title: Predicting radiotherapy outcomes using statistical learning techniques.
abstract: Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data.
authors.affiliation: Washington University, Saint Louis, MO, USA.
authors.forename: Issam
authors.initials: I
authors.lastname: El Naqa
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Bradley
authors.forename: Patricia E
authors.initials: PE
authors.lastname: Lindsay
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Hope
authors.forename: Joseph O
authors.initials: JO
authors.lastname: Deasy
mesh_terms: D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D006258:Head and Neck Neoplasms / Q000453:epidemiology* / Q000532:radiotherapy*; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D016016:Proportional Hazards Models; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000706:statistics & numerical data*; D016896:Treatment Outcome
references.citation: IEEE Trans Med Imaging. 2004 Oct;23(10):1233-44
references.pmid: 15493691
references.citation: Int J Radiat Oncol Biol Phys. 2007 Nov 15;69(4):985-92
references.pmid: 17689035
references.citation: Int J Radiat Oncol Biol Phys. 2001 Mar 15;49(4):907-16
references.pmid: 11240231
references.citation: Med Phys. 2003 May;30(5):979-85
references.pmid: 12773007
references.citation: Int J Radiat Oncol Biol Phys. 2001 Nov 15;51(4):1064-80
references.pmid: 11704332
references.citation: Int J Radiat Oncol Biol Phys. 2004 Mar 15;58(4):1106-13
references.pmid: 15001251
references.citation: Semin Radiat Oncol. 1999 Jan;9(1):35-47
references.pmid: 10196397
references.citation: Int J Radiat Oncol Biol Phys. 2006 Mar 15;64(4):1275-86
references.pmid: 16504765
references.citation: Med Phys. 2005 Feb;32(2):318-25
references.pmid: 15789575
references.citation: Int J Radiat Oncol Biol Phys. 2002 Oct 1;54(2):313-6
references.pmid: 12243802
references.citation: Int J Radiat Oncol Biol Phys. 2005 Jul 15;62(4):1055-69
references.pmid: 15990009
references.citation: Int J Radiat Oncol Biol Phys. 2006 May 1;65(1):112-24
references.pmid: 16618575
references.citation: Biochim Biophys Acta. 1975 Oct 20;405(2):442-51
references.pmid: 1180967
references.citation: Annu Rev Med. 2006;57:19-31
references.pmid: 16409134
references.citation: Cancer. 2003 Apr 1;97(7):1630-8
references.pmid: 12655519
references.citation: Int J Radiat Oncol Biol Phys. 2005 Jul 1;62(3):829-37
references.pmid: 15936567
references.citation: Int J Radiat Oncol Biol Phys. 2005 Feb 1;61(2):318-28
references.pmid: 15667949
references.citation: J Clin Oncol. 2005 Dec 20;23(36):9319-28
references.pmid: 16361631
references.citation: Value Health. 2001 Sep-Oct;4(5):348-61
references.pmid: 11705125
references.citation: Phys Med Biol. 1999 Sep;44(9):2241-9
references.pmid: 10495118
references.citation: J Biomed Biotechnol. 2009;2009:892863
references.pmid: 19704920
references.citation: Radiother Oncol. 2004 Apr;71(1):3-12
references.pmid: 15066290
references.citation: Int J Radiat Oncol Biol Phys. 2005 Jul 15;62(4):965-73
references.pmid: 15989996
references.citation: Int J Radiat Oncol Biol Phys. 2005 May 1;62(1):260-6
references.pmid: 15850931
references.citation: Med Phys. 2002 Sep;29(9):2109-27
references.pmid: 12349932
references.citation: IEEE Trans Neural Netw. 1991;2(6):568-76
references.pmid: 18282872
references.citation: IEEE Trans Med Imaging. 2002 Dec;21(12):1552-63
references.pmid: 12588039