Abstract
Purpose/Background:
Pancreatic cancer accounts for 2% of all cancers but responsible for 5% of cancer deaths and even with treatment, relapse is common and 5-year survival is poor. Staging is used to predict prognosis, however, stage IV with fewer treatment options, is harder to define an accurate life expectancy. Various machine learning methods and artificial intelligence algorithms 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.
Methods:
The aim of this investigation was to use Markov chains to predict survival rates in pancreatic cancer patients based on stage, particularly stage IV. 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 (R2 = 0.637) and similarly 0.804 with the published literature (R2 = 0.647).
Conclusion:
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.
Pancreatic cancer accounts for 2% of all cancers but responsible for 5% of cancer deaths and even with treatment, relapse is common and 5-year survival is poor. Staging is used to predict prognosis, however, stage IV with fewer treatment options, is harder to define an accurate life expectancy. Various machine learning methods and artificial intelligence algorithms 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.
Methods:
The aim of this investigation was to use Markov chains to predict survival rates in pancreatic cancer patients based on stage, particularly stage IV. 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 (R2 = 0.637) and similarly 0.804 with the published literature (R2 = 0.647).
Conclusion:
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 |
|---|---|
| Publication status | Published - 2024 |
| Event | 2024 SNMMI Annual Meeting - Metro Toronto Convention Centre, Toronto, Canada Duration: 08 Jun 2024 → 11 Jun 2024 https://web.archive.org/web/20240611155505/https://sites.snmmi.org/SNMMI-AM/Home.aspx?hkey=2f9701b4-43dd-4988-9b11-d5f2d3bc118c&WebsiteKey=2ac95882-cb13-4b7d-aabc-6f7c0ef60252 (Meeting website) |
Conference
| Conference | 2024 SNMMI Annual Meeting |
|---|---|
| Country/Territory | Canada |
| City | Toronto |
| Period | 08/06/24 → 11/06/24 |
| Other | The premier educational, scientific, research, and networking event in nuclear medicine and molecular imaging, the SNMMI Annual Meeting provides physicians, technologists, pharmacists, laboratory professionals, and scientists with an in-depth view of the latest research and development in the field as well as providing insights into practical applications for the clinic. |
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