A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinoma

English version

Autoři: Alison Bradley ^aff001; Robert Van der Meer ^aff001; Colin J. McKay ^aff002
Působiště autorů: Department of Management Science, Strathclyde Business School, University of Strathclyde, Glasgow, Scotland, United Kingdom ^aff001; West of Scotland Pancreatic Cancer Unit, Glasgow Royal Infirmary, Glasgow, Scotland, United Kingdom ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222270

Souhrn

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Klíčová slova:

Biology and life sciences – Physical sciences – Research and analysis methods – Neuroscience – Cognitive science – Cognitive psychology – Psychology – Social sciences – Mathematics – Medicine and health sciences – Diagnostic medicine – Statistics – Mathematical and statistical techniques – Statistical methods – Clinical medicine – Oncology – Cancer treatment – Cancers and neoplasms – Cognition – Surgical and invasive medical procedures – Gastrointestinal tumors – Clinical oncology – Decision making – Surgical resection – Pancreatic cancer – Surgical oncology – Prognosis

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