Development and validation of a nomogram for patients with stage II/III gastric adenocarcinoma after radical surgery DOI Creative Commons
Lei Wang,

Huiqiong Han,

Liwen Feng

и другие.

Frontiers in Surgery, Год журнала: 2022, Номер 9

Опубликована: Окт. 31, 2022

Background We aimed to construct nomograms based on clinicopathological features and routine preoperative hematological indices predict cancer-specific survival (CSS) disease-free (DFS) in patients with stage II/III gastric adenocarcinoma (GA) after radical resection. Methods retrospectively analyzed 468 GA curative gastrectomy between 2012 2018; 70% of the were randomly assigned training set ( n = 327) rest validation 141). The nomogram was constructed from independent predictors derived Cox regression set. Using consistency index, calibration time-dependent receiver operating characteristic curves used evaluate accuracy nomogram. Decision curve analysis assess value model clinical applications. Patients further divided into low- high-risk groups risk score. Results Multivariate identified depth invasion, lymph node tumor differentiation, adjuvant chemotherapy, CA724, platelet-albumin ratio as covariates associated CSS DFS. CA199 is a factor unique CSS. using results multivariate showed high index 0.771 (DFS). Moreover, area under values for 3-and 5-year 0.868 0.918, corresponding DFS 0.872 0.919, respectively. had greater benefit than TNM staging system. High-risk worse prognosis low-risk patients. Conclusion prognostic established this study has good predictive ability, which helpful doctors accurately make more reasonable treatment plans.

Язык: Английский

Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study DOI Creative Commons
Jitao Wang,

Tianlei Zheng,

Yong Liao

и другие.

Frontiers in Oncology, Год журнала: 2022, Номер 12

Опубликована: Ноя. 2, 2022

Introduction Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods A total 875 HCC who underwent hepatectomy were randomized into training cohort (n=612), validation (n=88), testing (n=175). Shapley additive explanation (SHAP) was performed determine importance individual variables. By combining these independent risk factors, an ML PHLF established. The area under receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative decision analyses (DCA) used evaluate accuracy compare it that other noninvasive models. Results AUCs cohort, 0.944, 0.870, 0.822, respectively. had higher AUC than did non-invasive found be more valuable Conclusion prediction using common clinical parameters constructed validated. better existing models

Язык: Английский

Процитировано

18
Nomogram based on liver stiffness and spleen area with ultrasound for posthepatectomy liver failure: A multicenter study DOI

Cheng Guang-wen,

Fang Yan, Liyun Xue

и другие.

World Journal of Gastroenterology, Год журнала: 2024, Номер 30(27), С. 3314 - 3325

Опубликована: Июль 11, 2024

Liver stiffness (LS) measurement with two-dimensional shear wave elastography (2D-SWE) correlates the degree of liver fibrosis and thus indirectly reflects function reserve. The size spleen increases due to tissue proliferation, fibrosis, portal vein congestion, which can reflect situation fibrosis/cirrhosis. It was reported that related posthepatectomy failure (PHLF). So far, there has been no study combining 2D-SWE measurements LS predict PHLF. This prospective aimed investigate utility assessing area (SPA) for prediction PHLF in hepatocellular carcinoma (HCC) patients develop a risk model.

Язык: Английский

Процитировано

4
Realizing Textbook Outcomes Following Liver Resection for Hepatic Neoplasms with Development and Validation of a Predictive Nomogram DOI Creative Commons
Kaival Gundavda, Shraddha Patkar,

Sadhana Kannan

и другие.

Annals of Surgical Oncology, Год журнала: 2024, Номер 31(12), С. 7870 - 7881

Опубликована: Авг. 5, 2024

'Textbook Outcome' (TO) represents an effort to define a standardized, composite quality benchmark based on intraoperative and postoperative endpoints. This study aimed assess the applicability of TO as outcome measure following liver resection for hepatic neoplasms from low- middle-income economy determine its impact long-term survival. Based identified perioperative predictors, we developed validated nomogram-based scoring risk stratification system.

Язык: Английский

Процитировано

4
A comprehensive preoperative predictive score for post-hepatectomy liver failure after hepatocellular carcinoma resection based on patient comorbidities, tumor burden, and liver function: the CTF score DOI
Laura Alaimo, Yutaka Endo, Henrique A. Lima

и другие.

Journal of Gastrointestinal Surgery, Год журнала: 2022, Номер 26(12), С. 2486 - 2495

Опубликована: Сен. 13, 2022

Язык: Английский

Процитировано

15
Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review DOI Open Access
Xiao Wang, Miao Zhu, Junfeng Wang

и другие.

World Journal of Hepatology, Год журнала: 2025, Номер 17(4)

Опубликована: Апрель 25, 2025

Partial hepatectomy continues to be the primary treatment approach for liver tumors, and post-hepatectomy failure (PHLF) remains most critical life-threatening complication following surgery. To comprehensively review PHLF prognostic models developed in recent years objectively assess risk of bias these models. This followed Checklist Critical Appraisal Data Extraction Systematic Reviews Prediction Modelling Studies Preferred Reporting Items Meta-Analyses guideline. Three databases were searched from November 2019 December 2022, references as well cited literature all included studies manually screened March 2023. Based on defined inclusion criteria, articles selected, data extracted by two independent reviewers. The PROBAST was used evaluate quality each article. A total thirty-four met eligibility criteria analysis. Nearly (32/34, 94.1%) validated exclusively using private sources. Predictive variables categorized into five distinct types, with majority utilizing multiple types data. area under curve training ranged 0.697 0.956. Analytical issues resulted a high across included. validation performance existing substantially lower compared development All evaluated having bias, primarily due within analytical domain. progression modeling technology, particularly artificial intelligence modeling, necessitates use suitable assessment tools.

Язык: Английский

Процитировано

0
Predictive value of intra-hepatectomy ICGR15 of the remnant liver for post-hepatectomy liver failure in hemi-hepatectomy: a prospective study DOI Creative Commons

Tianyi Liang,

Yongfei He,

Shutian Mo

и другие.

BMC Cancer, Год журнала: 2025, Номер 25(1)

Опубликована: Май 16, 2025

Язык: Английский

Процитировано

0
Prediction of Posthepatectomy Liver Failure in Narrow Resection Margins HCC: A Model Based on Iodine Map Histogram Analysis of Nontumorous Liver Parenchyma DOI
Yuan Xu, Bo Liu, Fukai Li

и другие.

Academic Radiology, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Magnetic resonance-derived hepatic uptake index improves the identification of patients at risk of severe post-hepatectomy liver failure DOI Creative Commons
Wolf Bartholomä, Stefan Gilg, Peter Lundberg

и другие.

British journal of surgery, Год журнала: 2025, Номер 112(5)

Опубликована: Апрель 30, 2025

Abstract Background Post-hepatectomy liver failure (PHLF) is a leading cause of mortality after major resection. Accurate preoperative risk assessment essential, yet current methods have limitations. Gadoxetic acid-enhanced MRI (Gd-EOB MRI) enables both morphological and functional evaluation the liver. The aim this study was to evaluate efficacy hepatic uptake index (HUI) obtained from routine Gd-EOB for identifying patients at severe PHLF. Methods This observational retrospective multicentre included 292 who underwent hepatectomy between 2010 2020 in Sweden, Denmark, Finland. Preoperative performed each patient HUI, standardized future remnant (sFLR-HUI), Model End-Stage Liver Disease Version 3 (MELD 3) score were evaluated. Statistical analyses logistic regression receiver operating characteristic (ROC) curve determine cut-off values discriminative accuracies PHLF (International Study Group Surgery grades B C). Results Among patients, 25 (8.6%) developed Patients with had significantly lower HUI sFLR-HUI (P < 0.001). demonstrated superior performance (area under (AUC) 0.758) compared volume-only assessments, such as (sFLR) (AUC 0.628). Combining MELD improved further 0.803). Conclusion outperforms volume-based biomarkers identification Incorporating image-derived independent biomarkers, score, may optimize stratification improve outcomes hepatectomy.

Язык: Английский

Процитировано

0
A special type of liver failure after partial hepatectomy: a case report DOI Creative Commons

Linxiao Gao,

Bing Tu,

Mingxiang Cheng

и другие.

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Июнь 2, 2025

Posthepatectomy liver failure (PHLF) is one of the most harmful complications after resection. Here, we report a case specific type PHLF in 60-year-old man with hepatocellular carcinoma. The patient developed extensive necrosis accompanied by further deterioration function and coagulation on eighth postoperative day. After being treated protection, circulation improvement, plasma infusion, anti-infective therapy, his bilirubin level still increased progressively, renal deteriorated anuria. Finally, patient’s family discontinued treatment. This highlights importance timely identification management this special PHLF.

Язык: Английский

Процитировано

0
A Novel Nomogram for Predicting Postoperative Liver Failure After Major Hepatectomy for Hepatocellular Carcinoma DOI Creative Commons
Zhengqing Lei, Nuo Cheng,

Anfeng Si

и другие.

Frontiers in Oncology, Год журнала: 2022, Номер 12

Опубликована: Март 14, 2022

Post-hepatectomy liver failure (PHLF) is the most common cause of mortality after major hepatectomy in hepatocellular carcinoma (HCC) patients. We aim to develop a nomogram preoperatively predict grade B/C PHLF defined by International Study Group on Liver Surgery Grading (ISGLS) HCC patients undergoing hepatectomy.The consecutive who underwent at Eastern Hepatobiliary Hospital between 2008 and 2013 served as training cohort preoperative nomogram, from 2 other hospitals comprised an external validation cohort. Least absolute shrinkage selection operator (LASSO) logistic regression was applied identify predictors PHLF. Multivariable utilized establish model. Internal validations were used verify performance nomogram. The accuracy also compared with conventional scoring models, including MELD ALBI score.A total 880 (668 192 cohort) enrolled this study. independent risk factors age, gender, prothrombin time, bilirubin, CSPH, which incorporated into Good prediction discrimination achieved (AUROC: 0.73) 0.72) cohorts. calibration curve showed good agreement both has better than score models.The proposed more accurate ability individually scores.

Язык: Английский

Процитировано

11