 were retrospectively included. EOB-MRI features were independently evaluated by two masked abdominal radiologists and the expression of GPC-3 was determined by two liver pathologists. On the training dataset, a predictive scoring system for GPC-3 was developed against pathology via logistical regression analysis. Model performances were characterized by computing areas under the receiver operating characteristic curve (AUCs).

Results

A total of 278 patients (training set, n=156; internal validation set, n=39; external validation set, n=83) with solitary HCC (208 [75%] with positive GPC-3 expression) were included. Serum alpha-fetoprotein >10 ng/ml (AFP, odds ratio [OR]=2.3, four points) and five EOB-MR imaging features, including tumor size >3.0cm (OR=0.5, -3 points), nonperipheral “washout” (OR=3.0, five points), infiltrative appearance (OR=9.3, 10 points), marked diffusion restriction (OR=3.3, five points), and iron sparing in solid mass (OR=0.2, -7 points) were significantly associated with positive GPC-3 expression. The optimal threshold of scoring system for predicting GPC-3 positive expression was 5.5 points, with AUC 0.726 and 0.681 on the internal and external validation sets, respectively.

Conclusion

Based on serum AFP and five EOB-MRI features, we developed an easy-to-use and noninvasive risk score which could accurately predict positive GPC-3 HCC, which may help identify potential responders for GPC-3-targeted immunotherapy.

Quantitative assessment of lumbar spine bone marrow in patients with different severity of CKD by IDEAL-IQ magnetic resonance sequence

BACKGROUND

Chronic kidney disease (CKD) has a significant negative impact on bone health. Bone marrow is an essential component of bone, mainly composed of trabecular bone and fat. The IDEAL-IQ sequence of MRI allows indirect quantification of trabecular bone mass by R2* and direct quantification of bone marrow fat content by FF map, respectively.

OBJECTIVE

Our objective was to explore the association of CKD severity with bone marrow using IDEAL-IQ and whether mineral and bone metabolism markers alter this association.

METHOD

We recruited 68 CKD patients in this cross-sectional research (15 with CKD stages 3-4, 26 with stage 5, and 27 with stage 5d). All patients underwent lumbar spine IDEAL-IQ, BMD, and several bone metabolism markers (iPTH, 25-(OH)-VitD, calcium and phosphorus). Multiple linear regression analysis was used to examine the association of CKD severity with MRI measurements (R2* and FF).

RESULTS

More severe CKD was associated with a higher R2* value [CKD 5d versus 3-4: 30.077 s^-1 (95% CI: 12.937, 47.217), P for trend < 0.001], and this association was attenuated when iPTH was introduced [CKD 5d versus 3-4: 19.660 s^-1 (95% CI: 0.205, 39.114), P for trend = 0.042]. Furthermore, iPTH had an association with R2* value [iPTH (pg/mL): 0.033 s^-1 (95% CI: 0.001, 0.064), P = 0.041]. Besides, FF was mainly affected by age and BMI, but not CKD.

CONCLUSIONS

The bone marrow R2* value measured by IDEAL-IQ sequence is associated with CKD severity and iPTH. The R2* of IDEAL-IQ has the potential to reflect lumbar bone changes in patients with CKD.

A label-free electrochemical aptasensor based on platinum@palladium nanoparticles decorated with hemin-reduced graphene oxide as a signal amplifier for glypican-3 determination

An electrochemical aptasensor for highly sensitive detection of glypican-3 has been developed using the GPC3 aptamer as the biorecognition probe and H-rGO-Pt@Pd NPs as an electroactive reagent.

Magnetic resonance imaging and diffusion-weighted imaging-based histogram analyses in predicting glypican 3-positive hepatocellular carcinoma

PURPOSE

We aimed to investigate the potential MR imaging findings in predicting glypican-3 (GPC3)-positive hepatocellular carcinomas (HCCs), with special emphasis on diffusion-weighted imaging (DWI)-based histogram analyses.

METHODS

Forty-three patients with pathologically-confirmed GPC3-negative HCCs and 100 patients with GPC3-positive HCCs were retrospectively evaluated using contrast-enhanced MRI and DWI. Clinical characteristics and MRI features including DWI-based histogram features were assessed and compared between the two groups. Univariate and multivariate analyses were used to identify the significant clinico-radiologic variables associated with GPC3 expressions that were then incorporated into a predictive nomogram. Nomogram performance was evaluated based on calibration, discrimination, and decision curve analyses.

RESULTS

Features significantly related to GPC3-positive HCCs at univariate analyses were serum alpha-fetoprotein (AFP) levels >20 ng/mL (P < 0.0001), absence of enhancing capsule (P = 0.040), peritumoral enhancement appearance on the arterial phase (P = 0.049), as well as lower mean (P = 0.0278), median (P = 0.0372) and 75th percentile (P = 0.0085) apparent diffusion coefficient (ADC) values. At multivariate analysis, the AFP levels (odds ratio, 11.236; P < 0.0001) and 75th percentile ADC values (odds ratio, 1.009; P = 0.033) were independent risk factors associated with GPC3-positive HCCs. When both criteria were combined, both sensitivity (79.0 %) and specificity (79.1 %) greater than 75 % were achieved, and satisfactory predictive nomogram performance was obtained with a C-index of 0.804 (95 % confidence interval, 0.729-0.866). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

Elevated serum AFP levels and lower 75th percentile ADC values were helpful in differentiating GPC3-positive and GPC3-negative HCCs. The combined nomogram achieved satisfactory preoperative risk prediction of GPC3 expression in HCC patients.