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肿瘤性肝胰胆疾病患者管理的评分系统

Scoring systems for the management of oncological hepato-pancreato-biliary patients.

作者信息

Coombs Alexander W, Jordan Chloe, Hussain Sabba A, Ghandour Omar

机构信息

Department of Surgery and Cancer, Imperial College London, London, United Kingdom.

出版信息

Ann Hepatobiliary Pancreat Surg. 2022 Feb 28;26(1):17-30. doi: 10.14701/ahbps.21-113.

Abstract

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

摘要

手术中的肿瘤学评分系统用作循证决策辅助工具,通过评估预后、有效性和复发情况来为最佳管理提供支持。目前,由于对准确性和适用性的担忧阻碍了其在临床中的广泛应用,肝胰胆(HPB)领域评分系统的使用受到限制。本综述的目的是讨论用于HPB患者手术管理的临床有用的肿瘤学评分系统。进行了一项叙述性综述以评估HPB肿瘤学评分系统。使用谷歌学术、PubMed、Cochrane和Ovid Medline搜索已建立和新型评分系统的原始研究文章。入选模型由作者确定。本综述讨论了肝脏癌症(CLIP、BCLC、ALBI分级、RETREAT、Fong评分)、胰腺(Genç评分、mGPS)和胆道(TMHSS、MEGNA)的九种评分系统。八个模型仅使用客观测量来计算其分数,而一个模型使用主观和客观输入的混合。七个模型在外部人群中评估了其评分性能,报告的鉴别c统计量范围为0.58至0.82。模型变量的选择最常通过单变量和多变量分析的组合来确定。校准是模型准确性的另一个决定因素,在九种评分系统中报告较少。各种各样的HPB手术评分系统可能有助于在患者管理和治疗方面做出循证决策。未来的评分系统需要使用分层改善的异质患者队列来开发,未来的趋势是整合机器学习和遗传学以改善结果预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57be/8901986/60bbd2748bbf/ahbps-26-1-17-f1.jpg

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