Statistical validation of normal tissue complication probability models

International Journal of Radiation Oncology Biology Physics

Volumn 84, Issue 1, 2012, Pages

  Xu, Cheng Jian ^a   Van Der Schaaf, Arjen ^a   Van'T Veld, Aart A ^a   Langendijk, Johannes A ^a   Schilstra, Cornelis ^a,b  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^b Radiotherapy Institute Friesland   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CLINICAL USE; CROSS VALIDATION; HEAD-AND-NECK CANCER; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATORS; LIKELIHOOD FUNCTIONS; MODEL ASSESSMENT; MODEL PERFORMANCE; NORMAL-TISSUE COMPLICATION PROBABILITY MODELS; PERMUTATION TESTING; PERMUTATION TESTS; RADIATION THERAPY TREATMENT; RECEIVER OPERATING CHARACTERISTIC CURVES; REGRESSION METHOD; STATISTICAL APPROACH; STATISTICAL SIGNIFICANCE; STATISTICAL VALIDATION; XEROSTOMIA;

ONCOLOGY; RADIOLOGY;

TISSUE;

ARTICLE; CONTROLLED STUDY; HEAD AND NECK CANCER; HUMAN; MAJOR CLINICAL STUDY; NORMAL TISSUE COMPLICATION PROBABILITY MODEL; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; REGRESSION ANALYSIS; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; XEROSTOMIA;

ALGORITHMS; AREA UNDER CURVE; HEAD AND NECK NEOPLASMS; HUMANS; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; ORGANS AT RISK; PAROTID GLAND; RADIOTHERAPY, CONFORMAL; REGRESSION ANALYSIS; SUBMANDIBULAR GLAND; UNCERTAINTY; XEROSTOMIA;

EID: 84865674669     PISSN: 03603016     EISSN: 1879355X     Source Type: Journal    
DOI: 10.1016/j.ijrobp.2012.02.022     Document Type: Article

References (18)

1. L.B. Marks, E.D. Yorke, and A. Jackson Use of normal tissue complication probability models in the clinic Int J Radiat Oncol Biol Phys 76 2010 S10 S19
2. J.O. Deasy, and I. El Naqa Image-based modeling of normal tissue complication probability for radiation therapy Cancer Treat Res 139 2008 211 252
3. C. Dehing-Oberije, D. De Ruysscher, and S. Petit Development, external validation and clinical usefulness of a practical prediction model for radiation-induced dysphagia in lung cancer patients Radiother Oncol 97 2010 455 461
4. I. El Naqa, J. Bradley, and A.I. Blanco Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors more option Int J Radiat Oncol Biol Phys 64 2006 1275 1286
5. S.K. Das, S. Zhou, and J. Zhang Predicting lung radiotherapy induced pneumonitis using a model combining parametric Lyman probit with nonparametric decision trees Int J Radiat Oncol Biol Phys 68 2007 1212 1221
6. A.G.T.M. Egelmeera, E.R. Velazqueza, and J.M.A. de Jonga Development and validation of a nomogram for prediction of survival and local control in laryngeal carcinoma patients treated with radiotherapy alone: a cohort study based on 994 patients Radiother Oncol 100 2011 108 115
7. K. De Ruyck, N. Sabbe, and C. Oberije Development of a multicomponent prediction model for acute esophagitis in lung cancer patients receiving chemooradiotherapy Int J Radiat Oncol Biol Phys 81 2011 537 544
8. C.J. Xu, A. van der Schaaf, and C. Schilstra Impact of learning methods on the predictive power of multivariate normal tissue complication probability models Int J Radiat Oncol Biol Phys 82 2012 e677 e684
9. C.M. Rubingh, S. Bijlsma, and E.P.P.A. Derks Assessing the performance of statistical validation tools for megavariate metabolomics data Metabolomics 2 2006 53 61
10. T. Hastie, R. Tibshirani, and J.H. Friedman The Elements of Statistical Learning: Data Mining, Inference and Prediction 2nd ed. 2009 Springer New York
11. M. Stone Cross-validatory choice and assessment of statistical predictions J R Stat Soc B 36 1974 111 147
12. J.A. Westerhuis, H.C.J. Hoefsloot, and S. Smit Assessment of PLSDA cross validation Metabolomics 4 2008 81 89
13. P.I. Good Permutation Tests: A Practical Guide to Resampling, Methods for Testing Hypotheses 2000 Springer New York
14. J.A. Langendijk, P. Doornaert, and I.M. Verdonck-de Leeuw Impact of late treatment-related toxicity on quality of life among patients with head and neck cancer treated with radiotherapy J Clin Oncol 22 2008 3770 3776
15. R. Tibshirani Regression shrinkage and selection via the lasso J Royal Stat Soc B 58 1996 267 288
16. J. Friedman, T. Hastie, and R. Tibshirani Regularization paths for generalized linear models via coordinate descent J Stat Softw 33 2010 1 22
17. L. Breiman Bagging predictors Mach Learn 24 1996 123 140
18. C.J. Xu, H.C.J. Hoefsloot, and A.K. Smilde To aggregate or not to aggregate high-dimensional classifier BMC bioinformatics 12 2011 153