Preoperative prediction of gastric cancer T-staging based on ordinal regression models
https://doi.org/10.37661/1816-0301-2024-21-2-36-53
Abstract
Objectives. Study of ordinal regressions presented via the set of binary logistic regressions and their application in clinical practice for T-staging of gastric cancer.
Methods. Methods of ordinal regression statistical models, model performance assessment, and survival analysis were used.
Results. Basic ordinal regression models have been studied and applied to the clinical data of gastric cancer. Some clinical predictors have been added to the well-known prognostic criteria according to the TNM classification in the multifactor regression model, results seem appropriate for a personalized approach when planning the treatment volume for improving efficacy.
Conclusion. The study showed that the analysis of ordinal models, along with multinomial ones, provides additional information that helps to understand the behavior of the latent variable in the complex cancer processes. The clinical part of the study facilitates a differentiated approach to preoperative planning of the treatment volume for patients with the same T-stage, based on modeling results.
About the Authors
O. V. Krasko
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus
Belarus
Olga V. Krasko, Ph. D. (Eng.), Assoc. Prof., Leading Researcher
st. Surganova, 6, Minsk, 220012
M. Yu. Reutovich
Belarusian State Medical University
Belarus
Belarus
Mikhail Yu. Reutovich, D. Sc. (Med.), Assoc. Prof., Dean of the Faculty of General Medicine
av. Dzerzhinsky, 83, Minsk, 220083
A. L. Patseika
N. N. Alexandrov National Cancer Centre of Belarus
Belarus
Belarus
Aliaksandr I. Patseika, Surgical Oncologist, Oncology Division of Gastroesophageal Abnormalities
Lesnoy, Minsk District, 223040
References
1. Stevens S. S. On the theory of scales of measurement //Science. – 1946. – Т. 103. – №. 2684. – С. 677-680.
2. Agresti A. Modelling ordered categorical data: recent advances and future challenges //Statistics in medicine. – 1999. – Т. 18. – №. 17‐18. – С. 2191-2207.
3. McCullagh P. Regression models for ordinal data //Journal of the Royal Statistical Society: Series B (Methodological). – 1980. – Т. 42. – №. 2. – С. 109-127.
4. Agresti A. Analysis of ordinal categorical data. – John Wiley & Sons, 2010. – Т. 656.
5. McCullagh P. Proportional odds model: Theoretical background //Wiley StatsRef: Statistics Reference Online. – 2014.
6. Thompson Jr W. A. On the treatment of grouped observations in life studies //Biometrics. – 1977. – С. 463-470.
7. Cox D. R. Regression models and life‐tables //Journal of the Royal Statistical Society: Series B (Methodological). – 1972. – Т. 34. – №. 2. – С. 187-202.
8. McCullagh P. Regression models for ordinal data //Journal of the Royal Statistical Society: Series B (Methodological). – 1980. – Т. 42. – №. 2. – С. 109-127.
9. Fienberg S. E. The analysis of cross-classified categorical data. – Springer Science & Business Media, 2007.
10. Powers D., Xie Y. Statistical methods for categorical data analysis. – Emerald Group Publishing, 2008.
11. Fullerton A. S., Xu J. Constrained and unconstrained partial adjacent category logit models for ordinal response variables //Sociological Methods & Research. – 2018. – Т. 47. – №. 2. – С. 169-206. ]
12. Anderson J. A. Regression and ordered categorical variables //Journal of the Royal Statistical Society: Series B (Methodological). – 1984. – Т. 46. – №. 1. – С. 1-22.
13. Fernandez D., Liu I., Costilla R. A method for ordinal outcomes: The ordered stereotype model //International journal of methods in psychiatric research. – 2019. – Т. 28. – №. 4. – С. e1801.
14. Long J. S., Cheng S. Regression models for categorical outcomes //Handbook of data analysis. – 2004. – С. 259-284.
15. Hilbe J. M. Logistic regression models. – CRC press, 2009.
16. Hauser R. M., Andrew M. 1. Another look at the stratification of educational transitions: the logistic response model with partial proportionality constraints //Sociological Methodology. – 2006. – Т. 36. – №. 1. – С. 1-26.
17. Tutz G. Ordinal regression: A review arn a taxoromy of models //Wiley Interdisciplinary Reviews: Computational Statistics. – 2022. – Т. 14. – №. 2. – С. e1545.
18. Hosmer D. W., Lemeshow S., Cook E. Applied logistic regression 2nd edition //New York: Jhon Wiley and Sons Inc. – 2000.
19. Lipsitz S. R., Fitzmaurice G. M., Molenberghs G. Goodness-of-fit tests for ordinal response regression models //Journal of the Royal Statistical Society Series C: Applied Statistics. – 1996. – Т. 45. – №. 2. – С. 175-190.
20. Pulkstenis E., Robinson T. J. Goodness‐of‐fit tests for ordinal response regression models //Statistics in medicine. – 2004. – Т. 23. – №. 6. – С. 999-1014.
21. Fagerland M. W., Hosmer D. W. Tests for goodness of fit in ordinal logistic regression models //Journal of Statistical Computation and Simulation. – 2016. – Т. 86. – №. 17. – С. 3398-3418.
22. Tharwat A. Classification assessment methods //Applied computing and informatics. – 2020. – Т. 17. – №. 1. – С. 168-192.
23. Japanese Gastric Cancer Association. Japanese Gastric Cancer Treatment Guidelines 2021 //Gastric cancer: official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association. – 2023. – Т. 26. – №. 1. – С. 1-25.
24. Götze T. O. et al. Preventive HIPEC in combination with perioperative FLOT versus FLOT alone for resectable diffuse type gastric and gastroesophageal junction type II/III adenocarcinoma–the phase III “PREVENT”-(FLOT9) trial of the AIO/CAOGI/ACO //BMC cancer. – 2021. – Т. 21. – №. 1. – С. 1-9.
25. Cohen J. A coefficient of agreement for nominal scales //Educational and psychological measurement. – 1960. – Т. 20. – №. 1. – С. 37-46.
26. Lin M. et al. Effect of Preoperative Tumor Under-Staging on the Long-term Survival of Patients Undergoing Radical Gastrectomy for Gastric Cancer //Cancer Research and Treatment: Official Journal of Korean Cancer Association. – 2021. – Т. 53. – №. 4. – С. 1123-1133.
27. Mcfadden D. Conditional Logit Analysis of Qualitative Choice Behavior //Frontier in Econometrics. – 1973.
28. Rosa F. et al. Classification of nodal stations in gastric cancer //Translational gastroenterology and hepatology. – 2017. – Т. 2.
29. Dai N. et al. Expression of phosphatase regenerating liver 3 is an independent prognostic indicator for gastric cancer //World journal of gastroenterology. – 2009. – Т. 15. – №. 12. – С. 1499-1505.
30. Kim E. Y. et al. The platelet-to-lymphocyte ratio versus neutrophil-to-lymphocyte ratio: which is better as a prognostic factor in gastric cancer? //Annals of surgical oncology. – 2015. – Т. 22. – С. 4363-4370.
Review
For citations:
Krasko O.V., Reutovich M.Yu., Patseika A.L. Preoperative prediction of gastric cancer T-staging based on ordinal regression models. Informatics. 2024;21(2):36-53. (In Russ.) https://doi.org/10.37661/1816-0301-2024-21-2-36-53
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