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

Decision support techniques Models, statistical Neoplasms

Journal

Annals of hepato-biliary-pancreatic surgery
ISSN: 2508-5859
Titre abrégé: Ann Hepatobiliary Pancreat Surg
Pays: Korea (South)
ID NLM: 101698342

Informations de publication

Date de publication:
28 Feb 2022
Historique:
received: 28 07 2021
accepted: 02 09 2021
entrez: 27 2 2022
pubmed: 28 2 2022
medline: 28 2 2022
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 35220286
pii: ahbps.21-113
doi: 10.14701/ahbps.21-113
pmc: PMC8901986
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

17-30

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Auteurs

Alexander W Coombs (AW)

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

Chloe Jordan (C)

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

Sabba A Hussain (SA)

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

Omar Ghandour (O)

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

Classifications MeSH