Single-cell RNA-sequencing atlas reveals an FABP1-dependent immunosuppressive environment in hepatocellular carcinoma.

BACKGROUND: Single-cell RNA sequencing, also known as scRNA-seq, is a method profiling cell populations on an individual cell basis. It is particularly useful for more deeply understanding cell behavior in a complicated tumor microenvironment. Although several previous studies have examined scRNA-seq for hepatocellular carcinoma (HCC) tissues, no one has tested and analyzed HCC with different stages. METHODS: In this investigation, immune cells isolated from surrounding normal tissues and cancer tissues from 3 II-stage and 4 III-stage HCC cases were subjected to deep scRNA-seq. The analysis included 15 samples. We distinguished developmentally relevant trajectories, unique immune cell subtypes, and enriched pathways regarding differential genes. Western blot and co-immunoprecipitation were performed to demonstrate the interaction between fatty acid binding protein 1 (FABP1) and peroxisome proliferator-activated receptor gamma(PPARG). In vivo experiments were performed in a C57BL/6 mouse model of HCC established via subcutaneous injection. RESULTS: FABP1 was discovered to be overexpressed in tumor-associated macrophages (TAMs) with III-stage HCC tissues compared with II-stage HCC tissues. This finding was fully supported by immunofluorescence detection in significant amounts of HCC human samples. FABP1 deficiency in TAMs inhibited HCC progression in vitro. Mechanistically, FABP1 interacted with PPARG/CD36 in TAMs to increase fatty acid oxidation in HCC. When compared with C57BL/6 mice of the wild type, tumors in FABP1-/- mice consistently showed attenuation. The FABP1-/- group's relative proportion of regulatory T cells and natural killer cells showed a downward trend, while dendritic cells, M1 macrophages, and B cells showed an upward trend, according to the results of mass cytometry. In further clinical translation, we found that orlistat significantly inhibited FABP1 activity, while the combination of anti-programmed cell death 1(PD-1) could synergistically treat HCC progression. Liposomes loaded with orlistat and connected with IR780 probe could further enhance the therapeutic effect of orlistat and visualize drug metabolism in vivo. CONCLUSIONS: ScRNA-seq atlas revealed an FABP1-dependent immunosuppressive environment in HCC. Orlistat significantly inhibited FABP1 activity, while the combination of anti-PD-1 could synergistically treat HCC progression. This study identified new treatment targets and strategies for HCC progression, contributing to patients with advanced HCC from new perspectives.

Translating imaging traits of mass-forming intrahepatic cholangiocarcinoma into the clinic: From prognostic to therapeutic insights.

Background & Aims: The progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour underlying imaging traits. We developed and validated imaging-based prognostic systems for resected MICCs with an appraisal of the tumour immune microenvironment (TIME) underpinning patient-specific imaging traits. Methods: Between January 2009 and December 2019, a total of 322 patients who underwent dynamic computed tomography/magnetic resonance imaging and curative-intent resection for MICC at three hepatobiliary institutions were retrospectively recruited, divided into training (n = 193) and validation (n = 129) datasets. Two radiological and clinical scoring (RACS) systems, one integrating preoperative variables and one integrating preoperative and postoperative variables, were developed using Cox regression analysis. We then prospectively analysed the TIME of tissue samples from 20 patients who met study criteria from January 2021 to December 2021 using multiplexed immunofluorescence. Results: Preoperative and postoperative MICC-RACS systems built on carbohydrate antigen 19-9, albumin, tumour number, radiological/pathological nodal status, pathological necrosis, and three radiological traits (arterial enhancement pattern, tumour boundary, and capsular retraction) demonstrated good performance in predicting disease-specific (C-statistic >0.80) and disease-free (C-statistic >0.75) survival that outperformed rival models and staging systems across study cohorts (P <0.05 for all). Patients with MICC-RACS score of 0-2 (low risk), 3-5 (medium risk), and ≥6 (high risk) had incrementally worse prognosis after surgery. Significant differences in spatial distribution and infiltration level of immune cells were identified between arterial enhancement patterns. Enhanced infiltration of immunosuppressive regulatory T cells and M2-like macrophages at the invasive margin were noted in tumours with distinct boundary and capsular retraction, respectively. Conclusions: Our MICC-RACS systems are simple but powerful prognostic tools that may facilitate the understanding of spatially distinct TIMEs and patient-tailored immunotherapy approach. Impact and Implications: The progress toward clinical translation of imaging biomarkers for mass-forming intrahepatic cholangiocarcinoma (MICC) is slower than anticipated. Questions remain on the biologic behaviour of MICC underlying imaging traits. In this study, we proposed novel and easy-to-use tools, built on radiological and clinical features, that demonstrated good performance in predicting the prognosis either before or after surgery and outperformed rival models/systems across major imaging modalities. The characteristic radiological traits integrated into prognostic systems (arterial enhancement pattern, tumour boundary, and capsular retraction) were highly correlated with heterogeneous tumour-immune microenvironments, thereby renovating treatment paradigms for this difficult-to-treat disease.

CT-based clinico-radiological nomograms for prognosis prediction in patients with intrahepatic mass-forming cholangiocarcinoma: a multi-institutional study.

OBJECTIVES: To establish prognostic nomograms based on CT imaging features for predicting the prognosis in patients with intrahepatic mass-forming cholangiocarcinoma (IMCC) before and after surgery. METHODS: Two models were established for overall survival (OS) prediction in a training set (179 IMCC patients underwent surgery at institution 1 from 2009 to 2019): imaging-based nomogram included imaging features and clinical characteristics acquired before surgery; postoperative nomogram included imaging-based score, equal to the linear predictor of the imaging-based nomogram, and pathological parameters. Both prognostic nomograms were validated in an independent external dataset (103 IMCC patients received surgical treatment at two independent institutions from 2009 to 2019). Predictive performance and discrimination were evaluated and compared with the common prognostic models. RESULTS: The imaging-based nomogram was developed according to preoperative serum carbohydrate antigen 19-9 and four imaging features including multiple nodules, arterial enhancement pattern, CT-reported lymph node (LN) metastasis, and capsular retraction; the postoperative nomogram was built based on the imaging-based score and three pathological parameters including tumor differentiation grade, capsular invasion, and LN status. Both nomograms presented improved prognostic performance and discrimination (concordance index, 0.770-0.812; integrated Brier score, 0.120-0.138) compared with the common prognostic models in the training and external validation datasets. Besides, the nomograms stratified IMCC patients into two risk strata for OS. CONCLUSIONS: Nomograms based on CT imaging features can provide accurate individual survival prediction for IMCC patients before and after surgery, which may help to improve personalized treatment. KEY POINTS: • Imaging features including multiple nodules, arterial enhancement pattern, CT-reported LN metastasis, and capsular retraction were poor independent prognostic factors for IMCC patients. • The imaging-based nomograms presented improved prognostic performance and discrimination compared with the common prognostic models. • The nomograms can provide accurate individual survival prediction for IMCC patients before and after surgery.

Development and validation of a gradient boosting machine to predict prognosis after liver resection for intrahepatic cholangiocarcinoma.

BACKGROUND: Accurate prognosis assessment is essential for surgically resected intrahepatic cholangiocarcinoma (ICC) while published prognostic tools are limited by modest performance. We therefore aimed to establish a novel model to predict survival in resected ICC based on readily-available clinical parameters using machine learning technique. METHODS: A gradient boosting machine (GBM) was trained and validated to predict the likelihood of cancer-specific survival (CSS) on data from a Chinese hospital-based database using nested cross-validation, and then tested on the Surveillance, Epidemiology, and End Results (SEER) database. The performance of GBM model was compared with that of proposed prognostic score and staging system. RESULTS: A total of 1050 ICC patients (401 from China and 649 from SEER) treated with resection were included. Seven covariates were identified and entered into the GBM model: age, tumor size, tumor number, vascular invasion, number of regional lymph node metastasis, histological grade, and type of surgery. The GBM model predicted CSS with C-Statistics ≥ 0.72 and outperformed proposed prognostic score or system across study cohorts, even in sub-cohort with missing data. Calibration plots of predicted probabilities against observed survival rates indicated excellent concordance. Decision curve analysis demonstrated that the model had high clinical utility. The GBM model was able to stratify 5-year CSS ranging from over 54% in low-risk subset to 0% in high-risk subset. CONCLUSIONS: We trained and validated a GBM model that allows a more accurate estimation of patient survival after resection compared with other prognostic indices. Such a model is readily integrated into a decision-support electronic health record system, and may improve therapeutic strategies for patients with resected ICC.

Surgical Strategy for Resecting Hepatocellular Carcinoma in the Caudate Lobe: Isolated or Combined Lobectomy? A Single-Center Study and Meta-Analysis.

BACKGROUND: Resection of hepatocellular carcinoma (HCC) originating in the caudate lobe remains challenging, while the optimal extent of resection is debated. We aimed to evaluate the relative benefits of combined caudate lobectomy (CCL) versus isolated caudate lobectomy (ICL) for caudate HCC. METHODS: Patients who underwent curative-intent resection for caudate HCC between January 2010 and December 2018 were identified from a single-center database. Surgical outcomes of the two strategy groups were analyzed before and after propensity score matching. A systematic review with meta-analysis was also performed to compare outcomes of CCL versus ICL for caudate HCC. RESULTS: A total of 28 patients were included: 11 in the CCL and 17 in the ICL group. Compared with ICL, the CCL group contained patients with larger tumors and a higher incidence of vascular invasion. After propensity score matching, 6 pairs of patients were selected. In the well-matched cohort, CCL demonstrated significantly improved recurrence-free survival (RFS) (P = 0.047) compared with ICL; no significant differences were noted for overall survival (OS), operation time, blood loss and morbidity rate. A total of 227 patients from nine eligible studies and ours were involved in the systematic review. Meta-analysis revealed that CCL provided better RFS (hazard ratio 0.54, 95% confidence interval 0.31-0.92) than ICL; no significant differences were observed in OS, operation time, blood loss and morbidity rate. CONCLUSION: CCL confers superior RFS over ICL without compromise of perioperative outcomes and should be prioritized for patients with caudate HCC when feasible, especially for those with large-sized tumors.

Machine Learning to Improve Prognosis Prediction of Early Hepatocellular Carcinoma After Surgical Resection.

BACKGROUND: Improved prognostic prediction is needed to stratify patients with early hepatocellular carcinoma (EHCC) to refine selection of adjuvant therapy. We aimed to develop a machine learning (ML)-based model to predict survival after liver resection for EHCC based on readily available clinical data. METHODS: We analyzed data of surgically resected EHCC (tumor≤5 cm without evidence of extrahepatic disease or major vascular invasion) patients from the Surveillance, Epidemiology, and End Results (SEER) Program to train and internally validate a gradient-boosting ML model to predict disease-specific survival (DSS). We externally tested the ML model using data from 2 Chinese institutions. Patients treated with resection were matched by propensity score to those treated with transplantation in the SEER-Medicare database. RESULTS: A total of 2778 EHCC patients treated with resection were enrolled, divided into 1899 for training/validation (SEER) and 879 for test (Chinese). The ML model consisted of 8 covariates (age, race, alpha-fetoprotein, tumor size, multifocality, vascular invasion, histological grade and fibrosis score) and predicted DSS with C-Statistics >0.72, better than proposed staging systems across study cohorts. The ML model could stratify 10-year DSS ranging from 70% in low-risk subset to 5% in high-risk subset. Compared with low-risk subset, no remarkable survival benefits were observed in EHCC patients receiving transplantation before and after propensity score matching. CONCLUSION: An ML model trained on a large-scale dataset has good predictive performance at individual scale. Such a model is readily integrated into clinical practice and will be valuable in discussing treatment strategies.

Integrating Machine Learning and Tumor Immune Signature to Predict Oncologic Outcomes in Resected Biliary Tract Cancer.

BACKGROUND: Improved methods are needed to predict outcomes in biliary tract cancers (BTCs). We aimed to build an immune-related signature and establish holistic models using machine learning. METHODS: Samples were from 305 BTC patients treated with curative-intent resection, divided into derivation and validation cohorts in a two-to-one ratio. Spatial resolution of T cell infiltration and PD-1/PD-L1 expression was assessed by immunohistochemistry. An immune signature was constructed using classification and regression tree. Machine learning was applied to develop prediction models for disease-specific survival (DSS) and recurrence-free survival (RFS). RESULTS: The immune signature composed of CD3+, CD8+, and PD-1+ cell densities and PD-L1 expression within tumor epithelium significantly stratified patients into three clusters, with median DSS varying from 11.7 to 80.8 months and median RFS varying from 6.2 to 62.0 months. Gradient boosting machines (GBM) outperformed rival machine-learning algorithms and selected the same 11 covariates for DSS and RFS prediction: immune signature, tumor site, age, bilirubin, albumin, carcinoembryonic antigen, cancer antigen 19-9, tumor size, tumor differentiation, resection margin, and nodal metastasis. The clinical-immune GBM models accurately predicted DSS and RFS, with respective concordance index of 0.776-0.816 and 0.741-0.781. GBM models showed significantly improved performance compared with tumor-node-metastasis staging system. CONCLUSIONS: The immune signature promises to stratify prognosis and allocate treatment in resected BTC. The clinical-immune GBM models accurately predict recurrence and death from BTC following surgery.

Development and Validation of Pre- and Post-Operative Models to Predict Recurrence After Resection of Solitary Hepatocellular Carcinoma: A Multi-Institutional Study.

BACKGROUND: The ideal candidates for resection are patients with solitary hepatocellular carcinoma (HCC); however, postoperative recurrence rate remains high. We aimed to establish prognostic models to predict HCC recurrence based on readily accessible clinical parameters and multi-institutional databases. PATIENTS AND METHODS: A total of 485 patients undergoing curative resection for solitary HCC were recruited from two independent institutions and the Cancer Imaging Archive database. We randomly divided the patients into training (n=323) and validation cohorts (n=162). Two models were developed: one using pre-operative and one using pre- and post-operative parameters. Performance of the models was compared with staging systems. RESULTS: Using multivariable analysis, albumin-bilirubin grade, serum alpha-fetoprotein and tumor size were selected into the pre-operative model; albumin-bilirubin grade, serum alpha-fetoprotein, tumor size, microvascular invasion and cirrhosis were selected into the postoperative model. The two models exhibited better discriminative ability (concordance index: 0.673-0.728) and lower prediction error (integrated Brier score: 0.169-0.188) than currently used staging systems for predicting recurrence in both cohorts. Both models stratified patients into low- and high-risk subgroups of recurrence with distinct recurrence patterns. CONCLUSION: The two models with corresponding user-friendly calculators are useful tools to predict recurrence before and after resection that may facilitate individualized management of solitary HCC.

Radiomic Features at Contrast-enhanced CT Predict Recurrence in Early Stage Hepatocellular Carcinoma: A Multi-Institutional Study.

Background Early stage hepatocellular carcinoma (HCC) is the ideal candidate for resection in patients with preserved liver function; however, cancer will recur in half of these patients and no reliable prognostic tool has been established. Purpose To investigate the effectiveness of radiomic features in predicting tumor recurrence after resection of early stage HCC. Materials and Methods In total, 295 patients (median age, 58 years; interquartile range, 50-65 years; 221 men) who underwent contrast material-enhanced CT and curative resection for early stage HCC that met the Milan criteria between February 2009 and December 2016 were retrospectively recruited from three independent institutions. Follow-up consisted of serum α-fetoprotein level, liver function tests, and dynamic imaging examinations every 3 months during the first 2 years and then every 6 months thereafter. In the development cohort of 177 patients from institution 1, recurrence-related radiomic features were computationally extracted from the tumor and its periphery and a radiomics signature was built with least absolute shrinkage and selection operator regression. Two models, one integrating preoperative and one integrating pre- and postoperative variables, were created by using multivariable Cox regression analysis. An independent external cohort of 118 patients from institutions 2 and 3 was used to validate the proposed models. Results The preoperative model integrated radiomics signature with serum α-fetoprotein level and tumor number; the postoperative model incorporated microvascular invasion and satellite nodules into the above-mentioned predictors. In both study cohorts, two radiomics-based models provided better predictive performance (concordance index ≥0.77, P < .05 for all), lower prediction error (integrated Brier score ≤0.14), and larger net benefits, as determined by means of decision curve analysis, than rival models without radiomics and widely adopted staging systems. The radiomics-based models gave three risk strata with high, intermediate, or low risk of recurrence and distinct profiles of recurrent tumor number. Conclusion The proposed radiomics models with pre- and postresection features helped predict tumor recurrence for early stage hepatocellular carcinoma. © RSNA, 2020 Online supplemental material is available for this article.

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

BACKGROUND: Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is no reliable prediction tool. We explored the potential of radiomics coupled with machine-learning algorithms to improve the predictive accuracy for HCC recurrence. METHODS: A total of 470 patients who underwent contrast-enhanced CT and curative resection for solitary HCC were recruited from 3 independent institutions. In the training phase of 210 patients from Institution 1, a radiomics-derived signature was generated based on 3384 engineered features extracted from primary tumor and its periphery using aggregated machine-learning framework. We employed Cox modeling to build predictive models. The models were then validated using an internal dataset of 107 patients and an external dataset of 153 patients from Institution 2 and 3. FINDINGS: Using the machine-learning framework, we identified a three-feature signature that demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.633-0.699. Serum alpha-fetoprotein, albumin-bilirubin grade, liver cirrhosis, tumor margin, and radiomics signature were selected for preoperative model; postoperative model incorporated satellite nodules into above-mentioned predictors. The two models showed superior prognostic performance, with C-index of 0.733-0.801 and integrated Brier score of 0.147-0.165, compared with rival models without radiomics and widely used staging systems (all P < 0.05); they also gave three risk strata for recurrence with distinct recurrence patterns. INTERPRETATION: When integrated with clinical data sources, our three-feature radiomics signature promises to accurately predict individual recurrence risk that may facilitate personalized HCC management.

A radiomics approach to predict lymph node metastasis and clinical outcome of intrahepatic cholangiocarcinoma.

OBJECTIVES: This study was conducted in order to establish and validate a radiomics model for predicting lymph node (LN) metastasis of intrahepatic cholangiocarcinoma (IHC) and to determine its prognostic value. METHODS: For this retrospective study, a radiomics model was developed in a primary cohort of 103 IHC patients who underwent curative-intent resection and lymphadenectomy. Radiomics features were extracted from arterial phase computed tomography (CT) scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. Multivariate logistic regression analysis was adopted to establish a radiomics model incorporating radiomics signature and other independent predictors. Model performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 52 consecutive patients. RESULTS: The radiomics signature comprised eight LN-status-related features and showed significant association with LN metastasis in both cohorts (p < 0.001). A radiomics nomogram that incorporates radiomics signature and CA 19-9 level showed good calibration and discrimination in the primary cohort (AUC 0.8462) and validation cohort (AUC 0.8921). Promisingly, the radiomics nomogram yielded an AUC of 0.9224 in the CT-reported LN-negative subgroup. Decision curve analysis confirmed the clinical utility of this nomogram. High risk for metastasis portended significantly lower overall and recurrence-free survival than low risk for metastasis (both p < 0.001). The radiomics nomogram was an independent preoperative predictor of overall and recurrence-free survival. CONCLUSIONS: Our radiomics model provided a robust diagnostic tool for prediction of LN metastasis, especially in CT-reported LN-negative IHC patients, that may facilitate clinical decision-making. KEY POINTS: • The radiomics nomogram showed good performance for prediction of LN metastasis in IHC patients, particularly in the CT-reported LN-negative subgroup. • Prognosis of high-risk patients remains dismal after curative-intent resection. • The radiomics model may facilitate clinical decision-making and define patient subsets benefiting most from surgery.

Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes.

Purpose To evaluate a radiomics model for predicting lymph node (LN) metastasis in biliary tract cancers (BTCs) and to determine its prognostic value for disease-specific and recurrence-free survival. Materials and Methods For this retrospective study, a radiomics model was developed on the basis of a primary cohort of 177 patients with BTC who underwent resection and LN dissection between June 2010 and December 2016. Radiomic features were extracted from portal venous CT scans. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator method. Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was internally validated in 70 consecutive patients with BTC between January 2017 and February 2018. Results The radiomics signature, composed of three LN-status-related features, was associated with LN metastasis in primary and validation cohorts (P < .001). The radiomics nomogram that incorporated radiomics signature and CT-reported LN status showed good calibration and discrimination in primary cohort (area under the curve, 0.81) and validation cohort (area under the curve, 0.80). Patients at high risk of LN metastasis portended lower disease-specific and recurrence-free survival than did those at low risk after surgery (both P < .001). High-risk LN metastasis was an independent preoperative predictor of disease-specific survival (hazard ratio, 3.37; P < .001) and recurrence-free survival (hazard ratio, 1.98; P = .003). Conclusion A radiomics model derived from portal phase CT of the liver has good performance for predicting lymph node metastasis in biliary tract cancer and may help to improve clinical decision making. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Laghi and Voena in this issue.

Short- and long-term outcomes of middle hepatic vein-oriented hepatectomy for advanced perihilar cholangiocarcinoma.

BACKGROUND: This study aimed to compare clinical outcomes of the middle hepatic vein (MHV)-oriented versus conventional hemihepatectomy for perihilar cholangiocarcinoma (PHC). METHODS: From 2008 to 2017, medical records of patients undergoing hemihepatectomy with caudate lobectomy for advanced PHC were reviewed retrospectively. MHV-oriented hepatectomy was defined as full exposure of the MHV on the dissection plane. Predictors of morbidity and survival were identified. RESULTS: A total of 125 patients were enrolled. MHV-oriented and conventional hepatectomies were performed in 44 and 81 patients, respectively. The curative resection rate, blood loss, transfusion, and survival were comparable between two groups; however, severe morbidity rate was significantly lower in the MHV-oriented group (9.1% vs 38.3%, P < 0.001). MHV-oriented approach was an independent predictor of severe morbidity, as were the age, bilirubin level, and blood transfusion. Severe morbidity was associated with significantly decreased overall survival and recurrence-free survival (RFS) (median 29.0 vs 46.9 months, P = 0.011 and 20.3 vs 31.1 months, P = 0.003, respectively). Multivariate analysis revealed that severe morbidity independently predicted shorter RFS (P = 0.025). CONCLUSIONS: MHV-oriented approach for advanced PHC is safe and associated with a significant decrease in severe morbidity. Severe morbidity adversely affects survival after surgery; therefore, optimal preoperative preparation and MHV-oriented hepatectomy with meticulous dissection remain of critical importance.

Radiologic evaluation of vasculobiliary anatomy in the umbilical fissure.

BACKGROUND: Preoperative evaluation of vasculobiliary anatomy in the umbilical fissure (U-point) is pivotal for perihilar cholangiocarcinoma (PCCA) applied to right-sided hepatectomy. The purpose of our study was to review the vasculobiliary anatomy in the U-point using three-dimensional (3D) reconstruction technique, to investigate the diagnostic ability of 2D scans to evaluate anatomic variations, and to discuss its surgical implications. METHODS: A retrospective study of 159 patients with Bismuth type I, II, and IIIa PCCA, who received surgery at our institution from November 2012 to September 2016, was conducted. Anatomic structures were assessed using multidetector computed tomography (MDCT) by one hepatobiliary surgeon, whereas 3D images were reconstructed by an independent radiologist. Normal confluence pattern of left biliary system was defined as the left medial segmental bile duct (B4) joining the common trunk of segment II (B2) and segment III (B3) ducts, whereas aberrant confluence patterns were classified into 3 types: type I, triple confluence of B2, B3, and B4; type II, B2 draining into the common trunk of B3 and B4; type III, other patterns. Surgical anatomy of B4 was classified into the central, peripheral, and combined type according to its relation to the hepatic confluence. The lengths from the bile duct branch of Spiegel's lobe (B1l) to the orifice of B4 and the junction of B2 and B3 were measured on 3D images. The anatomy of left hepatic artery (LHA) was classified according to different origins and the spatial relationship related to the U-point. RESULTS: 3D reconstruction revealed that normal confluence pattern of left biliary system was observed in 71.1% (113/159) of all patients, and variant patterns were type I in 11.9% (19/159), type II in 12.6% (20/159), and type III in 4.4% (7/159). The length from B1l to the junction of B2 and B3 was 12.1 ± 3.1 mm in type I variation, which was significantly shorter than that in normal configuration (30.0 ± 6.8 mm, P < 0.001) but significantly longer than that in type II variation (9.6 ± 3.4 mm, P = 0.019). Surgical anatomy of B4: the peripheral type was most commonly seen (74.2%, 118/159), followed by central type (15.7%, 25/159) and combined type (10.1%, 16/159). The distance between the B1l and B4 was 8.4 ± 2.4 mm in central and combined type, which was significantly shorter than that in peripheral type (14.5 ± 4.1 mm, P < 0.001). A replaced or accessory LHA from the left gastric artery was present in 6 (3.8%) and 9 (5.7%) patients, respectively. LHA running along the left caudal position of U-point was present in 143 cases (89.9%), along the right cranial position of U-point in nine cases (5.7 %), and combined position in seven cases (4.4%). Interobserver agreement of two imaging modalities was almost perfect in biliary confluence pattern (kappa = 0.90; 95% confidence interval: 0.79-1.00), substantial in surgical anatomy of B4 (kappa = 0.74; 95% confidence interval: 0.62-0.86), and perfect in LHA (kappa = 1.00). CONCLUSIONS: Thoroughly understanding the imaging characters of surgical anatomy in the U-point may be benefit for preoperative evaluation of PCCA by successive review of 2D images alone, whereas 3D reconstruction technique allows detailed hepatic anatomy and individualized surgical planning for advanced cases.

Clinical Implications of Biliary Confluence Pattern for Bismuth-Corlette Type IV Hilar Cholangiocarcinoma Applied to Hemihepatectomy.

BACKGROUND: Since biliary variations are commonly seen, our aims are to clarify these insidious variations and discuss their surgicopathologic implications for Bismuth-Corlette (BC) type IV hilar cholangiocarcinoma (HC) applied to hemihepatectomy. METHODS: Three-dimensional images of patients with distal bile duct obstruction (n = 97) and advanced HC (n = 79) were reconstructed and analyzed retrospectively. Normal biliary confluence pattern was defined as the peripheral segment IV duct (B4) joining the common trunk of segment II (B2) and segment III (B3) ducts to form the left hepatic duct (LHD) that then joined the right hepatic duct (RHD). The lengths from left and right secondary biliary ramifications to the right side of the umbilical portion of the left portal vein (Rl-L) and the cranio-ventral side of the right portal vein (Rr-R) were measured, respectively, and compared with the resectable bile duct length in HCs. Surgicopathologic findings were compared between different BC types. RESULTS: The resectable bile duct length in right hemihepatectomy for eradication of type IV tumors was significantly longer than the Rl-L length in normal biliary configuration (17.4 ± 1.8 and 10.3 ± 3.4 mm, respectively, p < 0.001), and type III variation (B2 joining the common trunk of B3 and B4) was the predominant configuration (53.8%). The resectable length in left hemihepatectomy for eradication of type IV tumors was comparable with the Rr-R length in RHD absent cases (15.2 ± 2.5 and 16.4 ± 2.6 mm, respectively, p = 0.177) but significantly longer than that in normal configuration (p < 0.001). The estimated length was 8.5 ± 2.0 mm in unresectable cases. There was no significant difference between type III and IV tumors, except for the rate of nodal metastasis (29.7 and 76.0%, respectively, p < 0.001). CONCLUSION: Hemihepatectomy might be selected for curative-intent resection of BC type IV tumors considering the advantageous biliary variations, whereas anatomical trisegmentectomy is recommended for the resectable bile duct length less than 10 mm. Biliary variations might result in excessive classification of BC type IV but require validation on further study.

FAM83D activates the MEK/ERK signaling pathway and promotes cell proliferation in hepatocellular carcinoma.

Publicly available microarray data suggests that the expression of FAM83D (Family with sequence similarity 83, member D) is elevated in a wide variety of tumor types, including hepatocellular carcinoma (HCC). However, its role in the pathogenesis of HCC has not been elucidated. Here, we showed that FAM83D was frequently up-regulated in HCC samples. Forced FAM83D expression in HCC cell lines significantly promoted their proliferation and colony formation while FAM83D knockdown resulted in the opposite effects. Mechanistic analyses indicated that FAM83D was able to activate the MEK/ERK signaling pathway and promote the entry into S phase of cell cycle progression. Taken together, these results demonstrate that FAM83D is a novel oncogene in HCC development and may constitute a potential therapeutic target in HCC.