Making personalized predictions of poor outcome post resection of pancreatic ductal adenocarcinoma (PDAC): a prognostic bayesian network with pre- and post-operative application

Alison Bradley, Robert Van der Meer, Colin McKay, Nigel Jamieson

Research output: Contribution to journalMeeting abstract

1 Citation (Scopus)

Abstract

Background and Objectives:
The high-risk field of pancreatic cancer surgery, where surgical benefits are often nullified by early disease reoccurrence, mandates better patient selection for surgical intervention. Existing predictive models are limited in value and scope, relying heavily on post-operative information. The objective of this study was to combine PubMed and patient level data to create and validate a Bayesian Network that can make accurate personalized predictions of poor prognosis (12 months or less) post resection of PDAC preoperatively and perform prognostic updating postoperatively.

Materials and Methods:
A weighted Bayesian network, based on PubMed post-resection survival analysis studies (n=31,214), was created using AgenaRisk software. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology and adjuvant therapy. The model was validated against the database of a prospectively maintained tertiary referral centre (n=387).

Results:
For pre-operative predictions an Area Under the Curve (AUC) of 0.70 (P value: 0.001; 95% CI 0.589-0.801) was achieved accepting up to two missing data points in the pre-operative validation dataset. For prognostic updating an AUC 0.79 (P value: 0.000; 95% CI:0.710-0.870) was achieved when validated against a dataset that had up to 6 missing pre-operative data points but full post-operative data. This dropped to 0.72 (P value: 0.000; 95% CI:0.660-0.788) when the validation dataset had up to 6 missing pre-operative, and up to 3 missing post-operative data points.

Conclusion:
The Bayesian network presented here demonstrates a predictive performance rivalling existing models. Benefits over existing models include: pre-operative application, application to neoadjuvant and upfront surgery management pathways, and greater generalizability. As patient databases mature globally and our understanding of disease at genomic level deepens so too will the accuracy of predictions of this model with associated benefits at clinical level by supporting better shared decision making. The future application of this work will be to include emerging genomic data and combine this with clinical and pathological data to make personalized predictions of outcome, hence effectively creating a vehicle to deliver precision medicine.
LanguageEnglish
Article numberP6-13
PagesS122
Number of pages1
JournalPancreatology
Volume19
Issue numberS1
DOIs
Publication statusPublished - 5 Jun 2019
Event51st Annual Meeting of the European Pancreatic Club - Grieghallen, Bergen, Norway
Duration: 26 Jun 201929 Jun 2019
https://epc2019.no/welcome/

Fingerprint

Bayesian Networks
Adenocarcinoma
Tumor Biomarkers
PubMed
Area Under Curve
Prediction
Databases
Updating
Precision Medicine
Neoadjuvant Therapy
Survival Analysis
Pancreatic Neoplasms
Tertiary Care Centers
Patient Selection
Surgery
Genomics
Tumor
Decision Making
Software
Pathology

Keywords

  • pancreatic cancer
  • bayesian network
  • personalized medicine
  • prognostic model development
  • prognostic health management
  • personalized cancer management

Cite this

@article{2e12ab4d6da042528b7b46ab3a7b92e0,
title = "Making personalized predictions of poor outcome post resection of pancreatic ductal adenocarcinoma (PDAC): a prognostic bayesian network with pre- and post-operative application",
abstract = "Background and Objectives:The high-risk field of pancreatic cancer surgery, where surgical benefits are often nullified by early disease reoccurrence, mandates better patient selection for surgical intervention. Existing predictive models are limited in value and scope, relying heavily on post-operative information. The objective of this study was to combine PubMed and patient level data to create and validate a Bayesian Network that can make accurate personalized predictions of poor prognosis (12 months or less) post resection of PDAC preoperatively and perform prognostic updating postoperatively.Materials and Methods:A weighted Bayesian network, based on PubMed post-resection survival analysis studies (n=31,214), was created using AgenaRisk software. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology and adjuvant therapy. The model was validated against the database of a prospectively maintained tertiary referral centre (n=387).Results:For pre-operative predictions an Area Under the Curve (AUC) of 0.70 (P value: 0.001; 95{\%} CI 0.589-0.801) was achieved accepting up to two missing data points in the pre-operative validation dataset. For prognostic updating an AUC 0.79 (P value: 0.000; 95{\%} CI:0.710-0.870) was achieved when validated against a dataset that had up to 6 missing pre-operative data points but full post-operative data. This dropped to 0.72 (P value: 0.000; 95{\%} CI:0.660-0.788) when the validation dataset had up to 6 missing pre-operative, and up to 3 missing post-operative data points.Conclusion:The Bayesian network presented here demonstrates a predictive performance rivalling existing models. Benefits over existing models include: pre-operative application, application to neoadjuvant and upfront surgery management pathways, and greater generalizability. As patient databases mature globally and our understanding of disease at genomic level deepens so too will the accuracy of predictions of this model with associated benefits at clinical level by supporting better shared decision making. The future application of this work will be to include emerging genomic data and combine this with clinical and pathological data to make personalized predictions of outcome, hence effectively creating a vehicle to deliver precision medicine.",
keywords = "pancreatic cancer, bayesian network, personalized medicine, prognostic model development, prognostic health management, personalized cancer management",
author = "Alison Bradley and {Van der Meer}, Robert and Colin McKay and Nigel Jamieson",
year = "2019",
month = "6",
day = "5",
doi = "10.1016/j.pan.2019.05.326",
language = "English",
volume = "19",
pages = "S122",
number = "S1",

}

TY - JOUR

T1 - Making personalized predictions of poor outcome post resection of pancreatic ductal adenocarcinoma (PDAC)

T2 - a prognostic bayesian network with pre- and post-operative application

AU - Bradley, Alison

AU - Van der Meer, Robert

AU - McKay, Colin

AU - Jamieson, Nigel

PY - 2019/6/5

Y1 - 2019/6/5

N2 - Background and Objectives:The high-risk field of pancreatic cancer surgery, where surgical benefits are often nullified by early disease reoccurrence, mandates better patient selection for surgical intervention. Existing predictive models are limited in value and scope, relying heavily on post-operative information. The objective of this study was to combine PubMed and patient level data to create and validate a Bayesian Network that can make accurate personalized predictions of poor prognosis (12 months or less) post resection of PDAC preoperatively and perform prognostic updating postoperatively.Materials and Methods:A weighted Bayesian network, based on PubMed post-resection survival analysis studies (n=31,214), was created using AgenaRisk software. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology and adjuvant therapy. The model was validated against the database of a prospectively maintained tertiary referral centre (n=387).Results:For pre-operative predictions an Area Under the Curve (AUC) of 0.70 (P value: 0.001; 95% CI 0.589-0.801) was achieved accepting up to two missing data points in the pre-operative validation dataset. For prognostic updating an AUC 0.79 (P value: 0.000; 95% CI:0.710-0.870) was achieved when validated against a dataset that had up to 6 missing pre-operative data points but full post-operative data. This dropped to 0.72 (P value: 0.000; 95% CI:0.660-0.788) when the validation dataset had up to 6 missing pre-operative, and up to 3 missing post-operative data points.Conclusion:The Bayesian network presented here demonstrates a predictive performance rivalling existing models. Benefits over existing models include: pre-operative application, application to neoadjuvant and upfront surgery management pathways, and greater generalizability. As patient databases mature globally and our understanding of disease at genomic level deepens so too will the accuracy of predictions of this model with associated benefits at clinical level by supporting better shared decision making. The future application of this work will be to include emerging genomic data and combine this with clinical and pathological data to make personalized predictions of outcome, hence effectively creating a vehicle to deliver precision medicine.

AB - Background and Objectives:The high-risk field of pancreatic cancer surgery, where surgical benefits are often nullified by early disease reoccurrence, mandates better patient selection for surgical intervention. Existing predictive models are limited in value and scope, relying heavily on post-operative information. The objective of this study was to combine PubMed and patient level data to create and validate a Bayesian Network that can make accurate personalized predictions of poor prognosis (12 months or less) post resection of PDAC preoperatively and perform prognostic updating postoperatively.Materials and Methods:A weighted Bayesian network, based on PubMed post-resection survival analysis studies (n=31,214), was created using AgenaRisk software. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology and adjuvant therapy. The model was validated against the database of a prospectively maintained tertiary referral centre (n=387).Results:For pre-operative predictions an Area Under the Curve (AUC) of 0.70 (P value: 0.001; 95% CI 0.589-0.801) was achieved accepting up to two missing data points in the pre-operative validation dataset. For prognostic updating an AUC 0.79 (P value: 0.000; 95% CI:0.710-0.870) was achieved when validated against a dataset that had up to 6 missing pre-operative data points but full post-operative data. This dropped to 0.72 (P value: 0.000; 95% CI:0.660-0.788) when the validation dataset had up to 6 missing pre-operative, and up to 3 missing post-operative data points.Conclusion:The Bayesian network presented here demonstrates a predictive performance rivalling existing models. Benefits over existing models include: pre-operative application, application to neoadjuvant and upfront surgery management pathways, and greater generalizability. As patient databases mature globally and our understanding of disease at genomic level deepens so too will the accuracy of predictions of this model with associated benefits at clinical level by supporting better shared decision making. The future application of this work will be to include emerging genomic data and combine this with clinical and pathological data to make personalized predictions of outcome, hence effectively creating a vehicle to deliver precision medicine.

KW - pancreatic cancer

KW - bayesian network

KW - personalized medicine

KW - prognostic model development

KW - prognostic health management

KW - personalized cancer management

U2 - 10.1016/j.pan.2019.05.326

DO - 10.1016/j.pan.2019.05.326

M3 - Meeting abstract

VL - 19

SP - S122

IS - S1

M1 - P6-13

ER -