PURPOSE:To assess the differences in early imaging features and progression pattern on CT between intrahepatic biliary metastasis (IBM) and non-mass-forming cholangiocarcinoma (NMFC) in patients with extrabiliary malignancy. METHODS:This retrospective study included 35 patients who were surgically and pathologically confirmed with IBM (n = 14) or NMFC (n = 21) at the time of or after surgery for extrabiliary malignancy. Two observers evaluated the following aspects of biliary lesions on initial or follow-up CT images: location, characteristics of intrahepatic duct (IHD) dilatation, presence of duct wall thickening, and periductal infiltration lesion or periductal expansile mass. RESULTS:All IBMs were associated with colorectal cancer (p = 0.032). As early imaging features on CT, smooth tapered localized IHD dilatation without duct wall thickening and peripheral duct involvement were observed significantly more often in IBM, and IHD dilatation with abrupt tapering or irregularity of transition site and bile duct wall thickening were significantly more common in NMFC (all p < 0.05). Regarding progression pattern, periductal expansile mass was present only in IBM, whereas periductal infiltrative lesion was present only in NMFC (p < 0.001). CONCLUSION:In the differentiation between IBM and NMFC in patients with extrabiliary malignancy, the differences in early imaging features and progression pattern of the two diseases revealed in this study would be helpful for diagnosis.

BACKGROUND:The differential diagnosis of intrahepatic cholangiocarcinomas (iCCAs) from metastatic adenocarcinomas from organs adjacent to the liver (gallbladder, pancreas, and stomach) is difficult due to histopathological similarity and a lack of specific markers. This study aimed to develop a method to differentiate iCCA and adenocarcinomas originated from extrahepatic organs adjacent to the liver. METHODS:We retrospectively enrolled surgically resected iCCA (n = 181) and adenocarcinomas from extrahepatic organs (n = 30, n = 28, and n = 38 from gallbladder, pancreas, and stomach, respectively) between 2007 and 2013. The albumin mRNA in situ hybridization (ISH) and immunohistochemistry (IHC) of filamin-A and cytokeratin 19 (CK19) were performed using tissue microarray. Using logistic regression analysis of three markers, iCCA-score was developed, and its diagnostic performance was evaluated. RESULTS:The iCCAs were more frequently positive for albumin ISH (23.2% vs. 0%), filamin-A IHC (47.5% vs. 12.5%) and CK19 (68.5% vs. 40.6%) than extrahepatic adenocarcinomas (p < 0.001 for all). The iCCA-score consisting of these three markers was developed, and it showed higher diagnostic performance (area under the curve [AUC], 0.798 vs. 0.616, p < 0.001). Taking an iCCA-score of 2 or higher as the threshold for iCCA, the sensitivity was substantially higher than albumin ISH alone (45.9% and 23.2%, respectively; p < 0.001), but maintained high specificity (94.8% and 100%, respectively). CONCLUSION:Albumin ISH and IHC staining for filamin-A and CK19 showed distinct expression patterns between iCCA and extrahepatic adenocarcinomas from gallbladder, pancreas, and stomach. We developed iCCA-score that consisted of those three markers, and it showed better diagnostic performance than albumin ISH alone.

AIMS:Intrahepatic cholangiocarcinoma (iCCA) and hepatocellular carcinoma (HCC) differ in prognosis and treatment. We aimed to non-invasively differentiate iCCA and HCC by means of radiomics extracted from contrast-enhanced standard-of-care computed tomography (CT). MATERIALS AND METHODS:In total, 94 patients (male, n = 68, mean age 63.3 ± 12.4 years) with histologically confirmed iCCA (n = 47) or HCC (n = 47) who underwent contrast-enhanced abdominal CT between August 2014 and November 2021 were retrospectively included. The enhancing tumour border was manually segmented in a clinically feasible way by defining three three-dimensional volumes of interest per tumour. Radiomics features were extracted. Intraclass correlation analysis and Pearson metrics were used to stratify robust and non-redundant features with further feature reduction by LASSO (least absolute shrinkage and selection operator). Independent training and testing datasets were used to build four different machine learning models. Performance metrics and feature importance values were computed to increase the models' interpretability. RESULTS:The patient population was split into 65 patients for training (iCCA, n = 32) and 29 patients for testing (iCCA, n = 15). A final combined feature set of three radiomics features and the clinical features age and sex revealed a top test model performance of receiver operating characteristic (ROC) area under the curve (AUC) = 0.82 (95% confidence interval =0.66-0.98; train ROC AUC = 0.82) using a logistic regression classifier. The model was well calibrated, and the Youden J Index suggested an optimal cut-off of 0.501 to discriminate between iCCA and HCC with a sensitivity of 0.733 and a specificity of 0.857. CONCLUSIONS:Radiomics-based imaging biomarkers can potentially help to non-invasively discriminate between iCCA and HCC.

CD133 is widely used as a marker to isolate tumor-initiating cells in many types of cancers. The structure of N-glycan on CD133 is altered during the differentiation of tumor-initiating cells. However, the relationship between CD133 N-glycosylation and stem cell characteristics remains elusive. Here, we found that the level of α-1,2-mannosylated CD133 was associated with the level of stemness genes in intrahepatic cholangiocarcinoma (iCCA) tissues. α-1,2-mannosylated CD133 cells possessed the characteristics of tumor-initiating cells. The loss of the Golgi α-mannosidase I coding gene MAN1C1 resulted in the formation of α-1,2-mannosylated CD133 in iCCA-initiating cells. Mechanistically, α-1,2-mannosylation promoted the cytoplasmic distribution of CD133 and enhanced the interaction between CD133 and the autophagy gene FIP200, subsequently promoting the tumorigenesis of α-1,2-mannosylated CD133 cells. Analysis of iCCA samples showed that the level of cytoplasmic CD133 was associated with poor iCCA prognosis. Collectively, α-1,2-mannosylated CD133 is a functional marker of iCCA-initiating cells.

OBJECTIVE:This study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS:The clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set ( = 149), test set ( = 38), and independent validation set ( = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy. RESULTS:The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC ( < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability ( < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806-0.989), 0.900, 0.857, 0.868, and 0.874 (0.733-0.961), 0.889, 0.867, and 0.872, respectively. CONCLUSION:The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.

BACKGROUND:Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor, which poses a serious threat to human health. Histone 3 lysine 9 trimethylation (H3K9me3) is a post-translational modification involved in regulating a broad range of biological processes and has been considered as potential therapeutic target in types of cancer. However, there is limited research on investigating profiles of histone modification H3K9me3 in ICC patients. METHODS:In this study, we applied the ChIP-seq technique to investigate the effect of H3K9me3 on ICC. Anti-H3K9me3 antibody was used for ChIP-seq in ICC (RBE cell lines) and HIBEpic (normal cell lines). MACS2 (peak-calling tools) was then used to identify the peaks recorded in RBE and HIBEpic cell lines. Gene expression, mutation and clinical data were downloaded from TCGA and cBioPortal databases. RESULTS:H3K9me3 exhibited abnormal methylation and influenced the process of abnormal gene expression in patients suffering from ICC. The Wnt/β-Catenin signaling pathway (also known as simply the WNT signaling pathway) was enriched in H3K9me3-regulated genes. CONCLUSIONS:We are the first to report that H3K9me3 may play an important role in the progression of ICC. It promotes the understanding of epigenetic molecular mechanisms for ICC.

Objectives:To establish a nomogram based on preoperative laboratory study variables using least absolute shrinkage and selection operator (LASSO) regression for differentiating combined hepatocellular cholangiocarcinoma (cHCC) from intrahepatic cholangiocarcinoma (iCCA). Methods:We performed a retrospective analysis of iCCA and cHCC patients who underwent liver resection. Blood signatures were established using LASSO regression, and then, the clinical risk factors based on the multivariate logistic regression and blood signatures were combined to establish a nomogram for a differential preoperative diagnosis between iCCA and cHCC. The differential accuracy ability of the nomogram was determined by Harrell's index (C-index) and decision curve analysis, and the results were validated using a validation set. Furthermore, patients were categorized into two groups according to the optimal cut-off values of the nomogram-based scores, and their survival differences were assessed using Kaplan-Meier curves. Results:A total of 587 patients who underwent curative liver resection for iCCA or cHCC between January 2008 and December 2017 at West China Hospital were enrolled in this study. The cHCC score was based on the personalized levels of the seven laboratory study variables. On multivariate logistic analysis, the independent factors for distinguishing cHCC were age, sex, biliary duct stones, and portal hypertension, all of which were incorporated into the nomogram combined with the cHCC-score. The nomogram had a good discriminating capability, with a C-index of 0.796 (95% CI, 0.752-0.840). The calibration plot for distinguishing cHCC from iCCA showed optimal agreement between the nomogram prediction and actual observation in the training and validation sets. The decision curves indicated significant clinical usefulness. Conclusion:The nomogram showed good accuracy for the differential diagnosis between iCCA and cHCC preoperatively, and therapeutic decisions would improve if it was applied in clinical practice.

Cholangiocarcinoma is a rare tumor but a challenging cancer in terms of pathological changes, clinical manifestations and therapeutic options. Recent studies have provided evidence for participation of non-coding RNAs in the carcinogenic process of cholangiocarcinoma. We demonstrate the role of long non-coding RNAs, microRNAs and circular RNAs in the pathogenesis of cholangiocarcinoma and highlight their significant position as therapeutic targets and biomarkers for this type of cancer. We also list a number of molecular axes comprising these non-coding RNAs that represent potential targets for therapeutic options in cholangiocarcinoma, based on their significant roles in the regulation of cell proliferation, differentiation and apoptosis of these cells.

PURPOSE:Intrahepatic cholangiocarcinoma (ICC) can invade and metastasize. EIF5A2 is involved in the invasive metastatic process of several digestive malignancies. However, its role in ICC is yet to be elucidated. METHODS:Immunohistochemistry (IHC) and Western blot (WB) were used to detect the level of EIF5A2 in the tumor specimens of ICC patients and evaluate the correlation between its expression and clinicopathological characteristics. The significance of EIF5A2 in the prognosis of ICC patients was further evaluated by Kaplan-Meier and Cox regression analysis. In addition, CCK-8, EdU, Transwell invasion, and scratch assays were utilized to detect tumor cell proliferation, invasion, and metastasis. Furthermore, the role of EIF5A2 in ICC cells was evaluated after modification of EIF5A2 expression. RESULTS:The level of EIF5A2 protein was significantly higher in ICC than in adjacent tissues. This high expression in the tumor samples was significantly associated with malignant phenotypes, such as lymph node metastasis (LNM), microvascular or bile duct invasion, and poor differentiation. ICC patients with high expression of EIF5A2 had short overall survival and a high cumulative recurrence rate. The multifactorial analysis showed that EIF5A2 is an independent prognostic marker. Furthermore, high levels of EIF5A2 may activate the PI3K/AKT/mTOR signaling pathway and upregulate Cyclin D1, Cyclin D3, MMP2, and MMP9 to promote ICC cell proliferation, migration, and invasion. CONCLUSION:The current study found that EIF5A2 promotes ICC progression and is a prognostic biomarker and candidate therapeutic target for ICC patients.

Kidney‑type glutaminase (GLS1) plays a significant role in tumor metabolism. Our recent studies demonstrated that GLS1 was aberrantly expressed in hepatocellular carcinoma (HCC) and facilitated tumor progression. However, the roles of GLS1 in intrahepatic cholangiocarcinoma (ICC) remain largely unknown. Thus, the aim of this study was to evaluate the expression and clinical significance of GLS1 in ICC. For this purpose, combined data from the Oncomine database with those of immunohistochemistry were used to determine the expression levels of GLS1 in cancerous and non‑cancerous tissues. Second, a wound‑healing assay and Transwell assay were used to observe the effects of the knockdown and overexpression of GLS1 on the invasion and migration of ICC cells. We examined the associations between the expression of GLS1 and epithelial‑mesenchymal transition (EMT)‑related markers by western blot analysis. Finally, we examined the associations between GLS1 levels and clinicopathological factors or patient prognosis. The results revealed that GLS1 was overexpressed in different digestive system tumors, including ICC, and that GLS1 expression in ICC tissue was higher than that in peritumoral tissue. The overexpression of GLS1 in RBE cells induced metastasis and invasion. Moreover, the EMT‑related markers, E‑cadherin and Vimentin, were regulated by GLS1 in ICC cells. By contrast, the knockdown of GLS1 expression in QBC939 cells yielded opposite results. Clinically, a high expression of GLS1 in ICC samples negatively correlated with E‑cadherin expression and positively correlated with Vimentin expression. GLS1 protein expression was associated with tumor differentiation (P=0.001) and lymphatic metastasis (P=0.029). Importantly, patients with a high GLS1 expression had a poorer overall survival (OS) and a shorter time to recurrence than patients with a low GLS1 expression. Multivariate analysis indicated that GLS1 expression was an independent prognostic indicator. On the whole, the findings of this study demonstrated that GLS1 is an independent prognostic biomarker of ICC. GLS1 facilitates ICC progression and may thus prove to be a therapeutic target in ICC.

BACKGROUND:Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. METHODS:We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier. FINDINGS:On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. INTERPRETATION:We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. FUNDING:This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

BACKGROUND:The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC. MATERIALS AND METHODS:We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models. RESULTS:With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736-0.913 [accuracy = 0.735-0.912], 0.602-0.828 [accuracy = 0.647-0.818], and 0.638-0.845 [accuracy = 0.618-0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers. CONCLUSIONS:The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.

BACKGROUND:Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are the most prevalent histologic types of primary liver cancer. HCC and ICC differ in treatment and prognosis, warranting an effective differential diagnosis between them. This study aimed to explore the clinical value of mean platelet volume (MPV) to discriminate between HCC and ICC. MATERIAL/METHODS:We performed a retrospective analysis of ICC and HCC patients who were from the Harbin Medical University Cancer Hospital, China. Logistic regression analysis was used to identify the independent factors for the differentiation of HCC and ICC. A receiver operating characteristic curve was built to evaluate the diagnostic performance of the potential model. An independent validation study was performed to validate the diagnostic ability. RESULTS:ICC patients were detected in 146 out of 348 patients in the primary cohort. MPV levels were decreased in ICC patients compared with those in HCC patients. Logistic regression analysis revealed that MPV was an independent factor in distinguishing HCC from ICC. A combination of sex, hepatitis B surface antigen, MPV, alpha-fetoprotein, and carbohydrate antigen 19-9 demonstrated a good capability to differentiate HCC from ICC. Similar results were achieved in the validation cohort. CONCLUSIONS:MPV may be a new marker to help distinguish ICC from HCC. Further validation studies are required.

BACKGROUND:We aimed to investigate whether there is a difference between intrahepatic cholangiocarcinoma (IHCC) and liver metastases of gastrointestinal system (GIS) adenocarcinoma in terms of apparent diffusion coefficient (ADC) values. PATIENTS AND METHODS:From January 2018 to January 2020, we retrospectively examined 64 consecutive patients with liver metastases due to gastrointestinal system adenocarcinomas and 13 consecutive IHCC in our hospital's medical records. After exclusions, fifty-three patients with 53 liver metastases and 10 IHCC were included in our study. We divided the patients into two groups as IHCC and liver metastases of GIS adenocarcinoma. For mean apparent diffusion coefficient (ADC) values, the region of interests (ROI) was placed in solid portions of the lesions. ADC values of groups were compared. RESULTS:The mean age of IHCC group was 62.50 ± 13.49 and mean age of metastases group was 61.15 ± 9.18. ADC values were significantly higher in the IHCC group compared to the metastatic group (p < 0.001). ROC curves method showed high diagnostic accuracy (AUC = 0.879) with cut-off value of < 1178 x 10 mm/s for ADC (Sensitivity = 90.57, Specificity = 70.0, positive predictive value [PPV] = 94.1, negative predictive value [NPV] = 58.3) in differentiating adenocarcinoma metastases from IHCC. CONCLUSIONS:The present study results suggest that ADC values have a potential role for differentiation between IHCC and GIS adenocarcinoma liver metastases which may be valuable for patient management.

BACKGROUND:Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis. AIM:To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration. METHODS:We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene. RESULTS:A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: . The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC. CONCLUSION: can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.

Hepatocellular carcinoma and intrahepatic cholangiocarcinoma are the two most common primary malignant tumors of the liver. The similarities and variations in imaging characteristics that may aid in distinguishing between these two primary tumors will be discussed and outlined in this review. Knowledge of imaging techniques that are currently available would assist in the differentiation between these primary malignancies.