Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

Abstract:

:This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed, and Cochrane Database were undertaken. Studies were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1) and artificial neural network (n = 1), and one study explored machine learning algorithms including Bayesian network, decision trees, k-nearest neighbor, and artificial neural networks. The main methodological issues identified were limited data sources, which limits generalizability and potentiates bias; lack of external validation; and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision-making.

journal_name

Pancreas

journal_title

Pancreas

authors

Bradley A,van der Meer R,McKay C

doi

10.1097/MPA.0000000000001312

subject

Has Abstract

pub_date

2019-05-01 00:00:00

pages

598-604

issue

5

eissn

0885-3177

issn

1536-4828

pii

00006676-201905000-00003

journal_volume

48

pub_type

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