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BACKGROUND: Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. METHODS: This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). RESULTS: CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. CONCLUSION: HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.
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BACKGROUND: Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients. AIM: To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients. METHODS: A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model's performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence. RESULTS: Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status. CONCLUSION: Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.
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Objective: To evaluate the usefulness of the apparent diffusion coefficient (ADC) in differentiating between benign and malignant LR-3 lesions classified by Liver Imaging Reporting and Data System 2018 (LI-RADS v2018). Methods: Retrospectively analyzed 88 patients with liver nodules confirmed by pathology and classified as LR-3 by LI-RADS. All patients underwent preoperative contrast-enhanced MR examination, and the following patient-related imaging features were collected: tumor size,nonrim APHE, nonperipheral "washout", enhancing "capsule", mild-moderate T2 hyperintensity, fat in mass, restricted diffusion, and nodule-in-nodule architecture. We performed ROC analysis and calculated the sensitivity and specificity. Results: A total of 122 lesions were found in 88 patients, with 68 benign and 54 malignant lesions. The mean ADC value for malignant and benign lesions were 1.01 ± 0.15 × 103 mm2/s and 1.41 ± 0.31 × 103 mm2/s, respectively. The ADC value of malignant lesions was significantly lower than that of benign lesions, p < 0.0001. Compared with other imaging features, ADC values had the highest AUC (AUC = 0.909), with a sensitivity of 92.6% and a specificity of 74.1% for the differentiation of benign and malignant lesions. Conclusions: ADC values are useful for differentiating between benign and malignant liver nodules in LR-3 classification, it improves the sensitivity of LI-RADS in the diagnosis of HCC while maintaining high specificity, and we recommend including ADC values in the standard interpretation of LI-RADSv2018.
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It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00136-8.
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Purpose: The purpose of this study was to establish a model for predicting early recurrence (≤2 years) of hepatocellular carcinoma (HCC) after anatomical hepatectomy based on the hepatobiliary phase (HBP) imaging characteristics of gadobenate-enhanced MRI. Methods: A total of 155 patients who underwent anatomical hepatectomy HCC therapy and gadobenate-enhanced MRI were included retrospectively. The patients were divided into the early recurrence-free group (n = 103) and the early recurrence group (n = 52). Univariate and multivariate Cox regression analysis was used to determine the independent risk factors related to early recurrence, and four models were established. The preoperative model with/without HBP imaging features (HBP-pre/No HBP-pre model) and the postoperative model with/without HBP imaging features (HBP-post/No HBP-post model). Bootstrap resampling 1,000 times was used to verify the model and displayed by nomograms. The performance of nomograms was evaluated by discrimination, calibration, and clinical utility. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to evaluate the differences between models and to select the optimal model. Results: Shape, arterial peritumoral enhancement, AFP-L3, and peritumoral hypointensity on HBP were identified as independent risk factors. Prothrombin time (PT) and r-glutamyltransferase (GGT) were selected by multivariate Cox regression. These six factors construct the HBP-pre model. Removing peritumoral hypointensity on HBP was the No HBP-pre model. Adding microvascular invasion (MVI) and microscopic capsule factors were the HBP-post and No HBP-post model. The C-index was 0.766, 0.738, 0.770, and 0.742, respectively. The NRI and IDI of the HBP-pre vs. the No HBP-pre model and the HBP-post vs. the No HBP-post model significantly increased 0.258, 0.092, 0.280, and 0.086, respectively. The calibration curve and decision curve analysis (DCA) had good consistency and clinical utility. However, the NRI and IDI of the No HBP-post vs. the No HBP-pre model and the HBP-post vs. the HBP-pre model did not increase significantly. Conclusions: Preoperative gadobenate-enhanced MR HBP imaging features significantly improve the model performance while the postoperative pathological factors do not. Therefore, the HBP-pre model is selected as the optimal model. The strong performance of this model may help hepatologists to assess the risk of recurrence in order to guide the selection of treatment options.