Abstract
Background: Pancreatic cancer staging is used to predict prognosis accurately in early stages of disease, however, stage IV with fewer treatment options, is harder to define an accurate life expectancy. Various machine learning methods have been used to improve predictive accuracy of survival. Markov chains are another way to mathematically model a sequence of probability vectors or eigenvalues and could provide a simple yet accurate method for predicting pancreatic cancer survival. The aim of this investigation was to use matrices, eigenvalues and Markov chains to predict survival rates in pancreatic cancer patients based on stage, particularly stage IV.
Methods: Matrices and eigenvalues/eigenvectors were used to create transition coefficients that were subsequently feed into the Markov chain and modelling. Outcomes were compared to literature values and decision tree analysis.
Results: For all pancreatic patients, 4-week survival is 85% at the time of diagnosis using Markov modelling. The Markov modelling revealed that, for those with advanced disease (stage IV) at presentation, 4-week mortality is 13.3% for those where treatment is undertaken and 21.3% where treatment is not an option. Matched pairs t-test revealed that Markov modelling had a 0.798 correlation coefficient compared to decision tree analysis (R 2=0.637) and similarly 0.804 with the published literature (R 2=0.647).
Conclusions: The decision tree analysis provided modelling of survival at a more granular level and as a result, would be more suitable than Markov modelling or current models based on regression analysis for predicting survival for patients and their families.
Original language | English |
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Article number | 7769 |
Pages (from-to) | 1-15 |
Number of pages | 15 |
Journal | Annals of Pancreatic Cancer |
Volume | 7 |
DOIs | |
Publication status | Published - 31 Aug 2024 |