Predicting radiotherapy outcomes using statistical learning techniques

Physics in Medicine and Biology

Volumn 54, Issue 18, 2009, Pages

  El Naqa, Issam ^a   Bradley, Jeffrey D ^a   Lindsay, Patricia E ^b   Hope, Andrew J ^b   Deasy, Joseph O ^a  

^a WASHINGTON UNIVERSITY   (United States)

^b PRINCESS MARGARET HOSPITAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BIVARIATE CORRELATIONS; CLINICAL PRACTICES; COMPLEX INTERACTION; CROSS VALIDATION; DATA SETS; ESOPHAGITIS; INTERACTION TERM; KERNEL METHODS; LEAVE-ONE-OUT; LOGISTIC REGRESSIONS; MODEL VARIABLES; NONLINEAR BEHAVIOR; NONLINEAR INTERACTIONS; NONLINEAR KERNELS; NONLINEAR RELATIONS; OVERFITTING; PRINCIPLE COMPONENTS ANALYSIS; PROGNOSTIC VARIABLES; RELATED VARIABLES; RESAMPLING; RESPONSE FUNCTIONS; STATISTICAL LEARNING; STATISTICAL LEARNING TECHNIQUES; SUPERVISED LEARNING PROBLEMS; TREATMENT RESPONSE; VARIABLE INTERACTION; XEROSTOMIA;

LEARNING ALGORITHMS; RADIOTHERAPY; SUPPORT VECTOR MACHINES;

SOCIETIES AND INSTITUTIONS;

ARTICLE; CANCER RADIOTHERAPY; CANCER RESEARCH; CONTROLLED STUDY; ESOPHAGITIS; HEAD AND NECK CANCER; HUMAN; KERNEL METHOD; LUNG CANCER; PNEUMONIA; PRIORITY JOURNAL; RISK FACTOR; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE; TREATMENT OUTCOME; TREATMENT RESPONSE; XEROSTOMIA;

ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; HEAD AND NECK NEOPLASMS; HUMANS; OUTCOME ASSESSMENT (HEALTH CARE); PATTERN RECOGNITION, AUTOMATED; PROPORTIONAL HAZARDS MODELS; RADIOTHERAPY, COMPUTER-ASSISTED; RADIOTHERAPY, CONFORMAL; TREATMENT OUTCOME;

EID: 71049175420     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/54/18/S02     Document Type: Article

References (44)

1. Armstrong K, Weber B, Ubel P A, Peters N, Holmes J and Schwartz J S 2005 Individualized survival curves improve satisfaction with cancer risk management decisions in women with BRCA1/2 mutations J. Clin. Oncol. 23 9319-28
2. Blanco A I, Chao K S, El Naqa I, Franklin G E, Zakarian K, Vicic M and Deasy J O 2005 Dose-volume modeling of salivary function in patients with head-and-neck cancer receiving radiotherapy Int. J. Radiat. Oncol. Biol. Phys. 62 1055-69
3. Bradley J, Deasy J O, Bentzen S and El Naqa I 2004 Dosimetric correlates for acute esophagitis in patients treated with radiotherapy for lung carcinoma Int. J. Radiat. Oncol. Biol. Phys. 58 1106-13
4. Bradley J, Graham M V, Winter K, Purdy J A, Komaki R, Roa W H, Ryu J K, Bosch W and Emami B 2005 Toxicity and outcome results of RTOG 9311: a phase I-II dose-escalation study using three-dimensional conformal radiotherapy in patients with inoperable non-small-cell lung carcinoma Int. J. Radiat. Oncol. Biol. Phys. 61 318-28
5. Bradley J D, Hope A, El Naqa I, Apte A, Lindsay P E, Bosch W, Matthews J, Sause W, Graham M V and Deasy J O 2007 A nomogram to predict radiation pneumonitis, derived from a combined analysis of RTOG 9311 and institutional data Int. J. Radiat. Oncol. Biol. Phys. 69 985-92
6. Brahme A 1999 Optimized radiation therapy based on radiobiological objectives Semin. Radiat. Oncol. 9 35-47
7. Chao K S, Deasy J O, Markman J, Haynie J, Perez C A, Purdy J A and Low D A 2001 A prospective study of salivary function sparing in patients with head-and-neck cancers receiving intensity-modulated or three-dimensional radiation therapy: initial results Int. J. Radiat. Oncol. Biol. Phys. 49 907-16
8. Chen Y, Hyrien O, Williams J, Okunieff P, Smudzin T and Rubin P 2005 Interleukin (IL)-1 A and IL-6: applications to the predictive diagnostic testing of radiation pneumonitis Int. J. Radiat. Oncol. Biol. Phys. 62 260-6
9. Dawson L A, Biersack M, Lockwood G, Eisbruch A, Lawrence T S and Ten Haken R K 2005 Use of principal component analysis to evaluate the partial organ tolerance of normal tissues to radiation Int. J. Radiat. Oncol. Biol. Phys. 62 829-37
10. Deasy J O, Blanco A I and Clark V H 2003 CERR: a computational environment for radiotherapy research Med. Phys. 30 979-85
11. Deasy J O and El Naqa I 2007 Radiation Oncology Advances ed M Mehta and S Bentzen (New York: Springer)
12. Deasy J O, Niemierko A, Herbert D, Yan D, Jackson A, Ten Haken R K, Langer M and Sapareto S 2002 Methodological issues in radiation dose-volume outcome analyses: summary of a joint AAPM/NIH workshop Med. Phys. 29 2109-27
13. de Crevoisier R, Tucker S L, Dong L, Mohan R, Cheung R, Cox J D and Kuban D A 2005 Increased risk of biochemical and local failure in patients with distended rectum on the planning CT for prostate cancer radiotherapy Int. J. Radiat. Oncol. Biol. Phys. 62 965-73
14. El Naqa I, Bradley J and Deasy J 2005 AAPM Symp. Proc. on Physical, Chemical and Biological Targeting in Radiation Oncology ed M Mehta et al (Madison, WI: Medical Physics Publishing) pp 150-9
15. El Naqa I, Bradley J D and Deasy J O 2008 Nonlinear kernel-based approaches for predicting normal tissue toxicities Machine Learning and Applications, 2008, ICMLA '08. 7th Int. Conf. on pp 539-44
16. El Naqa I, Bradley J D, Lindsay P E, Blanco A I, Vicic M, Hope A J and Deasy J O 2006 Multi-variable modeling of radiotherapy outcomes including dose-volume and clinical factors Int. J. Radiat. Oncol. Biol. Phys. 64 1275-86
17. El-Naqa I, Yang Y, Galatsanos N P, Nishikawa R M and Wernick M N 2004 A similarity learning approach to content-based image retrieval: application to digital mammography IEEE Trans. Med. Imaging 23 1233-44
18. El-Naqa I, Yang Y, Wernick M N, Galatsanos N P and Nishikawa R M 2002 A support vector machine approach for detection of microcalcifications IEEE Trans. Med. Imaging 21 1552-63
19. Elshaikh M, Ljungman M, Ten Haken R and Lichter A S 2006 Advances in radiation oncology Annu. Rev. Med. 57 19-31
20. Good P I 2006 Resampling Methods : A Practical Guide To Data Analysis (Boston, MA: Birkhèauser)
21. Gulliford S L, Webb S, Rowbottom C G, Corne D W and Dearnaley D P 2004 Use of artificial neural networks to predict biological outcomes for patients receiving radical radiotherapy of the prostate Radiother. Oncol. 71 3-12
22. Guyon I and Elissee A 2003 An Introduction to variable and feature selection J. Mach. Learn. Res. 3 1157-82
23. Guyon I, Weston J, Barnhill S and Vapnik V 2002 Gene selection for cancer classification using support vector machines Mach. Learn. 46 389-422
24. Härdle W and Simar L 2003 Applied Multivariate Statistical Analysis (Berlin: Springer)
25. Hastie T, Tibshirani R and Friedman J H 2001 The Elements of Statistical Learning: Data Mining, Inference, and Prediction (New York: Springer)
26. Haykin S 1999 Neural Networks: A Comprehensive Foundation (Englewood Cliffs, NJ: Prentice Hall)
27. Hope A J, Lindsay P E, El Naqa I, Bradley J D, Alaly J, Vicic M, Purdy J A and Deasy J O 2006 Radiation pneumonitis risk based on clinical, dosimetric, and location related factors Int. J. Radiat. Oncol. Biol. Phys. 65 112-24
28. Hope A J, Lindsay P E, El Naqa I, Bradley J D, Vicic M and Deasy J O 2005 Clinical, dosimetric, and location-related factors to predict local control in non-small cell lung cancer Astro 47th Ann. Meeting (Denver, CO) p S231
29. Kennedy R, Lee Y, Van Roy B, Reed C D and Lippman R P 1998 Solving Data Mining Problems Through Pattern Recognition (Englewood Cliffs, NJ: Prentice Hall)
30. Levegrun S, Jackson A, Zelefsky M J, Skwarchuk M W, Venkatraman E S, Schlegel W, Fuks Z, Leibel S A and Ling C C 2001 Fitting tumor control probability models to biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer: Pitfalls in deducing radiobiologic parameters for tumors from clinical data Int. J. Radiat. Oncol. Biol. Phys. 51 1064-80
31. Marks L B 2002 Dosimetric predictors of radiation-induced lung injury Int. J. Radiat. Oncol. Biol. Phys. 54 313-6
32. Matthews B W 1975 Comparison of the predicted and observed secondary structure of T4 phage lysozyme Biochim. Biophys. Acta 405 442-51
33. Moiseenko V, Kron T and Van Dyk J 2004 Biologically-based treatment plan optimization: a systematic comparison of NTCP models for tomotherapy treatment plans Proc. 14th Int. Conf. on the Use of Computers in Radiation Therapy (Seoul, Korea)
34. Munley M T, Lo J Y, Sibley G S, Bentel G C, Anscher M S and Marks L B 1999 A neural network to predict symptomatic lung injury Phys. Med. Biol. 44 2241-9
35. Pollack A, Cowen D, Troncoso P, Zagars G K, von Eschenbach A C, Meistrich M L and McDonnell T 2003 Molecular markers of outcome after radiotherapy in patients with prostate carcinoma: Ki-67, bcl-2, bax and bcl-x Cancer 97 1630-8
36. Rakotomamonjy A 2003 Variable selection using SVM-based criteria J. Mach. Learn. Res. 3 1357-70
37. Schèolkopf B and Smola A J 2002 Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Cambridge, MA: MIT Press)
38. Shawe-Taylor J and Cristianini N 2004 Kernel Methods for Pattern Analysis (Cambridge: Cambridge University Press)
39. Specht D F 1991 A general regression neural network IEEE Trans. Neural Netw. 2 568-76
40. Spencer S J, Bonnin D Almiron, Deasy J O, Bradley J D and El Naqa I 2009 Bioinformatics methods for learning radiation-induced lung inflammation from heterogeneous retrospective and prospective data J. Biomed. Biotechnol. 2009 892863
41. Su M, Miften M, Whiddon C, Sun X, Light K and Marks L 2005 An artificial neural network for predicting the incidence of radiation pneumonitis Med. Phys. 32 318-25
42. Vapnik V 1998 Statistical Learning Theory (New York: Wiley)
43. Webb S 1997 The Physics of Conformal Radiotherapy: Advances in Technology (Bristol: Institute of Physics Publishing)
44. Weinstein M C, Toy E L, Sandberg E A, Neumann P J, Evans J S, Kuntz K M, Graham J D and Hammitt J K 2001 Modeling for health care and other policy decisions: uses, roles, and validity Value Health 4 348-61