Imaging biomarkers for well and moderate hepatocellular carcinoma: preoperative magnetic resonance image and histopathological correlation

Diagnostic Model for Proliferative HCC Using LI-RADS: Assessing Therapeutic Outcomes in Hepatectomy and TKI-ICI Combination

BACKGROUND

Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis.

PURPOSE

To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value.

STUDY TYPE

Retrospective.

POPULATION

241 HCC patients underwent hepatectomy (90 proliferative HCCs: 151 nonproliferative HCCs), divided into the training (N = 167) and validation (N = 74) sets. 57 HCC patients received combination therapy with tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs).

FIELD STRENGTH/SEQUENCE

3.0 T, T1- and T2-weighted, diffusion-weighted, in- and out-phase, T1 high resolution isotropic volume excitation and dynamic gadoxetic acid-enhanced imaging.

ASSESSMENT

LI-RADS v2018 and other MRI features (intratumoral artery, substantial hypoenhancing component, hepatobiliary phase peritumoral hypointensity, and irregular tumor margin) were assessed. A diagnostic model for proliferative HCC was established, stratifying patients into high- and low-risk groups. Follow-up occurred every 3-6 months, and recurrence-free survival (RFS), progression-free survival (PFS) and overall survival (OS) in different groups were compared.

STATISTICAL TESTS

Fisher's test or chi-square test, t-test or Mann-Whitney test, logistic regression, Harrell's concordance index (C-index), Kaplan-Meier curves, and Cox proportional hazards. Significance level: P < 0.05.

RESULTS

The diagnostic model, incorporating corona enhancement, rim arterial phase hyperenhancement, infiltrative appearance, intratumoral artery, and substantial hypoenhancing component, achieved a C-index of 0.823 (training set) and 0.804 (validation set). Median follow-up was 32.5 months (interquartile range [IQR], 25.1 months) for postsurgery patients, and 16.8 months (IQR: 13.2 months) for combination-treated patients. 99 patients experienced recurrence, and 30 demonstrated tumor nonresponse. Differences were significant in RFS and OS rates between high-risk and low-risk groups post-surgery (40.3% vs. 65.8%, 62.3% vs. 90.1%, at 5 years). In combination-treated patients, PFS rates differed significantly (80.6% vs. 7.7% at 2 years).

DATA CONCLUSION

The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

CT-based radiomics for predicting pathological grade in hepatocellular carcinoma

Objective

To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade predictions based on contrast-enhanced CT (CECT).

Methods

Patients with pathologically confirmed HCC after surgery and underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation datasets. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

In total 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation datasets, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model were evaluated as the best for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training dataset; AUC = 0.842 in the validation dataset) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.

Conclusions

Low- and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection

BACKGROUND

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis.

AIM

To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection.

METHODS

A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed.

RESULTS

Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC.

CONCLUSION

Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Clear cell hepatocellular carcinoma: Gd-EOB-DTPA-enhanced MR imaging features and prognosis

PURPOSE

To investigate imaging findings on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) and prognosis of clear cell hepatocellular carcinoma (CCHCC) comparing with non-otherwise specified hepatocellular carcinoma (NOS-HCC).

METHODS

The clinical, pathological and MR imaging features of 42 patients with CCHCC and 84 age-matched patients with NOS-HCC were retrospectively analyzed from January 2015 to October 2021. Univariate and multivariate logistic regression and Cox regression analyses were performed to identify independent diagnostic and prognostic factors for CCHCC. Disease-free survival (DFS) and overall survival (OS) were determined by Kaplan-Meier analysis.

RESULTS

CCHCC showed fat content more frequently (P < 0.001) and relatively higher Edmondson tumor grade (P = 0.001) compared with NOS-HCC. The lesion-to-muscle ratio (LMR) and lesion-to-liver ratio (LLR) of CCHCC on pre-enhancement T1-weighted imaging (pre-T1WI) (P = 0.001, P = 0.003) and hepatobiliary phase (HBP) (P = 0.007, P = 0.048) were significantly higher than those of NOS-HCC. The area under the curve (AUC) for fat content, LLR on pre-T1WI and their combination with better diagnostic performance in predicting CCHCC were 0.678, 0.666, and 0.750, respectively. There was no statistically significant difference in clinical outcomes between CCHCC and NOS-HCC. Multivariate Cox analysis confirmed that tumor size > 2 cm and enhancing capsule were independent prognostic factors for DFS and OS among CCHCC patients.

CONCLUSION

Fat content and adjusted lesion signal intensity on pre-T1WI and HBP could be used to differentiate CCHCC from NOS-HCC. CCHCC had similar prognosis with NOS-HCC.

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Purpose

To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model.

Methods

A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model.

Results

The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively.

Conclusion

CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

Predicting histologic differentiation of solitary hepatocellular carcinoma up to 5 cm on gadoxetate disodium-enhanced MRI

BACKGROUND

To establish a preoperative score based on gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators for predicting histologic differentiation of solitary HCC up to 5 cm.

METHODS

From July 2015 to January 2022, consecutive patients with surgically proven solitary HCC measuring ≤ 5 cm at preoperative EOB-MRI were retrospectively enrolled. All MR images were independently evaluated by two radiologists who were blinded to all clinical and pathologic information. Univariate and multivariate logistic regression analyses were performed to identify significant clinicoradiological features associated with poorly differentiated (PD) HCC, which were then incorporated into the predictive score. The predictive score was validated in an independent validation set by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

A total of 182 patients were included, 42 (23%) with PD HCC. According to the multivariate analysis, marked hepatobiliary phase hypointensity (odds ratio [OR], 9.98), LR-M category (OR, 5.60), and serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 3.58) were incorporated into the predictive model; the predictive score achieved an AUC of 0.802 and 0.830 on the training and validation sets, respectively. The sensitivity, specificity, and accuracy of the predictive score were 66.7%, 85.7%, and 81.3%, respectively, on the training set and 66.7%, 81.0%, and 77.8%, respectively, on the validation set.

CONCLUSION

The proposed score integrating two EOB-MRI features and AFP level can accurately predict PD HCC in the preoperative setting.

Preoperative prediction model for macrotrabecular-massive hepatocellular carcinoma based on contrast-enhanced CT and clinical characteristics: a retrospective study

Objective

To investigate the predictive value of contrast-enhanced computed tomography (CECT) imaging features and clinical factors in identifying the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) preoperatively.

Methods

This retrospective study included 101 consecutive patients with pathology-proven HCC (35 MTM subtype vs. 66 non-MTM subtype) who underwent liver surgery and preoperative CECT scans from January 2017 to November 2021. The imaging features were evaluated by two board-certified abdominal radiologists independently. The clinical characteristics and imaging findings were compared between the MTM and non-MTM subtypes. Univariate and multivariate logistic regression analyses were performed to investigate the association of clinical-radiological variables and MTM-HCCs and develop a predictive model. Subgroup analysis was also performed in BCLC 0-A stage patients. Receiver operating characteristic (ROC) curves analysis was used to determine the optimal cutoff values and the area under the curve (AUC) was employed to evaluate predictive performance.

Results

Intratumor hypoenhancement (odds ratio [OR] = 2.724; 95% confidence interval [CI]: 1.033, 7.467; p = .045), tumors without enhancing capsules (OR = 3.274; 95% CI: 1.209, 9.755; p = .03), high serum alpha-fetoprotein (AFP) (≥ 228 ng/mL, OR = 4.101; 95% CI: 1.523, 11.722; p = .006) and high hemoglobin (≥ 130.5 g/L; OR = 3.943; 95% CI: 1.466, 11.710; p = .009) were independent predictors for MTM-HCCs. The clinical-radiologic (CR) model showed the best predictive performance, achieving an AUC of 0.793, sensitivity of 62.9% and specificity of 81.8%. The CR model also effectively identify MTM-HCCs in early-stage (BCLC 0-A stage) patients.

Conclusion

Combining CECT imaging features and clinical characteristics is an effective method for preoperatively identifying MTM-HCCs, even in early-stage patients. The CR model has high predictive performance and could potentially help guide decision-making regarding aggressive therapies in MTM-HCC patients.

Prediction for Aggressiveness and Postoperative Recurrence of Hepatocellular Carcinoma Using Gadoxetic Acid-Enhanced Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

To investigate the predictive value of gadoxetic acid-enhanced magnetic resonance imaging (MRI) features on the pathologic grade, microvascular invasion (MVI), and cytokeratin-19 (CK19) expression in hepatocellular carcinomas (HCC), and to evaluate their association with postoperative recurrence of HCC.

MATERIALS AND METHODS

This retrospective study included 147 patients with surgically confirmed HCCs who underwent gadoxetic-enhanced MRI. The lesions were evaluated quantitatively in terms of the relative enhancement ratio (RER), and qualitatively based on imaging features and clinical parameters. Logistic regression analyses were performed to investigate the value of these parameters in predicting the pathologic grade, MVI, and CK19 in HCC. Predictive factors for postoperative recurrence were determined using a Cox proportional hazards model.

RESULTS

Peritumoral enhancement (odds ratio [OR], 3.396; p = 0.025) was an independent predictor of high pathologic grades. Serum protein induced by vitamin K absence or antagonist (PIVKA) level > 40 mAU/mL (OR, 3.763; p = 0.018) and peritumoral hypointensity (OR, 4.343; p = 0.003) were independent predictors of MVI. Predictors of CK19 included serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 4.576; p = 0.005), rim enhancement (OR, 5.493; p = 0.024), and lower RER (OR, 0.013; p = 0.011). Peritumoral hypointensity (hazard ratio [HR], 1.957; p = 0.027) and poor pathologic grades (HR, 2.339; p = 0.043) were independent predictors of recurrence.

CONCLUSION

We demonstrated the value of preoperative gadoxetic-enhanced MRI in predicting aggressive pathological features of HCC. Poor pathologic grades and peritumoral hypointensity may independently predict the recurrence of HCC.

An Elevated Neutrophil-to-Lymphocyte Ratio Predicts Poor Prognosis in Patients with Liver Cancer after Interventional Treatments

This study is aimed at examining the prognostic value of blood neutrophil-to-lymphocyte ratio (NLR) in patients with hepatocellular carcinoma (HCC). Demographic and clinical data of 543 HCC patients treated with interventional therapies were retrospectively analyzed. Preoperative NLRs were determined and receiver operating characteristic (ROC) curves were plotted for survival time in patients with high (NLR ≥3.8) and low (NLR<3.8) NLR. The median overall survival (OS) was 1241 days after interventional therapies and was significantly reduced in the high NLR group when compared to the low NLR group. The median progression-free survival time (PFST) of patients was also significantly shorter in the high NLR group than in the low NLR group. Univariate analysis revealed that tumor type, therapy method, maximum tumor size (>3 mm), and NLR (>3.8) were risk factors for OST and PFST ( $P<0.05$ ). Multivariate analysis indicated that tumor type, maximum tumor diameter, therapy method, and NLR (>3.8) were independent risk factors for PFST ( $P<0.05$ ). Our results demonstrate that preoperative NLR has prognostic value for patients with HCC undergoing interventional therapies, and high NLR is an indication of poor prognosis.

Role of MRI-Derived Radiomics Features in Determining Degree of Tumor Differentiation of Hepatocellular Carcinoma

Background: To investigate radiomics ability in predicting hepatocellular carcinoma histological degree of differentiation by using volumetric MR imaging parameters. Methods: Volumetric venous enhancement and apparent diffusion coefficient were calculated on baseline MRI of 171 lesions. Ninety-five radiomics features were extracted, then random forest classification identified the performance of the texture features in classifying tumor degree of differentiation based on their histopathological features. The Gini index was used for split criterion, and the random forest was optimized to have a minimum of nine participants per leaf node. Predictor importance was estimated based on the minimal depth of the maximal subtree. Results: Out of 95 radiomics features, four top performers were apparent diffusion coefficient (ADC) features. The mean ADC and venous enhancement map alone had an overall error rate of 39.8%. The error decreased to 32.8% with the addition of the radiomics features in the multi-class model. The area under the receiver-operator curve (AUC) improved from 75.2% to 83.2% with the addition of the radiomics features for distinguishing well- from moderately/poorly differentiated HCCs in the multi-class model. Conclusions: The addition of radiomics-based texture analysis improved classification over that of ADC or venous enhancement values alone. Radiomics help us move closer to non-invasive histologic tumor grading of HCC.

The Value of CT Perfusion Parameters and Apparent Diffusion Coefficient Value of Magnetic Resonance Diffusion Weighted Imaging in Diagnosis of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma is one of the malignant tumors with the highest incidence in the world. According to the latest statistics of the National Cancer Center, the incidence of liver cancer ranks fifth in malignant tumors and its mortality rate ranks second in China, which seriously threatens people' s life and health.

Aim

To investigate the value of CT perfusion parameters and apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) in the diagnosis of hepatocellular carcinoma.

Methods

43 patients with hepatocellular carcinoma and 40 patients with hepatic hemangioma treated in our hospital from August 2018 to August 2021 were selected for CT perfusion imaging and MRI examination.

Results

The liver blood flow (BF), liver blood volume (BV), and hepatic artery perfusion (HAP) in the hepatocellular carcinoma group were (267.38 ± 35.59) ml/(min·100 g), (30.20 ± 8.82) ml/100 g, and (0.64 ± 0.10) ml/(min·ml), respectively, which were significantly higher than those in the hepatic hemangioma group (p < 0.05). The ADC value of hepatocellular carcinoma DWI sequence was (1.20 ± 0.17) ×10⁻³ mm², which was significantly lower than that of hepatic hemangioma (p < 0.05). The area under ROC curve of BF, BV, HAP, and ADC values for hepatocellular carcinoma was 0.860, 0.754, 0.804, and 0.890, respectively. The area under ROC curve of the four groups was compared (p > 0.05).

Conclusion

CT perfusion parameters BF, BV, HAP, and DWI sequence ADC values have certain application value in the diagnosis of hepatocellular carcinoma, and there is no significant difference between the diagnostic value of each parameter.

Preoperative gadoxetic acid-enhanced MRI predicts aggressive pathological features in LI-RADS category 5 hepatocellular carcinoma

AIM

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features and non-LI-RADS imaging features can predict aggressive pathological features in adult patients with hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

From February 2018 to September 2021, 236 adult patients with cirrhosis or hepatitis B virus infection in which liver cancer was suspected underwent MRI within 1 month before surgery. Significant MRI findings and alpha-fetoprotein (AFP) level predicted high-grade HCC and microvascular invasion (MVI) by univariate and multivariate logistic regression models.

RESULTS

The study included 112 patients with histopathologically confirmed liver cancer (≤5 cm), 35 of whom (31.3%) high-grade HCC and 42 of 112 (37.5%) patients had MVI. Mosaic architecture (odds ratio [OR] = 6.031; 95% confidence interval [CI]: 1.366, 26.626; p=0.018), coronal enhancement (OR=5.878; 95% CI: 1.471, 23.489; p=0.012), and intratumoural vessels (OR=5.278; 95% CI: 1.325, 21.020; p=0.018) were significant independent predictors of high-grade HCC. A non-smooth tumour margin (OR=10.237; 95% CI: 1.547, 67.760; p=0.016), coronal enhancement (OR=3.800; 95% CI: 1.152, 12.531; p=0.028), and peritumoural hypointensity on the hepatobiliary phase (HBP; OR=10.322; 95% CI: 2.733, 38.986; p=0.001) were significant independent predictors of MVI.

CONCLUSION

In high-risk adult patients with single LR-5 HCC (≤5 cm), mosaic architecture, coronal enhancement, and intratumoural vessels are independent predictors of high-grade HCC. Non-smooth tumour margin, coronal enhancement, and peritumoural hypointensity on HBP independently predicted MVI.

Quantification of contrast agent uptake in the hepatobiliary phase helps to differentiate hepatocellular carcinoma grade

This study aimed to assess the degree of differentiation of hepatocellular carcinoma (HCC) using Gd-EOB-DTPA-assisted magnetic resonance imaging (MRI) with T1 relaxometry. Thirty-three solitary HCC lesions were included in this retrospective study. This study's inclusion criteria were preoperative Gd-EOB-DTPA-assisted MRI of the liver and a histopathological evaluation after hepatic tumor resection. T1 maps of the liver were evaluated to determine the T1 relaxation time and reduction rate between the native phase and hepatobiliary phase (HBP) in liver lesions. These findings were correlated with the histopathologically determined degree of HCC differentiation (G1, well-differentiated; G2, moderately differentiated; G3, poorly differentiated). There was no significant difference between well-differentiated (950.2 ± 140.2 ms) and moderately/poorly differentiated (1009.4 ± 202.0 ms) HCCs in the native T1 maps. After contrast medium administration, a significant difference (p ≤ 0.001) in the mean T1 relaxation time in the HBP was found between well-differentiated (555.4 ± 140.2 ms) and moderately/poorly differentiated (750.9 ± 146.4 ms) HCCs. For well-differentiated HCCs, the reduction rate in the T1 time was significantly higher at 0.40 ± 0.15 than for moderately/poorly differentiated HCCs (0.25 ± 0.07; p = 0.006). In conclusion this study suggests that the uptake of Gd-EOB-DTPA in HCCs is correlated with tumor grade. Thus, Gd-EOB-DTPA-assisted T1 relaxometry can help to further differentiation of HCC.

Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma

BACKGROUND

Tumor-infiltrating CD8 cells and expression of programmed cell death ligand 1 (PD-L1) are immune checkpoint markers in patients with hepatocellular carcinoma (HCC). We aimed to determine the ability of preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI) findings to predict CD8 cell density and PD-L1 expression in HCC.

METHODS

A total of 120 patients with HCC who underwent 3.0-T gadoxetic acid-enhanced MRI before curative resection from January 2016 to June 2020 were enrolled and divided into a training set (n = 84) and a testing set (n = 36). Thirty-four patients with advanced stage HCC who received anti-PD-1 inhibitor between January 2017 and April 2020 and underwent pretreated gadoxetic acid-enhanced MRI scans were enrolled in an independent validation set. PD-L1 expression and CD8 cell infiltration were assessed with immunohistochemical staining, respectively. Two radiologists blinded to pathology results evaluated the pretreated MR features in consensus. Logistic regression and the receiver operating characteristic curve (ROC) analyses were used to determine the value of image features to predict high CD8 cell density, PD-L1 positivity and the combination of high CD8 cell density and PD-L1 positivity in HCC in the training set and validated the findings in the testing set. The associations of MRI predictors with the objective response to immunotherapy were assessed in the independent validation.

RESULTS

In the training set, the independent MRI predictors were irregular tumor margin (ITM, P = 0.008) and peritumoral low signal intensity (PLSI) on hepatobiliary phase (HBP) images (P < 0.001) for PD-L1 positivity, absence of an enhancing capsule (AEC, P = 0.001) and PLSI on HBP images (P = 0.025) for high CD8 cell density, and PLSI on HBP images (P = 0.001) and ITM (P = 0.023) for the both. The area under the curves (AUCs) of the predictive models for evaluating PD-L1 positivity, high CD8 cell density and the combination of high CD8 cell density and PD-L1 positivity were 0.810 and 0.809, 0.740 and 0.728, and 0.809 and 0.874 in the training and testing set, respectively. The objective response was demonstrated to be associated with the combination of PLSI on HBP images and ITM (PHI, P = 0.004), and the combination of PLSI on HBP images and AEC (PHA, P = 0.012) in the independent validation set.

CONCLUSIONS

Pretreated MRI features have the potential to identify patients with HCC in an immune-activated state and predict outcomes of immunotherapy. Trial registration The study was retrospectively registered on March 5, 2020 with registration no. [2020] 02-012-01.

Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma?

PURPOSE

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC.

MATERIALS AND METHODS

From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model.

RESULTS

Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size > 5 cm (OR = 9.852; p < 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p < 0.001), and serum AFP level > 400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence.

CONCLUSION

LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.

Retrospective CT/MRI Texture Analysis of Rapidly Progressive Hepatocellular Carcinoma

Rapidly progressive hepatocellular carcinoma (RPHCC) is a subset of hepatocellular carcinoma that demonstrates accelerated growth, and the radiographic features of RPHCC versus non-RPHCC have not been determined. The purpose of this retrospective study was to use baseline radiologic features and texture analysis for the accurate detection of RPHCC and subsequent improvement of clinical outcomes. We conducted a qualitative visual analysis and texture analysis, which selectively extracted and enhanced imaging features of different sizes and intensity variation including mean gray-level intensity (mean), standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis at each spatial scaling factor (SSF) value of RPHCC and non-RPHCC tumors in a computed tomography (CT) cohort of n = 11 RPHCC and n = 11 non-RPHCC and a magnetic resonance imaging (MRI) cohort of n = 13 RPHCC and n = 10 non-RPHCC. There was a statistically significant difference across visual CT irregular margins p = 0.030 and CT texture features in SSF between RPHCC and non-RPHCC for SSF-6, coarse-texture scale, mean p = 0.023, SD p = 0.053, MPP p = 0.023. A composite score of mean SSF-6 binarized + SD SSF-6 binarized + MPP SSF-6 binarized + irregular margins was significantly different between RPHCC and non-RPHCC (p = 0.001). A composite score ≥3 identified RPHCC with a sensitivity of 81.8% and specificity of 81.8% (AUC = 0.884, p = 0.002). CT coarse-texture-scale features in combination with visually detected irregular margins were able to statistically differentiate between RPHCC and non-RPHCC. By developing an image-based, non-invasive diagnostic criterion, we created a composite score that can identify RPHCC patients at their early stages when they are still eligible for transplantation, improving the clinical course of patient care.

Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study

Background

To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC).

Method

One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient.

Results

The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined.

Conclusions

Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.