Histopathological components correlated with MRI features and prognosis in combined hepatocellular carcinoma-cholangiocarcinoma

Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging

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.

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study

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.

Revealing the role of necroptosis microenvironment: FCGBP + tumor-associated macrophages drive primary liver cancer differentiation towards cHCC-CCA or iCCA

Previous research has demonstrated that the conversion of hepatocellular carcinoma (HCC) to intrahepatic cholangiocarcinoma (iCCA) can be stimulated by manipulating the tumor microenvironment linked with necroptosis. However, the specific cells regulating the necroptosis microenvironment have not yet been identified. Additionally, further inquiry into the mechanism of how the tumor microenvironment regulates necroptosis and its impact on primary liver cancer(PLC) progression may be beneficial for precision therapy. We recruited a single-cell RNA sequencing dataset (scRNA-seq) with 34 samples from 4 HCC patients and 3 iCCA patients, and a Spatial Transcriptomic (ST) dataset including one each of HCC, iCCA, and combined hepatocellular-cholangiocarcinoma (cHCC-CCA). Quality control, dimensionality reduction and clustering were based on Seurat software (v4.2.2) process and batch effects were removed by harmony (v0.1.1) software. The pseudotime analysis (also known as cell trajectory) in the single cell dataset was performed by monocle2 software (v2.24.0). Calculation of necroptosis fraction was performed by AUCell (v1.16.0) software. Switch gene analysis was performed by geneSwitches(v0.1.0) software. Dimensionality reduction, clustering, and spatial image in ST dataset were performed by Seurat (v4.0.2). Tumor cell identification, tumor subtype characterization, and cell type deconvolution in spot were performed by SpaCET (v1.0.0) software. Immunofluorescence and immunohistochemistry experiments were used to prove our conclusions. Analysis of intercellular communication was performed using CellChat software (v1.4.0). ScRNA-seq analysis of HCC and iCCA revealed that necroptosis predominantly occurred in the myeloid cell subset, particularly in FCGBP + SPP1 + tumor-associated macrophages (TAMs), which had the highest likelihood of undergoing necroptosis. The existence of macrophages undergoing necroptosis cell death was further confirmed by immunofluorescence. Regions of HCC with poor differentiation, cHCC-CCA with more cholangiocarcinoma features, and the tumor region of iCCA shared spatial colocalization with FCGBP + macrophages, as confirmed by spatial transcriptomics, immunohistochemistry and immunofluorescence. Pseudotime analysis showed that premalignant cells could progress into two directions, one towards HCC and the other towards iCCA and cHCC-CCA. Immunofluorescence and immunohistochemistry experiments demonstrated that the number of macrophages undergoing necroptosis in cHCC-CCA was higher than in iCCA and HCC, the number of macrophages undergoing necroptosis in cHCC-CCA with cholangiocarcinoma features was more than in cHCC-CCA with hepatocellular carcinoma features. Further investigation showed that myeloid cells with the highest necroptosis score were derived from the HCC_4 case, which had a severe inflammatory background on pathological histology and was likely to progress towards iCCA and cHCC-CCA. Switchgene analysis indicated that S100A6 may play a significant role in the progression of premalignant cells towards iCCA and cHCC-CCA. Immunohistochemistry confirmed the expression of S100A6 in PLC, the more severe inflammatory background of the tumor area, the more cholangiocellular carcinoma features of the tumor area, S100A6 expression was higher. The emergence of necroptosis microenvironment was found to be significantly associated with FCGBP + SPP1 + TAMs in PLC. In the presence of necroptosis microenvironment, premalignant cells appeared to transform into iCCA or cHCC-CCA. In contrast, without a necroptosis microenvironment, premalignant cells tended to develop into HCC, exhibiting amplified stemness-related genes (SRGs) and heightened malignancy.

Component prediction in combined hepatocellular carcinoma-cholangiocarcinoma: habitat imaging and its biologic underpinnings

PURPOSE

To construct an MRI-based habitat imaging model to help predict component percentage in combined hepatocellular carcinoma-cholangiocarcinoma (cHCC-CCA) preoperatively, and investigate the biologic underpinnings of habitat imaging in cHCC-CCA.

METHODS

The study consisted of one retrospective model-building dataset and one prospective validation dataset from two hospitals. All voxels were assigned into different clusters according to the similarity of enhancement pattern by using K-means clustering method, and each habitat's volume fraction in each lesion was calculated. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to select optimal predictors, and then to establish an MRI-based habitat imaging model. R-squared was calculated to evaluate performance of the prediction models. Model performance was also verified in the prospective dataset with RNA sequencing data, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was then applied to investigate the biologic underpinnings of habitat imaging.

RESULTS

A total of 129 patients were enrolled (mean age, 56.1 ± 10.4, 102 man), among which 104 patients were in the retrospective model-building set, while 25 patients in the prospective validation set. Three habitats, habitat1 (HCC-alike habitat), habitat2 (iCCA-alike habitat), and habitat3 (in-between habitat), were identified. Habitat 1's volume fraction, habitat 3's volume fraction, nonrim APHE, nonperipheral washout, and LI-RADS categorization were selected to develop an HCC percentage prediction model with R-squared of 0.611 in the model-building set and 0.541 in the validation set. Habitat 1's volume fraction was correlated with genes involved in regulation of actin cytoskeleton and Rap1 signaling pathway, which regulate cell migration and tumor metastasis.

CONCLUSION

Preoperative prediction of HCC percentage in patients with cHCC-CCA was achieved using an MRI-based habitat imaging model, which may correlate with signaling pathways regulating cell migration and tumor metastasis.

A comprehensive analysis of the potential role of necroptosis in hepatocellular carcinoma using single-cell RNA Seq and bulk RNA Seq

PURPOSE

Necroptosis plays an essential role in oncogenesis and tumor progression in hepatocellular carcinoma (HCC). This study aimed to investigate the role of necroptosis in the development and progression of HCC. Specifically, we constructed a prognostic prediction model using necroptosis-associated genes (NAGs) to predict patient outcomes.

METHODS

Using data from The Cancer Genome Atlas (TCGA) database, we analyzed gene expression and clinical data. We identified a 5-gene model associated with NAGs and explored genetic features and immune cell infiltration using the CIBERSORT algorithm. In addition, we conducted single-cell RNA sequencing to investigate the potential role of necroptosis in HCC.

RESULTS

We constructed a 5-gene prognostic model based on NAGs that demonstrated excellent predictive accuracy in both training and validation sets. Using multifactorial cox regression analysis, we confirmed the risk score derived from the model as an independent predictor of prognosis, surpassing other clinical characteristics. Patients with high risk scores had significantly worse prognosis than those with low risk scores. To enhance the clinical utility of the necroptosis score, we constructed an accurate nomogram. Additionally, we compared metabolic pathway and immune microenvironment differences between HCC tumors with high and low risk scores. Our single-cell RNA sequencing analyses revealed that necroptosis in HCC was primarily associated with a specific subset of macrophages.

CONCLUSIONS

Our study revealed the presence of two distinct necroptosis subtypes in HCC and developed a robust prognostic model with exceptional predictive accuracy. We observed significantly higher infiltration of M0 macrophages in the high-risk group. We propose that rescuing cytochrome c metabolism in HCC could serve as a potential therapeutic strategy. Furthermore, at a single-cell resolution, our analysis identified myeloid cells as the primary cells exhibiting necroptosis. Specifically, macrophages expressing CD5L, CETP, and MARCO, which may belong to a subset of tissue-resident macrophages, were found to be highly susceptible to necroptosis. These findings suggest the involvement of this specific macrophage subset in potential antitumor therapies. Our study provides novel insights into predicting patient prognosis and developing personalized therapeutic approaches for HCC.

The significance of the predominant component in combined hepatocellular-cholangiocarcinoma: MRI manifestation and prognostic value

PURPOSE

To investigate the significance of the predominant component of combined hepatocellular-cholangiocarcinoma (cHCC-CC) in terms of MRI manifestation and its potential prognostic value compared to hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC).

MATERIALS AND METHODS

A total of 300 patients with chronic liver disease from two centers were retrospectively enrolled, including 100 surgically proven cases of cHCC-CC, HCC, and ICC each. Univariate and multivariate regression analyses were performed to identify independent predictors for distinguishing HCC-predominant cHCC-CC and ICC-predominant cHCC-CC from HCC and ICC, respectively. Diagnostic models were constructed based on the independent features. Recurrence-free survival (RFS) was estimated and compared between groups.

RESULTS

The predominant component was an independent predictor for RFS in cHCC-CC (hazard ratio = 1.957, P = 0.044). The presence of targetoid appearance (odds ratio(OR) = 10.907, P = 0.001), lack of enhancing capsule (OR = 0.072, P = 0.001) and arterial peritumoral enhancement (OR = 0.091, P = 0.003) were independent predictors suggestive of HCC-predominant cHCC-CC over HCC; their combination yielded an area under the curve of 0.756. No significant differences were observed in RFS between HCC-predominant cHCC-CC and HCC (P = 0.864). Male gender (OR = 4.049, P = 0.015), higher alpha fetoprotein (OR = 16.789, P < 0.001) and normal carbohydrate antigen 19-9 (OR = 0.343, P = 0.036) levels, presence of enhancing capsule (OR = 7.819, P < 0.001) and hemorrhage (OR = 23.526, P = 0.004), and lack of targetoid appearance (OR = 0.129, P = 0.005) and liver surface retraction (OR = 0.190, P = 0.021) were independent predictors suggestive of ICC-predominant cHCC-CC over ICC; their combination yielded an area under the curve value of 0.898. ICC-predominant cHCC-CC exhibited poorer survival with shorter RFS than ICC (P = 0.009).

CONCLUSION

The predominant histopathological component is closely related to the imaging manifestation of cHCC-CC; and more importantly, it plays a significant prognostic role, which may alter the RFS prognosis of cHCC-CC.

Radiomics Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Preoperative Differentiation of Combined Hepatocellular-Cholangiocarcinoma from Hepatocellular Carcinoma: A Multi-Center Study

Purpose

To explore whether texture features based on magnetic resonance can distinguish diseases combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before operation.

Methods

The clinical baseline data and MRI information of 342 patients with pathologically diagnosed cHCC-CC and HCC in two medical centers were collected. The data were divided into the training set and the test set at a ratio of 7:3. MRI images of tumors were segmented with ITK-SNAP software, and python open-source platform was used for texture analysis. Logistic regression as the base model, mutual information (MI) and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the most favorable features. The clinical, radiomics, and clinic-radiomics model were constructed based on logistic regression. The model's effectiveness was comprehensively evaluated by the receiver operating characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, and Youden index which is the main, and the model results were exported by SHapley Additive exPlanations (SHAP).

Results

A total of 23 features were included. Among all models, the arterial phase-based clinic-radiomics model showed the best performance in differentiating cHCC-CC from HCC before an operation, with the AUC of the test set being 0.863 (95% CI: 0.782 to 0.923), the specificity and sensitivity being 0.918 (95% CI: 0.819 to 0.973) and 0.738 (95% CI: 0.580 to 0.861), respectively. SHAP value results showed that the RMS was the most important feature affecting the model.

Conclusion

Clinic-radiomics model based on DCE-MRI may be useful to distinguish cHCC-CC from HCC in a preoperative setting, especially in the arterial phase, and RMS has the greatest impact.

The Diagnostic Approach towards Combined Hepatocellular-Cholangiocarcinoma-State of the Art and Future Perspectives

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer which displays clinicopathologic features of both hepatocellular (HCC) and cholangiocellular carcinoma (CCA). The similarity to HCC and CCA makes the diagnostic workup particularly challenging. Alpha-fetoprotein (AFP) and carbohydrate antigen 19-9 (CA 19-9) are blood tumour markers related with HCC and CCA, respectively. They can be used as diagnostic markers in cHCC-CCA as well, albeit with low sensitivity. The imaging features of cHCC-CCA overlap with those of HCC and CCA, dependent on the predominant histopathological component. Using the Liver Imaging and Reporting Data System (LI-RADS), as many as half of cHCC-CCAs may be falsely categorised as HCC. This is especially relevant since the diagnosis of HCC may be made without histopathological confirmation in certain cases. Thus, in instances of diagnostic uncertainty (e.g., simultaneous radiological HCC and CCA features, elevation of CA 19-9 and AFP, HCC imaging features and elevated CA 19-9, and vice versa) multiple image-guided core needle biopsies should be performed and analysed by an experienced pathologist. Recent advances in the molecular characterisation of cHCC-CCA, innovative diagnostic approaches (e.g., liquid biopsies) and methods to analyse multiple data points (e.g., clinical, radiological, laboratory, molecular, histopathological features) in an all-encompassing way (e.g., by using artificial intelligence) might help to address some of the existing diagnostic challenges.