Projects per year
The narrative surrounding the management of potentially resectable pancreatic cancer is complex. Surgical resection is the only potentially curative treatment. However resection rates are low, the risk of operative morbidity and mortality are high, and survival outcomes remain poor. The aim of this study was to create a prognostic Bayesian network that pre-operatively makes personalized predictions of post-resection survival time of 12months or less and also performs post-operative prognostic updating.
A Bayesian network was created by synthesizing data from PubMed post-resection survival analysis studies through a two-stage weighting process. 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 results and adjuvant therapy.
77 studies (n = 31,214) were used to create the Bayesian network, which was validated against a prospectively maintained tertiary referral centre database (n = 387). For pre-operative predictions an Area Under the Curve (AUC) of 0.7 (P value: 0.001; 95% CI 0.589–0.801) was achieved accepting up to 4 missing data-points in the dataset. For prognostic updating an AUC 0.8 (P value: 0.000; 95% CI:0.710–0.870) was achieved when validated against a dataset with up to 6 missing pre-operative, and 0 missing post-operative data-points. This dropped to AUC: 0.7 (P value: 0.000; 95% CI:0.667–0.818) when the post-operative validation dataset had up to 2 missing data-points.
This Bayesian network is currently unique in the way it utilizes PubMed and patient level data to translate the existing empirical evidence surrounding potentially resectable pancreatic cancer to make personalized prognostic predictions. We believe such a tool is vital in facilitating better shared decision-making in clinical practice and could be further developed to offer a vehicle for delivering personalized precision medicine in the future.
- pancreatic cancer
- pancreatic neoplasms
- neoadjuvant therapy
- personalized cancer management
- personalized medicine
- prognostic health management
- prognostic model development
A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancerBradley, A., Van Der Meer, R. & McKay, C. J., 21 Aug 2019, In : BMJ Open. 9, 8, 9 p., e027192.
Research output: Contribution to journal › ArticleOpen AccessFile1 Citation (Scopus)3 Downloads (Pure)
Making personalized predictions of poor outcome post resection of pancreatic ductal adenocarcinoma (PDAC): a prognostic bayesian network with pre- and post-operative applicationBradley, A., Van der Meer, R., McKay, C. & Jamieson, N., 5 Jun 2019, In : Pancreatology. 19, S1, p. S122 1 p., P6-13.
Research output: Contribution to journal › Meeting abstractOpen AccessFile2 Citations (Scopus)2 Downloads (Pure)
Neoadjuvant therapy versus upfront surgery for potentially resectable pancreatic cancer: a Markov decision analysisBradley, A. & Van Der Meer, R., 28 Feb 2019, In : PLoS ONE. 14, 2, 20 p., e0212805.
Research output: Contribution to journal › ArticleOpen AccessFile3 Citations (Scopus)46 Downloads (Pure)
27 Mar 2019 → 29 Mar 2019
Activity: Participating in or organising an event types › Participation in conference
Gillian Hopkins Anderson (Organiser), Itamar Megiddo (Organiser), Dominic Finn (Organiser), Robert van der Meer (Organiser), John Connaghan (Keynote/plenary speaker), Alan Hunter (Keynote/plenary speaker), Alison Bradley (Invited speaker), Abigail Colson (Invited speaker), Johanna McQuarrie (Invited speaker), Roma Maguire (Invited speaker), Natalie Mcfadyen Weir (Invited speaker), Paul J Jenkins (Speaker), Claire Fernie (Speaker) & Fahim Ahmed (Participant)3 May 2018
Activity: Participating in or organising an event types › Organiser of special symposia