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.

Hypoxia-associated circPRDM4 promotes immune escape via HIF-1α regulation of PD-L1 in hepatocellular carcinoma.

BACKGROUND: Hypoxia is a hallmark of cancer, and is closely intertwined with tumor immune evasion. Circular RNAs (circRNAs) have been implicated in tumor response to immune checkpoint blockades. However, hypoxia-associated circRNAs that orchestrate the association between hypoxia and response to immunotherapy remain poorly understood. Here, we aimed to determine the roles of hypoxia-associated circRNAs in immune escape of hepatocellular carcinoma (HCC) cells. METHODS: Differentially expressed hypoxia-associated circRNAs were determined using high-throughput sequencing technology. HCC patients treated with PD-1 blockade were enrolled to assess the clinical significance of circPRDM4. RT-qPCR, western blotting, flow cytometry, T cell-mediated tumor cell killing assay, and enzyme linked immunosorbent assay were used to investigate the roles of circPRDM4 in immune escape of HCC cells in vitro. Patient-derived xenograft mouse models and adoptive human tumor infiltrating lymphocyte-CD8+ T cell transfer were adopted to evaluate the effects of circPRDM4 in vivo. RNA pull-down, mass spectrometry, RNA immunoprecipitation, chromatin immunoprecipitation, chromatin isolation by RNA purification, dual-luciferase reporter assays, dot blotting, DNA in situ hybridization, and immunoprecipitation were utilized to examine the interaction between circPRDM4, HIF-1α, and CD274 promoter. RESULTS: We identified circPRDM4 as a hypoxia-associated circRNA in HCC. circPRDM4 was upregulated in responders to PD-1 blockade and associated with therapeutic efficacy. In vitro and in vivo experiments showed that circPRDM4 induced PD-L1 expression and promoted CD8+ T cell-mediated immune escape under hypoxic conditions. Mechanistically, circPRDM4 acted as a scaffold to recruit HIF-1α onto CD274 promoter, and cemented their interaction, ultimately promoting the HIF-1α-mediated transactivation of PD-L1. CONCLUSIONS: These findings illustrated that circPRDM4 promoted immune escape of HCC cells by facilitating the recruitment of HIF-1α onto the promoter of CD274 under hypoxia, thereby inhibiting CD8+ T cell infiltration in the tumor microenvironment. This work may provide a novel prognostic biomarker and therapeutic candidate for HCC immunotherapy.

Apolipoprotein A-1 protected hepatic ischaemia-reperfusion injury through suppressing macrophage pyroptosis via TLR4-NF-κB pathway.

BACKGROUND AND AIMS: Apolipoprotein A-1 (ApoA-1), the major apolipoprotein of high-density lipoprotein, plays anti-atherogenic role in cardiovascular diseases and exerts anti-inflammation effect in various inflammatory and infectious diseases. However, the role and mechanism of ApoA-1 in hepatic ischaemia-reperfusion (I/R) injury is unknown. METHODS: In this study, we measured ApoA-1 expression in human liver grafts after transplantation. Mice partial hepatic I/R injury model was made in ApoA-1 knockout mice, ApoA-1 mimetic peptide D-4F treatment mice and corresponding control mice to examine the effect of ApoA-1 on liver damage, inflammation response and cell death. Primary hepatocytes and macrophages were isolated for in vitro study. RESULTS: The results showed that ApoA-1 expression was down-regulated in human liver grafts after transplantation and mice livers subjected to hepatic I/R injury. ApoA-1 deficiency aggravated liver damage and inflammation response induced by hepatic I/R injury. Interestingly, we found that ApoA-1 deficiency increased pyroptosis instead of apoptosis during acute phase of hepatic I/R injury, which mainly occurred in macrophages rather than hepatocytes. The inhibition of pyroptosis compensated for the adverse impact of ApoA-1 deficiency. Furthermore, the up-regulated pyroptosis process was testified to be mediated by ApoA-1 through TLR4-NF-κB pathway and TLR4 inhibition significantly improved hepatic I/R injury. In addition, we confirmed that D-4F ameliorated hepatic I/R injury. CONCLUSIONS: Our study has identified the protective role of ApoA-1 in hepatic I/R injury through inhibiting pyroptosis in macrophages via TLR4-NF-κB pathway. The effect of ApoA-1 may provide a novel therapeutic approach for hepatic I/R injury.

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.

c-Myc-driven glycolysis polarizes functional regulatory B cells that trigger pathogenic inflammatory responses.

B cells secreting IL-10 functionally are recognized as functional regulatory B (Breg) cells; however, direct evidence concerning the phenotype, regulation, and functional and clinical relevance of IL-10-secreting Breg cells in humans is still lacking. Here, we demonstrate that, although IL-10 itself is anti-inflammatory, IL-10+ functional Breg cells in patients with systemic lupus erythematosus (SLE) display aggressive inflammatory features; these features shift their functions away from inducing CD8+ T cell tolerance and cause them to induce a pathogenic CD4+ T cell response. Functional Breg cells polarized by environmental factors (e.g., CPG-DNA) or directly isolated from patients with SLE mainly exhibit a CD24intCD27-CD38-CD69+/hi phenotype that is different from that of their precursors. Mechanistically, MAPK/ERK/P38-elicited sequential oncogenic c-Myc upregulation and enhanced glycolysis are necessary for the generation and functional maintenance of functional Breg cells. Consistently, strategies that abrogate the activity of ERK, P38, c-Myc, and/or cell glycolysis can efficiently eliminate the pathogenic effects triggered by functional Breg cells.

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.

Chinese Expert Consensus on Immunotherapy for Hepatocellular Carcinoma (2021 Edition).

Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies in China. Most HCC patients are first diagnosed at an advanced stage, and systemic treatments are the mainstay of treatment. Summary: In recent years, immune checkpoint inhibitors have made a breakthrough in the systemic treatment of middle-advanced HCC, breaking the single therapeutic pattern of molecular-targeted agents. To better guide the clinical treatment for effective and safe use of immunotherapeutic drugs, the Chinese Association of Liver Cancer and Chinese Medical Doctor Association has gathered multidisciplinary experts and scholars in relevant fields to formulate the "Chinese Clinical Expert Consensus on Immunotherapy for Hepatocellular Carcinoma (2021)" based on current clinical studies and clinical medication experience for reference in China. Key Messages: The consensus contained 17 recommendations, including the preferred regimen for first- and second-line immunotherapy, evaluation and monitoring before/during/after treatment, management of complications, precautions for special patients, and potential population for immunotherapy.

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.

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Root exudates are important carriers for material exchange and information transfer between plant and soil, and important regulators of crop-soil-microorganism interaction in intercropping systems. We examined the interaction between crops in intercropping system by setting three treatments, monoculture Chinese milk vetch, monoculture rape and Chinese milk vetch intercropped with rape. The responses of root exudates were emphatically analyzed. The results showed that 391 root exudates were detected, with 93 of which being identified and divided into nine types of metabo-lites. Among them, organooxygen compounds were the most abundant, mainly in the form of ribitol. Under different planting patterns, root exudates of Chinese milk vetch and rape were significantly different. The characteristics of root exudates in intercropping were similar to monoculture rape, but significantly different from monoculture Chinese milk vetch. Among the root exudates in different planting modes, only 9-fluorenone 1 was negatively correlated with others. The differential root exudates were mainly benzenoids, lipids and lipid-like molecules, organic acids and derivatives, and organooxygen compounds. The benzenoids, lipids and lipid-like molecules were important types that characterized the changes of root exudates of Chinese milk vetch and rape. Chinese milk vetch intercropping with rape changed the characteristics of root exudates, which were closely related to benzenoids, lipids, and lipid-like molecules.

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.

Surgical Strategies for the Treatment of Bismuth Type I and II Hilar Cholangiocarcinoma: Bile Duct Resection with or Without Hepatectomy?

BACKGROUND: The role of hepatic resection in the treatment of type I and II hilar cholangiocarcinoma (HCCA) remains controversial. In the present study, we aimed to identify whether hepatic resection was necessary for type I and II HCCA. METHODS: A total of 23 patients classified as type I and II HCCA undergoing surgical resection were included in this study. The patients were divided into two groups: bile duct resection (BDR) group (n = 15) and hepatic resection (HR) group (n = 8). Systematic review and meta-analysis were performed to compare the R0 resection and long-term survival between BDR and HR for Bismuth type I and II HCCA. A total of 7 studies with 260 cases were included in this meta-analysis. RESULTS: In our cohort, the R0 resection rate was 73.3% in BDR group and 87.5% in HR group. The HR group had a higher number of postoperative complications than the BDR group (P = 0.002). There was no difference in long-term survival (P = 0.544) and recurrence (P = 0.846) between BDR and HR in Bismuth type I and II HCCA. The meta-analysis showed that HR was associated with better R0 resection rate (RR 4.45, 95% CI 2.34-8.48) and overall survival (HR 2.15, 95% CI 1.34-3.44) compared with BDR group. There was no publication bias and undue influence of any single study. CONCLUSIONS: The meta-analysis showed that HR was associated with better R0 resection rate and overall survival compared with BDR for type I and II HCCA patients. More aggressive surgical strategies should be increasingly considered for the treatment of type I and II HCCA patients.

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.

Effects of multimodal fast-track surgery on liver transplantation outcomes.

BACKGROUND: Fast-track surgery and enhanced recovery after surgery have been applied to many surgical procedures; however, data on fast-track surgery and enhanced recovery after surgery following liver transplantation is limited. This study aimed to conduct a prospective study to determine the effects of fast-track surgery on prognosis after liver transplantation. METHODS: This was a prospective, single-blinded, randomized study. One hundred twenty-eight patients undergoing liver transplantation were selected for the fast-track (FT group, n=54) or conventional process (NFT group, n=74). The primary endpoints were intensive care unit (ICU) stay and hospital stay. The secondary endpoints were as follows: operative time, anhepatic phase time, intraoperative blood loss, intraoperative blood transfusion volume, postoperative complications, readmission rate, and postoperative mortality. RESULTS: There was no significant difference in preoperative demographics between the two groups. The median ICU stay was 2 days (range 1-7 days) in the FT group and 5 days (range 3-12 days) in the NFT group (P<0.01). Furthermore, the hospital stay was also significantly reduced in the FT group (P<0.01). The operative time, anhepatic phase time, intraoperative blood loss, and intraoperative blood transfusion volume were decreased in the FT group compared with the NFT group (P<0.05). Based on Spearman correlation analysis, the ICU stay and hospital stay may be positively correlated with operative time, anhepatic phase time and intraoperative blood loss. There were no differences in the incidence of postoperative complications, readmissions, and postoperative mortality between the two groups. CONCLUSION: Fast-track procedures effectively reduce the ICU stay and hospital stay without adversely affecting prognosis. This study demonstrated that fast-track protocols are safe and feasible in liver transplantation.