Arterial Phase Hyperenhancement Research Articles

Gadoxetic Acid-Enhanced Magnetic Resonance Imaging Features Can Predict Immune-Excluded Phenotype of Hepatocellular Carcinoma

Introduction: Immunotherapy is the first-line treatment for intermediate-advanced stage hepatocellular carcinoma (HCC), although its outcomes vary. This study aimed to identify imaging biomarkers of immunotherapy susceptibility linked to gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) and immune phenotypes, particularly immune-excluded phenotypes, with a tumor immune barrier. Methods: We performed immunohistochemical staining with a CD8+ antibody, and samples were classified into immune-inflamed, -intermediate, -excluded, and -ignored phenotypes. We assessed EOB-MRI findings obtained from 104 patients who underwent hepatectomy for HCC and evaluated the relationship between MRI findings and immune phenotype. Spatial transcriptome analysis of tumor tissues in each immune phenotype was performed to characterize the MRI findings. For validation, we analyzed the treatment effect on 60 nodules in another cohort of 27 patients who received combined immunotherapy using anti-programmed death-ligand 1 and anti-vascular endothelial growth factor (VEGF) antibodies. Results: HCCs with rim arterial phase hyperenhancement (APHE) (odds ratio [OR] 17.3 P=0.009), peritumoral enhancement on the arterial phase (OR 8.6, P<0.004), and intermediate intensity on the hepatobiliary phase (HBP) measured with a visual 3-point scale (OR 28.2, P=0.002) were associated with immune-excluded phenotype, where tumors tended to be larger and of the single nodular type with extranodular growth and confluent multinodular rather than the simple nodular type. Spatial transcriptome analysis revealed a spatial relationship among cytotoxic T lymphocytes, VEGF signals, and cancer-associated fibroblasts at the tumor-invasive margins in this phenotype. From the validation study, nodules with any one of these three imaging findings had a significantly prolonged time to-nodular progression (P=0.007, median not reached vs. 226 days). Conclusion: HCCs with rim APHE, peritumoral enhancement on arterial phase, and intermediate intensity on HBP with visual 3-point scale could be non-invasive biomarkers to predict the immune-excluded phenotype with tumor immune barrier. These HCCs were most likely to respond to the combined immunotherapy.

Inter-reader agreement of LI-RADS treatment response algorithm among three readers with different seniorities for hepatocellular carcinoma after locoregional therapy.

The accurate evaluation of tumor response after locoregional therapy is crucial for adjusting therapeutic strategy and guiding individualized follow-up. To determine the inter-reader agreement of the LR-TR algorithm for hepatocellular carcinoma treated with locoregional therapy among radiologists with different seniority. A total of 275 treated observations on 249 MRI scans from 99 patients were retrospectively collected. Three readers of different seniorities (senior, intermediate, and junior with 10, 6, and 2 years of experience in hepatic imaging, respectively) analyzed the presence or absence of features (arterial-phase hyperenhancement and washout) and evaluated LR-TR category. There were substantial inter-reader agreements for overall LR-TR categorization (kappa = 0.704), LR-TR viable (kappa = 0.715), and LR-TR non-viable (kappa = 0.737), but fair inter-reader agreement for LR-TR equivocal (kappa = 0.231) among three readers. The inter-reader agreement was substantial for arterial-phase hyperenhancement (kappa = 0.725), but moderate for washout (kappa = 0.443) among three readers. The inter-reader agreements between two readers were substantial for overall LR-TR categorization (kappa = 0.734, 0.727, 0.652), LR-TR viable (kappa = 0.719, 0.752, 0.678), and LR-TR non-viable (kappa = 0.758, 0.760, 0.694), which were at the same level as the inter-reader agreements among three readers. In addition, the inter-reader agreements between two readers were substantial for arterial-phase hyperenhancement (kappa = 0.733, 0.766, 0.678), but moderate for washout (kappa = 0.473, 0.422, 0.446), which were at the same level as the inter-reader agreements among three readers. LR-TR algorithm demonstrated overall substantial inter-reader agreement among radiologists with different seniority.

Diagnostic performance of Sonazoid-enhanced CEUS in identifying definitive hepatocellular carcinoma in cirrhotic patients according to KLCA-NCC 2022 and APASL 2017 guidelines

ObjectiveThis study aims to assess the diagnostic performance of Sonazoid-contrast-enhanced ultrasound (CEUS) in identifying definitive HCC within hepatic nodules in cirrhotic patients, comparing the KLCA-NCC 2022 and APASL 2017 diagnostic guidelines.Materials and methodsThis retrospective study analyzed cirrhotic patients who underwent Sonazoid-CEUS for liver lesion evaluation between October 2019 and October 2023. HCC diagnosis was based on the KLCA-NCC 2022 and APASL 2017 guidelines. Inter-reader agreement on CEUS imaging features and the diagnostic accuracy of the guidelines were evaluated. Sensitivity and specificity comparisons were made using McNemar’s test.ResultsAmong 400 patients with 432 lesions, CEUS showed excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects. The KLCA-NCC 2022 criteria notably enhanced sensitivity to 96.2%, with specificity and accuracy of 93.8% and 95.8%, respectively. APASL 2017 achieved the highest sensitivity at 97.8%, although specificity dropped to 46.9%, resulting in an accuracy of 90.3%. The KLCA-NCC 2022 guidelines demonstrated significantly higher specificity than APASL 2017 (p < 0.001), while APASL 2017 exhibited the highest sensitivity at 97.8%. Notably, the KLCA-NCC 2022 guidelines also demonstrated an impressive positive predictive value of 98.9%.ConclusionSonazoid-enhanced CEUS, particularly when applied using the KLCA-NCC 2022 guidelines, is an effective diagnostic tool for HCC.Critical relevance statementPerfluorobutane CEUS, particularly in accordance with the KLCA-NCC 2022 guidelines, emerges as a valuable adjunct for diagnosing HCC in cirrhotic patients. It demonstrates superior positive predictive value and specificity compared to APASL 2017, underscoring its potential as an effective diagnostic tool.Key PointsContrast-enhanced (CE)US using Sonazoid with KLCA-NCC 2022 guidelines is highly effective for HCC diagnosis.KLCA-NCC 2022 criteria showed high accuracy, 96.2% sensitivity, and 98.9% PPV.CEUS demonstrated excellent inter-reader consistency in detecting arterial phase hyperenhancement and Kupffer defects.Graphical

Combining CEUS and CT/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC.

To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS. A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations. Reference standards included biopsy, explant histology, and follow-up CT/MRI. The diagnostic performance of the combinations of LI-RADS major features for definitive diagnosis of HCC was calculated. A reverse, stepwise logistical regression sub-analysis was also performed. This study included 114 observations indeterminate on both CT/MRI and CEUS. These observations were categorized as LR-3 (n = 37), LR-4 (n = 41), and LR-M (n = 36) on CT/MRI and LR-3 (n = 48), LR-4 (n = 36), LR-M (n = 29), and LR-TIV (n = 1) on CEUS. Of them, 43.0% (49/114) were confirmed as HCC, 37.3% (43/114) non-malignant, and 19.3% (22/114) non-hepatocellular malignancies. The highest diagnostic accuracy among the combinations of imaging features was achieved in CT/MRI LR-3 observations, where the combination of CEUS arterial phase hyper-enhancement (APHE) + CT/MRI APHE had 96.7% specificity, 75.0% positive predictive value (PPV), and 86.5% accuracy for HCC. The combination of LI-RADS major features on CT/MRI and CEUS showed higher specificity, PPV, and accuracy compared to individual modalities' assessments, particularly for CT/MRI LR-3 observations.

A non-invasive predictive model based on multimodality ultrasonography images to differentiate malignant from benign focal liver lesions

We have developed a non-invasive predictive nomogram model that combines image features from Sonazoid contrast-enhanced ultrasound (SCEUS) and Sound touch elastography (STE) with clinical features for accurate differentiation of malignant from benign focal liver lesions (FLLs). This study ultimately encompassed 262 patients with FLLs from the First Hospital of Shanxi Medical University, covering the period from March 2020 to April 2023, and divided them into training set (n = 183) and test set (n = 79). Logistic regression analysis was used to identify independent indicators and develop a predictive model based on image features from SCEUS, STE, and clinical features. The area under the receiver operating characteristic (AUC) curve was determined to estimate the diagnostic performance of the nomogram with CEUS LI-RADS, and STE values. The C-index, calibration curve, and decision curve analysis (DCA) were further used for validation. Multivariate and LASSO logistic regression analyses identified that age, ALT, arterial phase hyperenhancement (APHE), enhancement level in the Kupffer phase, and Emean by STE were valuable predictors to distinguish malignant from benign lesions. The nomogram achieved AUCs of 0.988 and 0.978 in the training and test sets, respectively, outperforming the CEUS LI-RADS (0.754 and 0.824) and STE (0.909 and 0.923) alone. The C-index and calibration curve demonstrated that the nomogram offers high diagnostic accuracy with predicted values consistent with actual values. DCA indicated that the nomogram could increase the net benefit for patients. The predictive nomogram innovatively combining SCEUS, STE, and clinical features can effectively improve the diagnostic performance for focal liver lesions, which may help with individualized diagnosis and treatment in clinical practice.

Tumor Growth Pattern and Intra- and Peritumoral Radiomics Combined for Prediction of Initial TACE Outcome in Patients with Primary Hepatocellular Carcinoma.

Non-invasive methods are urgently needed to assess the efficacy of transarterial chemoembolization (TACE) and to identify patients with hepatocellular carcinoma (HCC) who may benefit from this procedure. This study, therefore, aimed to investigate the predictive ability of tumor growth patterns and radiomics features from contrast-enhanced magnetic resonance imaging (CE-MRI) in predicting tumor response to TACE among patients with HCC. A retrospective study was conducted on 133 patients with HCC who underwent TACE at three centers between January 2015 and April 2023. Enrolled patients were divided into training, testing, and validation cohorts. Rim arterial phase hyperenhancement (Rim APHE), tumor growth patterns, nonperipheral washout, markedly low apparent diffusion coefficient (ADC) value, intratumoral arteries, and clinical baseline features were documented for all patients. Radiomics features were extracted from the intratumoral and peritumoral regions across the three phases of CE-MRI. Seven prediction models were developed, and their performances were evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA). Tumor growth patterns and albumin-bilirubin (ALBI) score were significantly correlated with tumor response. Tumor growth patterns also showed a positive correlation with tumor burden (r = 0.634, P = 0.000). The Peritumor (AUC = 0.85, 0.71, and 0.77), Clinics_Peritumor (AUC = 0.86, 0.77, and 0.81), and Tumor_Peritumor (AUC = 0.87, 0.77, and 0.80) models significantly outperformed the Clinics and Tumor models (P < 0.05), while the Clinics_Tumor_Peritumor model (AUC = 0.88, 0.81, and 0.81) outperformed the Clinics (AUC = 0.67, 0.77, and 0.75), Tumor (AUC = 0.78, 0.72, and 0.68), and Clinics_Tumor (AUC = 0.82, 0.83, and 0.78) models (P < 0.05 or 0.053, respectively). The DCA curve demonstrated better predictive performance within a specific threshold probability range for Clinics_Tumor_Peritumor. Combining tumor growth patterns, intra- and peri-tumoral radiomics features, and ALBI score could be a robust tool for non-invasive and personalized prediction of treatment response to TACE in patients with HCC.

Benign pericholecystic regeneration mimicking hepatocellular carcinoma: a potential pitfall in imaging of cirrhosis.

Several pseudolesions mimicking malignancy have been reported in cirrhotic and non-cirrhotic livers. Pericholecystic regeneration is among those pseudolesions and can occasionally mimic malignancy. Herein we present a case series comprised of 10 cirrhotic patients (majority due to alcoholic liver disease [ALD] or metabolic dysfunction associated steatotic liver disease [MASLD]) with pericholecystic observations initially categorized as highly suspicious for HCC (LR-4 and LR-5) due to their suspicious enhancement pattern which were later proven to be benign based on biopsy, transplantation or imaging criteria (stability of size and morphology on serial CT or MRI for > 2 years). These observations ranged 2-6.3cm in size, were multiple in most patients and universally resulted in indentations of gallbladder. Arterial phase hyperenhancement and washout, features usually attributed to hepatocellular carcinoma (HCC), were seen in most patients. However, ancillary features of malignancy such as diffusion restriction, increased T2 signal, and hypointensity on hepatobiliary phase MRI were not present. Pericholecystic observations, in particular in the setting of ALD and MASLD, should be carefully assessed and possibility of pseudolesion should be considered especially when the observations are multiple, cause indentation of gallbladder, and lack ancillary features of malignancy.

Advancing microvascular invasion diagnosis: a multi-center investigation of novel MRI-based models for precise detection and classification in early-stage small hepatocellular carcinoma (≤ 3cm).

This study aimed to develop two preoperative magnetic resonance imaging (MRI) based models for detecting and classifying microvascular invasion (MVI) in early-stage small hepatocellular carcinoma (sHCC) patients. MVI is graded as M0 (no invasion), M1 (invasion of five or fewer vessels located within 1cm of the tumor's peritumoral region), and M2 (invasion of more than five vessels or those located more than 1cm from the tumor's surface). This study enrolled 395 early-stage sHCC (≤ 3cm) patients from three centers who underwent preoperative gadopentetate-enhanced MRI. From the first two centers, 310 patients were randomly divided into training (n = 217) and validation (n = 93) cohorts in a 7:3 ratio to develop the first model for predicting MVI presence. Among these, 153 patients with identified MVI were further divided into training (n = 112) and validation (n = 41) cohorts, using the same ratio, to construct the second model for MVI classification. An independent test cohort of 85 patients from the third center to validate both models. Univariate and multivariate logistic regression analyses identified independent predictors of MVI and its classification in the training cohorts. Based on these predictors, two nomograms were developed and assessed for their discriminative ability, calibration, and clinical usefulness. Besides, considering the two models are supposed applied in a serial fashion in the real clinical setting, we evaluate the performance of the two models together on the test cohorts by applying them simultaneously. Kaplan-Meier survival curve analysis was employed to assess the correlation between predicted MVI status and early recurrence, similar to the association observed with actual MVI status and early recurrence. The MVI detection nomogram, with platelet count (PLT), activated partial thromboplastin time (APTT), rim arterial phase hyperenhancement (Rim APHE) and arterial peritumoral enhancement, achieved area under the curve (AUC) of 0.827, 0.761 and 0.798 in the training, validation, and test cohorts, respectively. The MVI classification nomogram, integrating Total protein (TP), Shape, Arterial peritumoral enhancement and enhancement pattern, achieved AUC of 0.824, 0.772, and 0.807 across the three cohorts. When the two models were applied on the test cohorts in a serial fashion, they both demonstrated good performance, which means the two models had good clinical applicability. Calibration and decision curve analysis (DCA) results affirmed the model's reliability and clinical utility. Notably, early recurrence was more prevalent in the MVI grade 2 (M2) group compared to the MVI-absent and M1 groups, regardless of the actual or predicted MVI status. The nomograms exhibited excellent predictive performance for detecting and classifying MVI in patients with early-stage sHCC, particularly identifying high-risk M2 patients preoperatively.

Development and validation of a CT-based nomogram for accurate hepatocellular carcinoma detection in high risk patients.

To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease. A total of 223 patients were divided into training (n=161) and validation (n=62) cohorts between January of 2017 and May of 2022. Logistic analysis was performed, and clinical model and radiological model were developed separately. Finally, a nomogram was established based on clinical and radiological features. All models were evaluated using the area under the curve (AUC). DeLong's test was used to evaluate the differences among these models. In the multivariate analysis, gender (p = 0.014), increased Alpha-fetoprotein (AFP) (p = 0.017), non-rim arterial phase hyperenhancement (APHE) (p = 0.011), washout (p = 0.011), and enhancing capsule (p = 0.001) were the independent differential predictors of HCC. A nomogram was formed with well-fitted calibration curves based on these five factors. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.961(95%CI: 0.935~0.986) and 0.979 (95% CI: 0.949~1), respectively. The nomogram outperformed the clinical and the radiological models in training and validation cohorts. The nomogram incorporating clinical and CT features can be a simple and reliable tool for detecting HCC and achieving risk stratification in patients at high risk for HCC.

Intraindividual comparison of prognostic imaging features of HCCs between MRIs with extracellular and hepatobiliary contrast agents.

Accumulating evidence suggests that certain imaging features of hepatocellular carcinoma (HCC) may have prognostic implications. This study aimed to intraindividually compare MRIs with extracellular contrast agent (ECA-MRI) and hepatobiliary agent (HBA-MRI) for prognostic imaging features of HCC and to compare the prediction of microvascular invasion (MVI) and early recurrence between the two MRIs. The present study included 102 prospectively enrolled at-risk patients (median age, 61.0 years; 83 men) with surgically resected single HCC with both preoperative ECA-MRI and HBA-MRI between July 2019 and June 2023. The McNemar test was used to compare each prognostic imaging feature between the two MRIs. Significant imaging features associated with MVI were identified by multivariable logistic regression analysis, and early recurrence rates (<2 years) were compared between the two MRIs. The frequencies of prognostic imaging features were not significantly different between the two MRIs (p = .07 to >.99). Non-smooth tumour margin (ECA-MRI, odds ratio [OR] = 5.30; HBA-MRI, OR = 7.07) and peritumoral arterial phase hyperenhancement (ECA-MRI, OR = 4.26; HBA-MRI, OR = 4.43) were independent factors significantly associated with MVI on both MRIs. Two-trait predictor of venous invasion (presence of internal arteries and absence of hypoattenuating halo) on ECA-MRI (OR = 11.24) and peritumoral HBP hypointensity on HBA-MRI (OR = 20.42) were other predictors of MVI. Early recurrence rates of any two or more significant imaging features (49.8% on ECA-MRI vs 51.3% on HBA-MRI, p = .75) were not significantly different between the two MRIs. Prognostic imaging features of HCC may be comparable between ECA-MRI and HBA-MRI.

Comparison of the clinicoradiological features and outcomes of lymphoepithelioma-like cholangiocarcinoma and conventional intrahepatic cholangiocarcinoma: A propensity score matching analysis

Background/purposeLymphoepithelioma-like cholangiocarcinoma (LELCC) is a rare variant of intrahepatic cholangiocarcinoma (ICC). We aim to analyze the differences in the clinical and radiological features of LELCC and ICC. MethodsBetween January 2003 and December 2019, a total of 21 patients diagnosed with LELCC were retrospectively enrolled, and 84 patients with ICC were selected through propensity score matching by sex, age, and initial cancer stage. The clinical characteristics, pathological findings, and radiological features were analyzed. The differences in overall survival (OS) between LELCC and ICC were evaluated using the Kaplan-Meier method. ResultsThe serum carbohydrate antigen 19-9 (CA 19-9) level was higher in the ICC group than in the LELCC group (77.9 vs 30.0 U/mL, p = 0.004). Non-rim arterial phase hyperenhancement (47.1% vs 13.7%, p = 0.005) and portovenous washout (35.3% vs 4.1%, p = 0.001) were more frequently observed in the LELCC group than in the ICC group. Intrahepatic duct dilatation was a distinct feature of the ICC group. The 5-year OS rates in the LELCC and ICC groups were 69.3% and 58.2%, respectively (p = 0.047). The 5-year OS of patients with stages I and II LELCC between ICC were not significantly different (90.0% vs 83.4%, p = 0.464). However, the 5-year OS of patients with stages III and IV LELCC was more favorable than that of patients with ICC (29.2% vs 23.0%, p = 0.017). ConclusionsLELCC had a favorable outcome and several different clinicoradiological features compared with ICC.

Preoperative and postoperative MRI-based models versus clinical staging systems for predicting early recurrence in hepatocellular carcinoma

BackgroundTo predict the early recurrence of HCC patients who received radical resection using preoperative variables based on Gd-EOB-DTPA enhanced MRI, followed by the comparison with the postoperative model and clinical staging systems. MethodsOne hundred and twenty-nine HCC patients who received radical resection were categorized into the early recurrence group (n = 48) and the early recurrence-free group (n = 81). Through COX regression analysis, statistically significant variables of laboratory, pathologic, and Gd-EOB-DTPA enhanced MRI results were identified. The preoperative and postoperative models were established to predict early recurrence, and the prognostic performances and differences were compared between the two models and clinical staging systems. ResultsSix variables were incorporated into the preoperative model, including alpha-fetoprotein (AFP) level, aspartate aminotransferase/platelet ratio index (APRI), rim arterial phase hyperenhancement (rim APHE), peritumoral hypointensity on hepatobiliary phase (HBP), CERHBP (tumor-to-liver SI ratio on hepatobiliary phase imaging), and ADC value. Moreover, the postoperative model was developed by adding microvascular invasion (MVI) and histological grade. The C-index of the preoperative model and postoperative model were 0.889 and 0.901 (p = 0.211) respectively. Using receiver operating characteristic curve analysis (ROC) and decision curve analysis (DCA), it was determined that the innovative models we developed had superior predictive capabilities for early recurrence in comparison to current clinical staging systems. HCC patients who received radical resection were stratified into low-, medium-, and high-risk groups on the basis of the preoperative and postoperative models. ConclusionThe preoperative and postoperative MRI-based models built in this study were more competent compared with clinical staging systems to predict the early recurrence in hepatocellular carcinoma.

Intrahepatic cholangiocarcinoma with arterial phase hyperenhancement and specialized tumor microenvironment associated with good prognosis after radical resection: A single-center retrospective study

BackgroundThis single-center retrospective study aimed to clarify the clinical and pathologic background of mass-forming intrahepatic cholangiocarcinomas. MethodsA total of 53 patients with mass-forming intrahepatic cholangiocarcinomas were selected from 2007 to 2021 and analyzed based on several parameters, including the preoperative computed tomography pattern (enhancement in the arterial phase of dynamic contrast-enhanced computed tomography), clinical data, and tumor microenvironment evaluated by immunohistochemistry. The hyperenhancement (n = 13) and hypoenhancement (n = 40) groups were defined using the 50% cutoff of tumors with higher attenuation than the liver parenchyma. ResultsThe hyperenhancement group was characterized by a better overall survival than the hypoenhancement group (5-year survival: 86% vs 27%, respectively; P < .001) and by a higher infiltration of peritumoral (92% vs 58%; P = .020) and intratumoral CD3-positive T lymphocytes (85% vs 35%; P = .002). Conversely, the hypoenhancement group was characterized by a higher infiltration versus peritumoral CD163-positive tumor-associated macrophages (60% vs 8%; P = .001), peritumoral pentraxin 3–positive tumor-associated macrophages (50% vs 15%; P = .024), and intratumoral α–smooth muscle actin–positive cancer-associated fibroblasts (15% vs 68%; P = .001). A multiple regression analysis was performed to predict overall survival from the microenvironment, and the independent poor predictor factors were low intratumoral CD3-positive T lymphocytes (hazard ratio = 2.75), high peritumoral (hazard ratio = 2.38), and intratumoral CD163-positive tumor-associated macrophages (hazard ratio = 2.81) (all P values < 0.05). ConclusionCompared with hypovascular, hypervascular mass-forming intrahepatic cholangiocarcinomas have better tumor immunity and prognosis.

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Correlation between CEUS LI-RADS categorization of HCC < 20 mm and clinic-pathological features

ObjectiveTo retrospectively evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) LI-RADS in liver nodules < 20 mm at high risk of hepatocellular carcinoma (HCC) and their correlation with clinic-pathological features.MethodsA total of 432 pathologically proved liver nodules < 20 mm were included from January 2019 to June 2022. Each nodule was categorized as LI-RADS grade (LR)-1 to LR-5 through LR-M according to CEUS LI-RADS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of CEUS LI-RADS were evaluated using pathological reference standard. Correlations between clinic-pathological features and CEUS LI-RADS categorization, together with major CEUS features, were further explored.ResultsWith LR-5 to diagnose HCC, the sensitivity, specificity, PPV, NPV, and AUC were 50.3%, 70.0%, 91.2%, 18.5%, and 0.601, respectively. The proportion of LR-5 in primary HCCs was significantly higher than that in recurrent ones (p = 0.014). HCC 10–19 mm showed significantly more frequent arterial phase hyper-enhancement (APHE) and late washout (p < 0.05) and less no-washout (p = 0.003) compared with those in HCC < 10 mm. Well-differentiated HCCs showed more frequent non-APHE and no-washout than moderate- and poor-differentiated HCCs (p < 0.05). Upgrading “APHE without washout” LR-4 nodules 10–19 mm with HCC history and “APHE with late mild washout” LR-4 nodules < 10 mm to LR-5 could improve the diagnostic performance of LR-5. The corresponding sensitivity, specificity, PPV, NPV, and AUC are 60.2%, 70.0%, 92.6%, 22.1%, and 0.651, respectively.ConclusionsCEUS LI-RADS is valuable in the diagnosis of HCC < 20 mm and performance can be improved with the combination of clinic-pathological features.Critical relevance statementCEUS LI-RADS was valuable in the diagnosis of HCC < 20 mm and its diagnostic performance can be improved by combining clinic-pathological features. Further research is needed to define its value in this set of lesions.Key PointsContrast-enhanced ultrasound can detect small liver lesions where LI-RADS accuracy is uncertain.Many LI-RADS Grade-4 nodules were upgraded to Grade-5 by combining imaging with clinic-pathological factors.The reclassification of LI-RADS Grade-5 can improve sensitivity without decreasing positive predictive value.Graphical

Clinical and Radiologic Predictors of Response to Atezolizumab-Bevacizumab in Advanced Hepatocellular Carcinoma.

This study aimed to identify clinical and radiologic characteristics that could predict response to atezolizumab-bevacizumab combination therapy in patients with advanced hepatocellular carcinoma (HCC). This single-center retrospective study included 108 advanced HCC patients with intrahepatic lesions who were treated with atezolizumab-bevacizumab. Two radiologists independently analyzed imaging characteristics of the index tumor on pretreatment computed tomography. Predictive factors associated with progressive disease (PD) at the best response based on Response Evaluation Criteria in Solid Tumors, ver. 1.1 were evaluated using logistic regression analysis. Progression-free survival (PFS) was estimated by the Kaplan-Meier method and compared with the log-rank test. Of 108 patients with a median PFS of 15 weeks, 40 (37.0%) had PD during treatment. Factors associated with PD included the presence of extrahepatic metastases (adjusted odds ratio [aOR], 4.13; 95% confidence interval [CI], 1.19 to 14.35; p=0.03), the infiltrative appearance of the tumor (aOR, 3.07; 95% CI, 1.05 to 8.93; p=0.04), and the absence of arterial-phase hyperenhancement (APHE) (aOR, 6.34; 95% CI, 2.18 to 18.47; p < 0.001). Patients with two or more of these factors had a PD of 66.7% and a median PFS of 8 weeks, indicating a significantly worse outcome compared to the patients with one or no of these factors. In patients with advanced HCC treated with atezolizumab-bevacizumab treatment, the absence of APHE, infiltrative appearance of the intrahepatic tumor, and presence of extrahepatic metastases were associated with poor response and survival. Evaluation of early response may be necessary in patients with these factors.

Using GPT-4 for LI-RADS feature extraction and categorization with multilingual free-text reports.

The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports. Three radiologists generated 160 fictitious free-text liver MRI reports written in Korean and English, simulating real-world practice. Of these, 20 were used for prompt engineering, and 140 formed the internal test cohort. Seventy-two genuine reports, authored by 17 radiologists were collected and de-identified for the external test cohort. LI-RADS features were extracted using GPT-4, with a Python script calculating categories. Accuracies in each test cohort were compared. On the external test, the accuracy for the extraction of major LI-RADS features, which encompass size, nonrim arterial phase hyperenhancement, nonperipheral 'washout', enhancing 'capsule' and threshold growth, ranged from .92 to .99. For the rest of the LI-RADS features, the accuracy ranged from .86 to .97. For the LI-RADS category, the model showed an accuracy of .85 (95% CI: .76, .93). GPT-4 shows promise in extracting LI-RADS features, yet further refinement of its prompting strategy and advancements in its neural network architecture are crucial for reliable use in processing complex real-world MRI reports.

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

Proliferative hepatocellular carcinoma (HCC), aggressive with poor prognosis, and lacks reliable MRI diagnosis. To develop a diagnostic model for proliferative HCC using liver imaging reporting and data system (LI-RADS) and assess its prognostic value. Retrospective. 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). 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. 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. 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. 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). The MR-based model could pre-treatment identify proliferative HCC and assist in prognosis evaluation. Stage 2.

Inter-reader agreement for CT/MRI LI-RADS category M imaging features: a systematic review and meta-analysis.

To systematically evaluate inter-reader agreement in the assessment of individual liver imaging reporting and data system (LI-RADS) category M (LR-M) imaging features in computed tomography/magnetic resonance imaging (CT/MRI) LIRADS v2018, and to explore the causes of poor agreement in LR-M assignment. Original studies reporting inter-reader agreement for LR-M features on multiphasic CT or MRI were identified using the MEDLINE, EMBASE, and Cochrane databases. The pooled kappa coefficient (κ) was calculated using the DerSimonian-Laird random-effects model. Heterogeneity was assessed using Cochran's Q test and I2 statistics. Subgroup meta-regression analyses were conducted to explore the study heterogeneity. In total, 24 eligible studies with 5,163 hepatic observations were included. The pooled κ values were 0.72 (95% confidence interval [CI], 0.65-0.78) for rim arterial phase hyperenhancement, 0.52 (95% CI, 0.39-0.65) for peripheral washout, 0.60 (95% CI, 0.50-0.70) for delayed central enhancement, 0.68 (95% CI, 0.57-0.78) for targetoid restriction, 0.74 (95% CI, 0.65-0.83) for targetoid transitional phase/hepatobiliary phase appearance, 0.64 (95% CI, 0.49-0.78) for infiltrative appearance, 0.49 (95% CI, 0.30-0.68) for marked diffusion restriction, and 0.61 (95% CI, 0.48-0.73) for necrosis or severe ischemia. Substantial study heterogeneity was observed for all LR-M features (Cochran's Q test, P<0.01; I2≥89.2%). Studies with a mean observation size of <3 cm, those performed using 1.5-T MRI, and those with multiple image readers, were significantly associated with poor agreement of LR-M features. The agreement for peripheral washout and marked diffusion restriction was limited. The LI-RADS should focus on improving the agreement of LR-M features.

A Modified Targetoid Feature Emphasizing Thin-Rim APHE to Improve the Diagnostic Performance of LI-RADS for Malignant Hepatic Tumors.

To identify imaging features that help distinguish between HCCs and non-HCC malignancies assigned to LI-RADS M (LR-M) and evaluate the diagnostic performance of a LI-RADS with targetoid criteria using thin-rim arterial phase hyperenhancement (APHE). This retrospective study included 381 patients (387 observations) at high-risk for HCC who underwent enhanced-MRI before surgery. Three radiologists reviewed images for LI-RADS categorization of hepatic observations. Univariate and multivariate analysis was conducted to determine reliable features to differentiate between HCC and non-HCC malignancies among the LR-M lesions. The thin-rim (<30%) APHE was defined based on the thickest thickness of rim APHE compared with the tumor radius, and a modified LI-RADS emphasizing thin-rim APHE as a specific feature of LR-M was established. We compared the diagnostic performance of modified LR-M and LI-RADS 5 (LR-5) with the conventional one. Thin-rim APHE and targetoid diffusion-weighted imaging (DWI) were found as independent predictive factors of non-HCC malignancies, while enhancing capsule, thick-rim APHE and peripheral washout were noted as independent variables significantly associated with HCC of LR-M (P<0.05). The noticeable diagnostic performance of thin-rim APHE in distinguishing non-HCC malignancies from HCCs using the ROC curve. Emphasizing thin-rim APHE on targetoid features, the modified LR-M revealed significantly superior specificity and accuracy (89.4% vs 81.1%, P=0.004; and 87.9% vs 82.2%, P=0.027, respectively) while maintaining high sensitivity (82.2% vs 86.0%; P=0.529) compared with the LR-M. Meanwhile, the modified LR-5 achieved greater sensitivity and accuracy (88.6% vs 79.7%, P=0.004; and 85.8% vs 80.1%, P=0.036, respectively) for diagnosing HCC, without compromising specificity (78.3% vs.81.1%; P=0.608) compared with the LR-5. Thin-rim APHE may be the specific imaging feature for differentiating non-HCC malignancies from HCCs within LR-M. The modified targetoid criteria emphasizing thin-rim APHE can improve the diagnostic performance of LI-RADS for hepatic malignancies.