1. Mira J.G., Fullerton G.D., Ezekiel J. Evaluation of computed tomography numbers for treatment planning of lung cancer // International Journal of Radiation Oncology, Biology, Physics. V. 8. - № 9. - 1982. - P. 1625-1628.
2. Bowden P., Fisher R., Mac Manus M. Measurement of lung tumor volumes using three-dimensional computer planning software // International Journal of Radiation Oncology, Biology, Physics. - V. 53 - № 3. - 2002. - P. 566-573.
3. Bradley J., Thorstad W.L., Mutic S. Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancerт // International Journal of Radiation Oncology, Biology, Physics. - V. 59. - № 1. - 2004. - P. 78-86.
4. Balogh J.B., Caldwell C.B., Ung Y.C. Interobserver variation in contourinig gross tumour volume in carcinoma of the lung associated with pneumonitis and atelectasis: The impact of 18FDG-hybrid pet fusion // International Journal of Radiation Oncology, Biology, Physics. - V. 48. - № 3. - 2000. - P. 128-129.
5. Venables W.N., Ripley B.D. Modern applied statistics with S, 4th edition. - Berlin: Springer, 2002. - 495 p.
6. Maindonald J., Braun J. Data analysis and graphics using R: an example-based approach. - Cambridge: Cambridge University Press, 2003. - 400 p.
7. Press W.H., Teukolsky S.A., Vetterling W.T. Numerical recipes in C: the art of scientific computing, 2nd edition. - Cambridge: Cambridge University Press, 2002. - 994 p.
8. Breiman L.J., Friedman H., Olshen R.A. Classification and regression trees. - Belmont: Chapman & Hall (reprinted by First CRC Press), 1984. - 368 p.
9. Therneau T.M., Atkinson B. Rpart: recursive partitioning in R, package version 3.1-22. - Rochester: Mayo Clinic, College of Medicine, Division of Biostatistics, 2005. - 28 p.
10. Cortes C., Vapnik V. Support-vector network // Machine Learning. - V. 20. - № 3. - 1995. - P. 273-297.
11. Bennett K.P., Campbell C. Support vector machines: Hype or Hallelujah? // ACM Special Interest Group on Knowledge Discovery and Data Mining, Explorations. - V. 2. - № 2. - 2000. - P. 1-13.
12. Dimitriadou E., Hornik K., Leisch F. E1071: Miscellaneous functions of the department of statistics, R package version 1.5-7. - TU Wien, Austria, 2005. - 58 p.
13. Shapire R., Freund Y., Bartlett P. Boosting the margin: A new explanation for the effectiveness of voting methods // The Annals of Statistics. - V. 26. - № 5. - 1998. - P. 1651-1686.
14. Breiman L. Bagging predictors // Machine Learning. - V. 24. - № 2. - 1996. - P. 123-140.
15. Wehrens R., Putter H., Buydens L.M.C. The bootstrap: a tutorial // Chemometrics and intelligent laboratory systems. - V. 54. - 2000. - P. 35-52.
16. Breiman L. Random Forests // Machine Learning. - V. 45. - № 1. - 2001. - P. 5-32.
17. Breiman L., Cutler A. Wiener M. randomForest: Breiman and Cutler's random forests for classification and regression, R package version 4.5-4, 2005. - 25 p.
18. Cox T.F., Cox M.A. Multidimensional scaling. - London: Chapman & Hall, 1994. - 213 p.
19. Cailliez F. The analytical solution of the additive constant problem // Psychometrika. - V. 48. - 1983. - P. 343-349.