Wang YF, Han K, An N, Sun YN, Gao F, Sun Y, Zhang D, Zhao ZF, Guo Q, Gu JN, Yang Z. Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year multicenter retrospective study. World J Gastroenterol 2025; 31(47): 113156 [DOI: 10.3748/wjg.v31.i47.113156]
Corresponding Author of This Article
Zhuo Yang, MD, Chief Physician, Professor, Department of Endoscopy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang 110016, Liaoning Province, China. yangzhuocy@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Dec 21, 2025 (publication date) through Dec 19, 2025
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
Wang YF, Han K, An N, Sun YN, Gao F, Sun Y, Zhang D, Zhao ZF, Guo Q, Gu JN, Yang Z. Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year multicenter retrospective study. World J Gastroenterol 2025; 31(47): 113156 [DOI: 10.3748/wjg.v31.i47.113156]
Yi-Fei Wang, Ke Han, Na An, Ya-Nan Sun, Feng Gao, Yong Sun, Di Zhang, Jiang-Ning Gu, Zhuo Yang, Department of Endoscopy, General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
Di Zhang, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, Liaoning Province, China
Zhi-Feng Zhao, Department of Gastroenterology, The Fourth Affiliated Hospital of China Medical University, Shenyang 110032, Liaoning Province, China
Qing Guo, Surgical Endoscopy Center, The Northeast International Hospital, Shenyang 110015, Liaoning Province, China
Co-corresponding authors: Jiang-Ning Gu and Zhuo Yang.
Author contributions: Wang YF and Han K conceived and designed the study, drafted the manuscript, and approved the final version for publication; Wang YF contributed to data curation, formal analysis, and data visualization; An N, Sun YN, and Gao F validated the results and revised the manuscript; Sun Y, Zhang D, Zhao ZF, and Guo Q contributed to the methodology and manuscript editing; Gu JN and Yang Z played key roles in the experimental design, data interpretation, manuscript preparation, and project supervision as co-corresponding authors; All authors reviewed and approved the final version and agree to be accountable for the integrity of the work. Wang YF and Han K made substantial contributions to the work and are designated as co-first authors.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of the General Hospital of Northern Theater Command [Approval No. Y (2024) 336].
Informed consent statement: This retrospective study used existing clinical data and was approved by the Ethics Committee, with informed consent waived.
Conflict-of-interest statement: The authors have no conflicts of interest to declare.
Data sharing statement: No additional data are available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Zhuo Yang, MD, Chief Physician, Professor, Department of Endoscopy, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenhe District, Shenyang 110016, Liaoning Province, China. yangzhuocy@163.com
Received: August 18, 2025 Revised: September 16, 2025 Accepted: October 28, 2025 Published online: December 21, 2025 Processing time: 124 Days and 3.4 Hours
Abstract
BACKGROUND
As a prominent drainage method, endoscopic retrograde cholangiopancreatography (ERCP) with stenting has been universally employed to treat malignant perihilar biliary obstruction (MPHBO). Nonetheless, postoperative biliary infection (PBI) constitutes a remarkable complication associated with this procedure, which can result in fatal outcomes under some circumstances.
AIM
To investigate the risk factors and predict the occurrence of PBI following ERCP drainage in patients suffering from MPHBO.
METHODS
This retrospective study analyzed data from patients who underwent ERCP drainage at three different centers. Independent risk factors for PBI were identified by adopting multivariate analyses. Logistic regression model and artificial neural network (ANN) models were developed and validated to predict PBI.
RESULTS
A total of 288 patients who underwent 403 ERCP procedures were included in the study. The incidence of PBI was 39% (158/403). As evidently demonstrated by multivariate analysis, the Bismuth-Corlett classification (odds ratio [OR] = 1.412; 95% confidence interval [CI]: 1.144-1.743; P = 0.001), hypokalemia (OR = 4.080; 95%CI: 1.958-8.505; P < 0.001), and aspartate transaminase (AST) (OR = 1.003; 95%CI: 1.000-1.006; P = 0.021) were independent risk factors for PBI. Simultaneously, both a logistic regression model (area under the curve [AUC] = 0.734) and an ANN model (AUC = 0.867) were developed by adopting these factors. As suggested by a validation through 45 additional cases, the ANN model demonstrated an AUC of 0.940, surpassing the logistic regression model’s AUC of 0.791.
CONCLUSION
The Bismuth-Corlett classification, hypokalemia, and AST levels were identified as independent risk factors for PBI following ERCP drainage. The ANN model was proven to be an effective approach for the anticipation of the PBI occurrence.
Core Tip: This study established logistic regression and artificial neural network (ANN) models to preoperatively predict postoperative biliary infection—a serious complication worsening surgical outcomes and short-term prognosis in patients with malignant perihilar biliary obstruction undergoing endoscopic retrograde cholangiopancreatography drainage. Multivariate logistic regression identified key preoperative risk factors, notably hypokalemia, Bismuth-Corlett classification, and elevated aspartate transaminase levels. The ANN model demonstrated markedly superior predictive performance compared with logistic regression. These models offer clinicians a practical tool for early identification of high-risk patients, enabling timely, targeted interventions to mitigate infection-related complications and improve postoperative outcomes.
Citation: Wang YF, Han K, An N, Sun YN, Gao F, Sun Y, Zhang D, Zhao ZF, Guo Q, Gu JN, Yang Z. Risk prediction of biliary infection after endoscopic drainage for malignant perihilar biliary obstruction: A 10-year multicenter retrospective study. World J Gastroenterol 2025; 31(47): 113156
Malignant perihilar biliary obstruction (MPHBO) encompasses a vast array of malignant entities, comprising hilar cholangiocarcinoma (Klatskin tumor), icteric hepatocellular carcinoma, gallbladder carcinoma, metastatic tumors, and lymphomas. It is defined as an intrinsic or extrinsic lesion involving the bile duct system, which not only extends from the common hepatic duct to the second-order intrahepatic ducts but also gives rise to obstructive jaundice[1,2]. Surgical resection continuously serves as a favorable treatment for MPHBO. Nevertheless, early disease typically presents with nonspecific symptoms, delaying diagnosis until advanced stages that are often complicated by comorbidities and poor performance status. Fewer than 20% of patients are eligible for surgical intervention[3], and long-term survival remains less favorable[4-6].
Since 2000, biliary drainage has functioned as a cornerstone of palliative management for unresectable MPHBO, which underscores the obstruction relief through drainage of more than 50% of the hepatic volume to mitigate jaundice and secondary hepatic dysfunction[7,8]. Current drainage modalities predominantly include percutaneous transhepatic biliary drainage (PTBD) and endoscopic retrograde cholangiopancreatography (ERCP) with stenting. In contrast to PTBD, due to its minimally invasive approach, lower hemorrhage risk, and better quality of life, ERCP with stent placement yields superior outcomes particularly in older adults, which establishes it as the preferred palliative intervention for malignant biliary obstruction[9,10].
Notwithstanding these advantages, ERCP carries inherent procedural risks, particularly postoperative biliary infection (PBI), which may delay recovery, precipitate more severe complications, and lead to life-threatening outcomes[11,12]. The pathogenesis of PBI may involve: (1) Biliary hypertension due to incomplete drainage that compromises the blood-biliary barrier; (2) Retrograde colonization by intestinal flora, particularly Escherichia coli driven by multifactorial pathological triggers; and (3) Tumor-induced systemic immunosuppression[13]. It is particularly noteworthy that the Tokyo Guidelines 2018 recognize PBI as a critical precursor to biliary sepsis, emphasizing that delayed management may lead to rapid clinical deterioration[14]. Despite the fundamental fact that conventional logistic regression models have been applied for PBI prediction, their less favorable capacity to model complex nonlinear relationships constrains clinical predictive performance.
Traditional logistic regression models may fail to adequately capture complex nonlinear associations between dissimilar variables. Explosive evidence underscores the increasing clinical adoption of artificial neural network (ANN) models in medical practice[15,16]. Shao et al[17] put forward a standpoint that an ANN (area under the curve [AUC] = 0.954) outperformed logistic regression (AUC = 0.889) in predicting early occlusion in the aftermath of bilateral plastic stent placement for inoperable hilar cholangiocarcinoma. As a consequence, we employed an ANN model to address this gap and potentially improve prediction accuracy, which has demonstrated satisfactory performance in other clinical applications to predict PBI in patients suffering from MPHBO.
This multicenter retrospective study was predominantly intended to identify independent risk factors for PBI in those with MPHBO and to establish the first ANN-based predictive model for infection risk. We aimed to provide evidence-based strategies for personalized intervention and advance precision medicine in ERCP management, ultimately reducing infection-related morbidity and mortality.
MATERIALS AND METHODS
Study design and data collection
This retrospective and multicenter study was conducted across three endoscopy centers in China: The Department of Endoscopy, General Hospital of Northern Theater Command; The Department of Gastroenterology, Fourth Affiliated Hospital of China Medical University; And the Surgical Endoscopy Center, Northeast International Hospital.
Clinical data encompassing preoperative, perioperative, and postoperative follow-up variables (e.g., age, sex, total procedure time, and serum albumin) were extracted and entered into a dedicated database under the supervision of designated personnel. All patient information was handled confidentially. Written informed consent was obtained from all patients prior to ERCP. The study adhered to the Declaration of Helsinki and was approved by the Ethics Committee of the General Hospital of Northern Theater Command [Approval No. Y (2024) 336].
Patient recruitment and eligibility
Enrolled patients met the following criteria: (1) Aged 18 years or older; (2) Clinical or histopathological diagnosis of MPHBO; and (3) Successful ERCP treatment. Patients were excluded for the following reasons: (1) Biliary infection occurring before ERCP drainage; (2) Organ failure present before ERCP; (3) Any surgery performed within 1 month before ERCP; (4) Infections in other systems, such as pulmonary or gastrointestinal infections, occurring before the onset of PBI; and (5) Missing clinical data either before or 30 days after ERCP drainage. Between January 2014 and October 2024, 337 patients with MPHBO underwent 471 ERCP drainage procedures across three centers. After excluding 49 those suffering from 68 ERCPs, 288 individuals enduring 403 eligible ERCP clinical datasets were included in the study (Figure 1).
Figure 1 Flowchart of model patient enrollment.
From January 2014 to October 2024, 337 patients with malignant perihilar biliary obstruction (MPHBO) successfully received 471 endoscopic retrograde cholangiopancreatography (ERCP) drainage procedures for MPHBO in three centers. Nineteen patients suffering from 32 ERCPs had pre-procedural biliary infection (n = 32); three individuals suffering from 5 ERCPs (n = 5) had severe organ dysfunction before ERCP; two of those enduring 3 ERCPs (n = 3) had infections in other systems present before ERCP; twenty-five individuals suffering from 28 ERCPs (n = 28) had incomplete data. After 49 patients enduring 68 ERCPs were excluded, 288 individuals suffering from 403 eligible ERCP clinical data were enrolled in the study. ANN: Artificial neural network; LR: Logistic regression.
Diagnostic criteria for PBI
PBI encompasses early infectious complications arising within 30 days after biliary procedures, comprising cholangitis, cholecystitis, liver abscesses, and other infections associated with the biliary tract[18]. It is characterized by: (1) Biliary tract infection, with a temperature > 38 °C lasting for more than 24 hours, or a white blood cell count > 10.0 × 109/L, accompanied by increased epigastric pain and the exclusion of other systemic infections; and (2) Manifestations of cholestasis, comprising jaundice and abnormal liver function, as outlined in the 2018 Tokyo Guidelines[19,20].
Statistical analysis
Logistic regression model development: In the modeling group, all variables that exhibited statistical significance in the univariate analysis were included in a multivariate logistic regression model to identify independent risk factors for PBI. The multivariate results were predominantly employed to cultivate a logistic prediction model, and its performance was evaluated through internal and external validation by employing bootstrap resampling (1000 iterations). Parameter estimates, along with standard errors, odds ratios (ORs), and asymptotic 95% confidence intervals (CIs) for all significant variables, were obtained successively, and the final equation was applied to each case. The independent risk factors for PBI identified in the modeling group were subsequently employed to construct a predictive nomogram following regression modeling strategies in R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).
ANN model development: We utilized a basic multilayer perceptron consisting of three layers to predict PBI. The input layer included three predictors retained from the multivariable analysis, the hidden layer contained two units with a hyperbolic tangent (tanh) activation function and the output layer produced a binary classification by utilizing the SoftMax function to generate posterior class probabilities. The dataset was randomly divided into training and internal validation sets in a 70:30 ratio. Training was systematically conducted in batch mode by employing a conjugate-gradient optimizer, with an initial sigma of 0.00005 and an interval offset of ± 0.5; the maximum training time was set to 15 minutes. Early stopping was applied when the minimum relative change in the training error fell below 0.001. The model’s calibration was assessed by adopting the validation set.
All statistical analyses were performed by utilizing SPSS version 27.0 (IBM Corporation, Somers, NY, United States). Medians with interquartile ranges are used to describe continuous variables. Subsequent to the above steps, numbers and percentages are provided to summarize categorical variables. Data were analyzed by employing the Mann-Whitney U test for continuous variables and Pearson’s χ2 test or Fisher’s exact test for categorical variables. The predictive performance of both models was validated both internally and externally. The area under the receiver operating characteristic curve was utilized to assess how both prediction models perform. The standard curve was also adopted to validate how prediction is consistent with observation. P < 0.05 was considered statistically significant.
RESULTS
Baseline characteristics
This multicenter study analyzed clinical data from 288 patients with MPHBO who underwent 403 ERCP procedures across three centers. In particular, 158 patients (39.2%) developed PBI, with 47 cases (11.7%) classified as severe. Among the 403 ERCP procedures, 358 were assigned to the modeling group (143 cases of PBI, 39.9%) and 45 to the validation group (15 cases of PBI, 33.3%), which subsequently underwent randomization by employing R software. The demographic data of all patients underwent a thorough review prior to modeling. A comparison of baseline characteristics between the modeling and validation groups revealed no statistically significant differences (all P > 0.05; Table 1 and Supplementary Table 1).
Table 1 Baseline demographic and clinicopathological characteristics, n (%).
Prediction of risk factors and model development by logistic analysis
The univariate analysis identified chemotherapy history, antibiotic prophylaxis, hypokalemia, body mass index (BMI), absolute neutrophil count (ANC), neutrophil-lymphocyte ratio (NLR), total bilirubin, direct bilirubin, aspartate transaminase (AST), serum lipase levels, and Bismuth-Corlett classification as potential risk factors for PBI (all P < 0.05; Table 2 and Supplementary Table 2). Further inclusion of the univariate results in multivariate analysis suggested that the independent risk factors for PBI in patients with MPHBO were hypokalemia (OR = 4.080; 95%CI: 1.958-8.505; P < 0.001), Bismuth-Corlett classification (OR = 1.412; 95%CI: 1.144-1.743; P = 0.001), and AST (OR = 1.003; 95%CI: 1.000-1.006; P < 0.001) (Table 2). On the basis of these three factors, the identified model equation was Y = Logit P = -1.842 + 0.393 × Bismuth-Corlett classification + 1.491 × hypokalemia + 0.003 × AST (Supplementary Table 3). A PBI nomogram was established earlier to assess the risk of PBI in individuals with MPHBO who underwent ERCP (Figure 2).
Figure 2 Nomogram for postoperative biliary infection.
The nomogram illustrates the risk of postoperative biliary infection (PBI) in individuals suffering from malignant perihilar biliary obstruction who underwent endoscopic retrograde cholangiopancreatography. To apply it, the clinician identifies the patient’s value on each variable axis and draws a line upward to assign points for that variable. After summing the points across all variables, the total is located on the overall points axis. From there, a line is drawn downward to the risk scale to estimate the probability of PBI. In this nomogram, a patient with hypokalemia is shown as 1 and the case without is denoted as 0; in the Bismuth-Corlett classification, “3” denotes type IIIa, “4” symbolizes type IIIb and “5” defines type IV. AST: Aspartate transaminase.
Table 2 Univariate and multivariate logistic regression analyses of the modeling group.
The importance of each variable was as follows: Bismuth-Corlett classification (0.413; 100.0%), AST (0.307; 74.4%), and hypokalemia (0.280; 67.8%) (Supplementary Table 4 and Supplementary Figure 1). The ANN model identified the Bismuth-Corlett classification as the most influential among the three independent risk factors.
Validation and comparison of models
The logistic regression model was evaluated by employing the bootstrap method in conjunction with repeated sampling (1000 iterations). As evidenced by the experimental findings, the model displayed substantial discriminative capability, which was evidenced by an internal concordance index (C-index) of 0.734 (95%CI: 0.655-0.813) (Figure 3A) and an external C-index of 0.867 (95%CI: 0.812-0.922) (Figure 3B). On top of that, calibration plots suggested a trivial discrepancy between predicted probabilities and actual clinical outcomes, which further validated the reliable predictive performance of the logistic regression model (Supplementary Figure 2A and B).
Figure 3 Receiver operating characteristic curves for the prediction models.
A: Receiver operating characteristic (ROC) curve for the logistic regression (LR) modeling group; B: ROC curve for the LR validation group; C: ROC curve for the artificial neural network (ANN) modeling group; D: ROC curve for the ANN validation group. AUC: Area under the curve.
By contrast, the ANN model revealed striking discriminative power. To be more specific, it achieved an AUC of 0.791 (95%CI: 0.710-0.872) (Figure 3C) in the modeling set and a substantially higher AUC of 0.940 (95%CI: 0.928-0.952) (Figure 3D) in the external validation set. On top of that, the ANN model suggested favorable calibration characteristics which provided no evidence of overfitting (Supplementary Figure 2C and D). Collectively, these findings underline the robustness of the ANN model and its superior predictive accuracy in contrast to the logistic regression model (Table 3).
Table 3 Predictive performance metrics of the prediction models.
ERCP serves as the preferred palliative treatment for MPHBO, associated with high biliary drainage success rates, shorter hospitalizations, and enhanced quality of life[21]. Nevertheless, PBI represents a serious complication that can worsen patient outcomes and is potentially fatal. In this study, we analyzed clinical data from 403 patients with MPHBO who underwent ERCP and found a PBI incidence of 39.2%. Given the lack of predictive tools for PBI, we aimed to identify risk factors to develop a model for its early identification.
Univariate analysis identified several potential risk factors, comprising chemotherapy history, antibiotic prophylaxis, Bismuth-Corlett classification, hypokalemia, BMI, ANC, NLR, total and direct serum bilirubin levels, preoperative AST, and lipase. Multivariate analysis identified Bismuth-Corlett classification, preoperative AST, and hypokalemia as independent risk factors for PBI. Grounded in these factors, a clinical predictive model was developed to assist in managing high-risk patients.
The Bismuth-Corlett classification was proven to be the uppermost risk factor. Everett et al[22] arrived at the conclusion that higher grades were linked to a more remarkable risk of infection as a consequence of a more extensive tumor burden, local anatomical damage, and severe bile stasis. This complicates drainage, leading to insufficient biliary drainage during ERCP and creating a favorable environment for bacterial growth[23,24]. Notwithstanding a fundamental fact that the OR for AST is close to 1.000 (OR = 1.003), suggesting a particularly negligible effect size per unit increase, its consistent statistical significance across analyses demonstrates it may serve as a marker reflecting the degree of hepatic stress or impairment, which could indirectly influence susceptibility to infection. Apart from that, elevated serum AST levels could weaken immune function, which ultimately heightened the risk of infection[25,26]. Notably, this research pioneered the validation of hypokalemia as a novel risk factor, which illustrated the potential of electrolyte imbalance to bring about biliary infection. It is paramount to note that rare reports have uncovered this significant finding in the past. In line with the research by Heitzmann and Warth[27], hypokalemia may interfere with cellular metabolism and biliary smooth muscle contraction, which further gave rise to bile stasis and facilitated bacterial proliferation. Aside from that, malnutrition spawned from cholestasis can exacerbate potassium deficiency, ultimately forming a vicious cycle that renders post-ERCP patients more susceptible to biliary infections.
By incorporating additional infection cases in contrast to a stricter definition requiring bile culture results and imaging evidence, the definition of PBI was relatively broad in this study. Nevertheless, this broader definition also elevated the efficiency of the predictive model. These additional infection cases not only derived further validation from other clinical evidence, but also were classified as highly suspected infections. The incidence of PBI was 39.9% in the modeling group and 33.3% in the validation group. By adopting the identified risk factors, logistic regression and ANN models were developed to enable earlier and more accurate prediction of PBI occurrence. The logistic regression model is simple, intuitive, and easy to compute. More importantly, it has been extensively utilized in a diverse spectrum of studies[28,29]. The logistic regression model developed demonstrated moderate predictive ability, with an AUC of 0.734 in the modeling group and 0.791 in the validation group. The ANN model, nonetheless, outperformed the logistic regression model, with an AUC of 0.867 in the modeling group and 0.940 in the validation group. This phenomenon demonstrates its incomparable ability to handle complex interactions among risk factors.
Despite the aforementioned findings, this study is less satisfactory in some respects. First and foremost, despite the multicenter design improving representativeness, inter-institutional variations in clinical practices could influence risk estimation, encompassing antibiotic prophylaxis protocols, stent selection, and operator experience. The retrospective nature of this study prevented standardization or adjustment for all such variables, which may affect the external validity of our findings. It is preferable for future prospective studies to incorporate center-level adjustments to enhance generalizability. In addition, it is unusual to observe that the logistic regression model performed better in the external validation cohort (AUC = 0.867) than in the modeling cohort (AUC = 0.734). This discrepancy may be attributable to the relatively small size of the external validation cohort (n = 45), as well as the possibility that certain features in the validation cohort closely resemble those in the training set. While internal validation methods like bootstrapping were used for logistic regression and a hold-out set for ANN development, subsequent academic studies with larger validation samples are essential for the confirmation of stability and clinical applicability. In addition, although ANN models have displayed exceptional performance in a diverse spectrum of prognostic tasks[30-33], their application in clinical practice remains constrained by the “black-box” problem and substantial data requirements[34,35].
CONCLUSION
This multicenter retrospective study identified Bismuth-Corlette classification, preoperative AST, and hypokalemia as independent risk factors for PBI. The developed ANN model suggested encouraging predictive accuracy in comparison with logistic regression. What deserves mentioning here is that this tool could be advantageous for clinicians to identify high-risk patients in early clinical practices, thereby enabling timely interventions to improve outcomes.
ACKNOWLEDGEMENTS
We are deeply grateful to all staff members and collaborators for their participation in this study.
Footnotes
Provenance and peer review: Invited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade B
Novelty: Grade A, Grade C
Creativity or Innovation: Grade B, Grade C
Scientific Significance: Grade B, Grade C
P-Reviewer: Das S, MD, Assistant Professor, India; Tahtabasi M, MD, Associate Professor, Türkiye S-Editor: Fan M L-Editor: Filipodia P-Editor: Zhao YQ
Al Nakshabandi A, Ali FS, Albustami I, Hwang H, Qiao W, Johnston NC, Shaikh AS, Coronel E, Ge PS, Ross W, Weston B, Lee JH. Biliary drainage in hilar and perihilar cholangiocarcinoma: 25-year experience at a tertiary cancer center.Gastrointest Endosc. 2024;99:938-949.e15.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 5][Reference Citation Analysis (0)]
Nagino M, Hirano S, Yoshitomi H, Aoki T, Uesaka K, Unno M, Ebata T, Konishi M, Sano K, Shimada K, Shimizu H, Higuchi R, Wakai T, Isayama H, Okusaka T, Tsuyuguchi T, Hirooka Y, Furuse J, Maguchi H, Suzuki K, Yamazaki H, Kijima H, Yanagisawa A, Yoshida M, Yokoyama Y, Mizuno T, Endo I. Clinical practice guidelines for the management of biliary tract cancers 2019: The 3rd English edition.J Hepatobiliary Pancreat Sci. 2021;28:26-54.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 38][Cited by in RCA: 161][Article Influence: 32.2][Reference Citation Analysis (0)]
Kimura Y, Takada T, Strasberg SM, Pitt HA, Gouma DJ, Garden OJ, Büchler MW, Windsor JA, Mayumi T, Yoshida M, Miura F, Higuchi R, Gabata T, Hata J, Gomi H, Dervenis C, Lau WY, Belli G, Kim MH, Hilvano SC, Yamashita Y. TG13 current terminology, etiology, and epidemiology of acute cholangitis and cholecystitis.J Hepatobiliary Pancreat Sci. 2013;20:8-23.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 122][Cited by in RCA: 129][Article Influence: 10.8][Reference Citation Analysis (0)]
Mayumi T, Okamoto K, Takada T, Strasberg SM, Solomkin JS, Schlossberg D, Pitt HA, Yoshida M, Gomi H, Miura F, Garden OJ, Kiriyama S, Yokoe M, Endo I, Asbun HJ, Iwashita Y, Hibi T, Umezawa A, Suzuki K, Itoi T, Hata J, Han HS, Hwang TL, Dervenis C, Asai K, Mori Y, Huang WS, Belli G, Mukai S, Jagannath P, Cherqui D, Kozaka K, Baron TH, de Santibañes E, Higuchi R, Wada K, Gouma DJ, Deziel DJ, Liau KH, Wakabayashi G, Padbury R, Jonas E, Supe AN, Singh H, Gabata T, Chan ACW, Lau WY, Fan ST, Chen MF, Ker CG, Yoon YS, Choi IS, Kim MH, Yoon DS, Kitano S, Inomata M, Hirata K, Inui K, Sumiyama Y, Yamamoto M. Tokyo Guidelines 2018: management bundles for acute cholangitis and cholecystitis.J Hepatobiliary Pancreat Sci. 2018;25:96-100.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 176][Cited by in RCA: 174][Article Influence: 24.9][Reference Citation Analysis (0)]
Miura F, Okamoto K, Takada T, Strasberg SM, Asbun HJ, Pitt HA, Gomi H, Solomkin JS, Schlossberg D, Han HS, Kim MH, Hwang TL, Chen MF, Huang WS, Kiriyama S, Itoi T, Garden OJ, Liau KH, Horiguchi A, Liu KH, Su CH, Gouma DJ, Belli G, Dervenis C, Jagannath P, Chan ACW, Lau WY, Endo I, Suzuki K, Yoon YS, de Santibañes E, Giménez ME, Jonas E, Singh H, Honda G, Asai K, Mori Y, Wada K, Higuchi R, Watanabe M, Rikiyama T, Sata N, Kano N, Umezawa A, Mukai S, Tokumura H, Hata J, Kozaka K, Iwashita Y, Hibi T, Yokoe M, Kimura T, Kitano S, Inomata M, Hirata K, Sumiyama Y, Inui K, Yamamoto M. Tokyo Guidelines 2018: initial management of acute biliary infection and flowchart for acute cholangitis.J Hepatobiliary Pancreat Sci. 2018;25:31-40.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 318][Cited by in RCA: 279][Article Influence: 39.9][Reference Citation Analysis (0)]
Saillard C, Schmauch B, Laifa O, Moarii M, Toldo S, Zaslavskiy M, Pronier E, Laurent A, Amaddeo G, Regnault H, Sommacale D, Ziol M, Pawlotsky JM, Mulé S, Luciani A, Wainrib G, Clozel T, Courtiol P, Calderaro J. Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides.Hepatology. 2020;72:2000-2013.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 95][Cited by in RCA: 203][Article Influence: 40.6][Reference Citation Analysis (0)]
Kiriyama S, Kozaka K, Takada T, Strasberg SM, Pitt HA, Gabata T, Hata J, Liau KH, Miura F, Horiguchi A, Liu KH, Su CH, Wada K, Jagannath P, Itoi T, Gouma DJ, Mori Y, Mukai S, Giménez ME, Huang WS, Kim MH, Okamoto K, Belli G, Dervenis C, Chan ACW, Lau WY, Endo I, Gomi H, Yoshida M, Mayumi T, Baron TH, de Santibañes E, Teoh AYB, Hwang TL, Ker CG, Chen MF, Han HS, Yoon YS, Choi IS, Yoon DS, Higuchi R, Kitano S, Inomata M, Deziel DJ, Jonas E, Hirata K, Sumiyama Y, Inui K, Yamamoto M. Tokyo Guidelines 2018: diagnostic criteria and severity grading of acute cholangitis (with videos).J Hepatobiliary Pancreat Sci. 2018;25:17-30.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 468][Cited by in RCA: 466][Article Influence: 66.6][Reference Citation Analysis (0)]
Yokoe M, Hata J, Takada T, Strasberg SM, Asbun HJ, Wakabayashi G, Kozaka K, Endo I, Deziel DJ, Miura F, Okamoto K, Hwang TL, Huang WS, Ker CG, Chen MF, Han HS, Yoon YS, Choi IS, Yoon DS, Noguchi Y, Shikata S, Ukai T, Higuchi R, Gabata T, Mori Y, Iwashita Y, Hibi T, Jagannath P, Jonas E, Liau KH, Dervenis C, Gouma DJ, Cherqui D, Belli G, Garden OJ, Giménez ME, de Santibañes E, Suzuki K, Umezawa A, Supe AN, Pitt HA, Singh H, Chan ACW, Lau WY, Teoh AYB, Honda G, Sugioka A, Asai K, Gomi H, Itoi T, Kiriyama S, Yoshida M, Mayumi T, Matsumura N, Tokumura H, Kitano S, Hirata K, Inui K, Sumiyama Y, Yamamoto M. Tokyo Guidelines 2018: diagnostic criteria and severity grading of acute cholecystitis (with videos).J Hepatobiliary Pancreat Sci. 2018;25:41-54.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 769][Cited by in RCA: 748][Article Influence: 106.9][Reference Citation Analysis (0)]
Matsuda Y, Shimakura K, Akamatsu T. Factors affecting the patency of stents in malignant biliary obstructive disease: univariate and multivariate analysis.Am J Gastroenterol. 1991;86:843-849.
[PubMed] [DOI]
Lehmann-Werman R, Magenheim J, Moss J, Neiman D, Abraham O, Piyanzin S, Zemmour H, Fox I, Dor T, Grompe M, Landesberg G, Loza BL, Shaked A, Olthoff K, Glaser B, Shemer R, Dor Y. Monitoring liver damage using hepatocyte-specific methylation markers in cell-free circulating DNA.JCI Insight. 2018;3:e120687.
[RCA] [PubMed] [DOI] [Full Text][Cited by in Crossref: 61][Cited by in RCA: 96][Article Influence: 13.7][Reference Citation Analysis (0)]
Wang J, An H, Ding M, Liu Y, Wang S, Jin Q, Wu Q, Dong H, Guo Q, Tian X, Liu J, Zhang J, Zhu T, Li J, Shao Z, Briles DE, Veening JW, Zheng H, Zhang L, Zhang JR. Liver macrophages and sinusoidal endothelial cells execute vaccine-elicited capture of invasive bacteria.Sci Transl Med. 2023;15:eade0054.
[RCA] [PubMed] [DOI] [Full Text][Cited by in RCA: 10][Reference Citation Analysis (0)]
