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

MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma

OBJECTIVES

It is essential yet highly challenging to preoperatively diagnose variant histologies such as urothelial carcinoma with squamous differentiation (UC w/SD) from pure UC in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. We developed a non-invasive automated machine learning (AutoML) model to preoperatively differentiate UC w/SD from pure UC in patients with MIBC.

METHODS

A total of 119 MIBC patients who underwent baseline bladder MRI were enrolled in this study, including 38 patients with UC w/SD and 81 patients with pure UC. These patients were randomly assigned to a training set or a test set (3:1). An AutoML model was built from the training set, using 13 selected radiomic features from T2-weighted imaging, semantic features (ADC values), and clinical features (tumor length, tumor stage, lymph node metastasis status), and subsequent ten-fold cross-validation was performed. A test set was used to validate the proposed model. The AUC of the ROC curve was then calculated for the model.

RESULTS

This AutoML model enabled robust differentiation of UC w/SD and pure UC in patients with MIBC in both training set (ten-fold cross-validation AUC = 0.955, 95% confidence interval [CI]: 0.944-0.965) and test set (AUC = 0.932, 95% CI: 0.812-1.000).

CONCLUSION

The presented AutoML model, that incorporates the radiomic, semantic, and clinical features from baseline MRI, could be useful for preoperative differentiation of UC w/SD and pure UC.

CLINICAL RELEVANCE STATEMENT

This MRI-based automated machine learning (AutoML) study provides a non-invasive and low-cost preoperative prediction tool to identify the muscle-invasive bladder cancer patients with variant histology, which may serve as a useful tool for clinical decision-making.

KEY POINTS

• It is important to preoperatively diagnose variant histology from urothelial carcinoma in patients with muscle-invasive bladder carcinoma (MIBC), as their treatment strategy varies significantly. • An automated machine learning (AutoML) model based on baseline bladder MRI can identify the variant histology (squamous differentiation) from urothelial carcinoma preoperatively in patients with MIBC. • The developed AutoML model is a non-invasive and low-cost preoperative prediction tool, which may be useful for clinical decision-making.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

BACKGROUND

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

AIM

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

METHODS

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

RESULTS

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

CONCLUSION

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

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.

Quantitative image features of gadoxetic acid-enhanced MRI for predicting glypican-3 expression of small hepatocellular carcinoma ≤3 cm

AIM

To explore the value of quantitative image features of gadoxetic acid-enhanced magnetic resonance imaging (MRI) for predicting Gglypican-3 (GPC3) expression of single hepatocellular carcinoma (HCC) ≤3 cm.

MATERIALS AND METHODS

One hundred and forty-nine patients with histopathologically confirmed HCC were included retrospectively. Quantitative image features and clinicopathological parameters were analysed. The significant predictors for GPC3 expression were identified using multivariate logistic regression analyses. Nomograms were constructed from the prediction model and the progression-free survival (PFS) rate was evaluated by the Kaplan-Meier method.

RESULTS

The tumour-to-liver signal intensity (SI) ratio on the hepatobiliary phase (HBP; odds ratio [OR] = 0.004; p=0.001), serum alpha-fetoprotein (AFP) > 20 ng/ml (OR=6.175; p<0.001), and non-smooth tumour margin (OR=4.866; p=0.002) were independent significant factors for GPC3 expression. When the three factors were combined, the diagnostic specificity was 97.7% (42/43). The nomogram based on the predictive model performed satisfactorily (C-index: 0.852). Kaplan-Meier curves showed that patients with GPC3-positive HCCs have lower PFS rates than patients with GPC3-negative HCCs (Log-rank test, p=0.006).

CONCLUSION

The tumour-to-liver SI ratio on the HBP combined with serum AFP >20 ng/ml and non-smooth tumour margin are potential predictive factors for GPC3 expression of small HCC ≤3cm. GPC3 expression is correlated with a poor prognosis in HCC patients.

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.

Predicting glypican-3 expression in hepatocellular carcinoma: A comprehensive analysis using combined contrast-enhanced ultrasound and clinical factors

OBJECTIVE

Glypican-3 (GPC3) has emerged as a significant marker for the diagnosis and prognosis of hepatocellular carcinoma (HCC) and has garnered considerable attention as an immunotherapeutic target. In this study, we propose a combination of preoperative contrast-enhanced ultrasound (CEUS) imaging features and clinical factors to predict the positive expression of GPC3 in HCC patients.

METHODS

We retrospectively included 30 cases of GPC3-negative HCC and 115 cases of GPC3-positive HCC patients who underwent conventional ultrasound and CEUS evaluation. We assessed and compared the clinical characteristics, conventional ultrasound features, and CEUS features between the two groups of HCC patients. Based on the clinical and ultrasound features between the two groups, we developed a binary logistic regression model for predicting GPC3-positive HCC.

RESULTS

A total of 145 HCC patients were included in this study. Binary logistic regression analysis showed that AFP > 20 ng/mL (OR = 4.047; 95% CI: 1.467-11.16; p = 0.007), arterial phase hyperenhancement (APHE) (OR = 12.557; 95% CI: 3.608-43.706; p < 0.001), and asynchronous perfusion (OR = 4.209; 95% CI: 1.206-14.691; p = 0.024) were predictive factors for GPC3-positive HCC. Receiver operating characteristic (ROC) analysis was conducted to predict GPC3-positive expression. The model combining the three independent predictive factors showed good predictive performance (AUC 0.817, 95% CI: 0.731-0.902, sensitivity: 91.3%, specificity: 60.0%). This combined model demonstrated excellent discriminatory ability to predict GPC3-positive HCC.

CONCLUSION

Preoperative integration of CEUS features and clinical factors can non-invasively and effectively identify GPC3-positive HCC patients, providing valuable assistance in making personalized treatment decisions.

Magnetic resonance imaging for characterization of hepatocellular carcinoma metabolism

With a better understanding of the pathophysiological and metabolic changes in hepatocellular carcinoma (HCC), multiparametric and novel functional magnetic resonance (MR) and positron emission tomography (PET) techniques have received wide interest and are increasingly being applied in preclinical and clinical research. These techniques not only allow for non-invasive detection of structural, functional, and metabolic changes in malignant tumor cells but also characterize the tumor microenvironment (TME) and the interactions of malignant tumor cells with the TME, which has hypoxia and low pH, resulting from the Warburg effect and accumulation of metabolites produced by tumor cells and other cellular components. The heterogeneity and complexity of the TME require a combination of images with various parameters and modalities to characterize tumors and guide therapy. This review focuses on the value of multiparametric magnetic resonance imaging and PET/MR in evaluating the structural and functional changes of HCC and in detecting metabolites formed owing to HCC and the TME.

Diagnostic accuracy and prognostic significance of Glypican-3 in hepatocellular carcinoma: A systematic review and meta-analysis

Purpose

Glypican-3 (GPC-3) expression is abnormal in the occurrence and development of hepatocellular carcinoma (HCC). To explore whether GPC-3 has diagnostic accuracy and prognostic significance of HCC, we did a systematic review and meta-analysis.

Method

PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure were searched with keywords “GPC-3” and “HCC” and their MeSH terms from inception to July 2022. We applied the hierarchical summary receiver operating characteristic model and evaluated the diagnostic value of GPC-3 alone and combination, and the correlation between high and low GPC-3 expression on clinicopathological features and survival data in prognosis.

Results

Forty-one original publications with 6,305 participants were included, with 25 of them providing data for diagnostic value and 18 records were eligible for providing prognostic value of GPC-3. GPC-3 alone got good diagnostic value in patients with HCC when compared with healthy control and moderate diagnostic value when compared with patients with cirrhosis. In addition, combination of GPC-3 + AFP and GPC-3 + GP73 got great diagnostic value in HCC versus cirrhosis groups; the combination of GPC-3 can also improve the diagnostic accuracy of biomarkers. Moreover, we discovered that overexpression of GPC-3 was more likely found in HBV infection, late tumor stage, and microvascular invasion groups and causes shorter overall survival and disease free survival, which means poor prognosis.

Conclusion

GCP-3 could be used as a biomarker in HCC diagnosis and prognosis, especially in evaluated diagnostic value in combination with AFP or GP73, and in forecasting worse survival data of overexpression GPC-3

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42022351566].

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.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.