Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers
dc.contributor.author | Deist, Timo M. | |
dc.contributor.author | Dankers, Frank J. W. M. | |
dc.contributor.author | Valdes, Gilmer | |
dc.contributor.author | Wijsman, Robin | |
dc.contributor.author | Hsu, I‐chow | |
dc.contributor.author | Oberije, Cary | |
dc.contributor.author | Lustberg, Tim | |
dc.contributor.author | Soest, Johan | |
dc.contributor.author | Hoebers, Frank | |
dc.contributor.author | Jochems, Arthur | |
dc.contributor.author | El Naqa, Issam | |
dc.contributor.author | Wee, Leonard | |
dc.contributor.author | Morin, Olivier | |
dc.contributor.author | Raleigh, David R. | |
dc.contributor.author | Bots, Wouter | |
dc.contributor.author | Kaanders, Johannes H. | |
dc.contributor.author | Belderbos, José | |
dc.contributor.author | Kwint, Margriet | |
dc.contributor.author | Solberg, Timothy | |
dc.contributor.author | Monshouwer, René | |
dc.contributor.author | Bussink, Johan | |
dc.contributor.author | Dekker, Andre | |
dc.contributor.author | Lambin, Philippe | |
dc.date.accessioned | 2018-08-13T18:49:02Z | |
dc.date.available | 2019-09-04T20:15:38Z | en |
dc.date.issued | 2018-07 | |
dc.identifier.citation | Deist, Timo M.; Dankers, Frank J. W. M.; Valdes, Gilmer; Wijsman, Robin; Hsu, I‐chow ; Oberije, Cary; Lustberg, Tim; Soest, Johan; Hoebers, Frank; Jochems, Arthur; El Naqa, Issam; Wee, Leonard; Morin, Olivier; Raleigh, David R.; Bots, Wouter; Kaanders, Johannes H.; Belderbos, José ; Kwint, Margriet; Solberg, Timothy; Monshouwer, René ; Bussink, Johan; Dekker, Andre; Lambin, Philippe (2018). "Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers." Medical Physics 45(7): 3449-3459. | |
dc.identifier.issn | 0094-2405 | |
dc.identifier.issn | 2473-4209 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/145237 | |
dc.publisher | Springerâ Verlag | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | radiotherapy | |
dc.subject.other | classification | |
dc.subject.other | machine learning | |
dc.subject.other | outcome prediction | |
dc.subject.other | predictive modeling | |
dc.title | Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers | |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/145237/1/mp12967_am.pdf | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/145237/2/mp12967.pdf | |
dc.identifier.doi | 10.1002/mp.12967 | |
dc.identifier.source | Medical Physics | |
dc.identifier.citedreference | Wijsman R, Dankers F, Troost EGC, et al. Multivariable normalâ tissue complication modeling of acute esophageal toxicity in advanced stage nonâ small cell lung cancer patients treated with intensityâ modulated (chemoâ )radiotherapy. Radiother Oncol. 2015; 117: 49 â 54. | |
dc.identifier.citedreference | Lambin P, van Stiphout RGPM, Starmans MHW, et al. Predicting outcomes in radiation oncologyâ multifactorial decision support systems. Nat Rev Clin Oncol. 2013; 10: 27 â 40. | |
dc.identifier.citedreference | Lambin P, Roelofs E, Reymen B, et al. Rapid learning health care in oncologyâ â An approach towards decision support systems enabling customised radiotherapy. Radiother Oncol. 2013; 109: 159 â 164. | |
dc.identifier.citedreference | Kuhn M, Wing J, Weston S, et al. Caret: Classification and Regression Training; 2016. https://CRAN.R-project.org/package=caret. | |
dc.identifier.citedreference | Fernándezâ Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014; 15: 3133 â 3181. | |
dc.identifier.citedreference | Wainer J. Comparison of 14 different families of classification algorithms on 115 binary datasets. ArXiv160600930 Cs. June 2016. http://arxiv.org/abs/1606.00930. Accessed April 8, 2017. | |
dc.identifier.citedreference | Olson RS, Cava WL, Mustahsan Z, Varik A, Moore JH. Dataâ driven advice for applying machine learning to bioinformatics problems. In: Biocomputing 2018. WORLD SCIENTIFIC; 2017:192â 203. https://doi.org/10.1142/9789813235533_0018 | |
dc.identifier.citedreference | Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015; 5: 13087. | |
dc.identifier.citedreference | Belderbos J, Heemsbergen W, Hoogeman M, Pengel K, Rossi M, Lebesque J. Acute esophageal toxicity in nonâ small cell lung cancer patients after high dose conformal radiotherapy. Radiother Oncol. 2005; 75: 157 â 164. | |
dc.identifier.citedreference | Bots WTC, van den Bosch S, Zwijnenburg EM, et al. Reirradiation of head and neck cancer: longâ term disease control and toxicity. Head Neck. 2017; 39: 1122 â 1130. | |
dc.identifier.citedreference | Carvalho S, Troost EGC, Bons J, Menheere P, Lambin P, Oberije C. Prognostic value of bloodâ biomarkers related to hypoxia, inflammation, immune response and tumour load in nonâ small cell lung cancer â a survival model with external validation. Radiother Oncol. 2016; 119: 487 â 494. | |
dc.identifier.citedreference | Carvalho S, Troost E, Bons J, Menheere P, Lambin P, Oberije C. Data from: Prognostic value of bloodâ biomarkers related to hypoxia, inflammation, immune response and tumour load in nonâ small cell lung cancer â a survival model with external validation. https://doi.org/10.17195/candat.2016.04.1. Published 2016. | |
dc.identifier.citedreference | Janssens GO, Rademakers SE, Terhaard CH, et al. Accelerated radiotherapy with carbogen and nicotinamide for laryngeal cancer: results of a phase III randomized trial. J Clin Oncol. 2012; 30: 1777 â 1783. | |
dc.identifier.citedreference | Jochems A, Deist TM, El Naqa I, et al. Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries. Int J Radiat Oncol. 2017; 99: 344 â 352. | |
dc.identifier.citedreference | Kwint M, Uyterlinde W, Nijkamp J, et al. Acute esophagus toxicity in lung cancer patients after intensity modulated radiation therapy and concurrent chemotherapy. Int J Radiat Oncol Biol Phys. 2012; 84: e223 â e228. | |
dc.identifier.citedreference | Egelmeer AGTM, Velazquez ER, de Jong JMA, et al. 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. 2011; 100: 108 â 115. | |
dc.identifier.citedreference | Lustberg T, Bailey M, Thwaites DI, et al. Implementation of a rapid learning platform: predicting 2â year survival in laryngeal carcinoma patients in a clinical setting. Oncotarget. 2016; 7: 37288 â 37296. | |
dc.identifier.citedreference | Oberije C, De Ruysscher D, Houben R, et al. A validated prediction model for overall survival from stage III nonâ small cell lung cancer: toward survival prediction for individual patients. Int J Radiat Oncol Biol Phys. 2015; 92: 935 â 944. | |
dc.identifier.citedreference | Oberije C, De Ruysscher D, Houben R, et al. Data from: A validated prediction model for overall survival from Stage III Non Small Cell Lung Cancer: towards survival prediction for individual patients; 2015. https://www.cancerdata.org/id/10.5072/candat.2015.02. | |
dc.identifier.citedreference | Olling K, Nyeng DW, Wee L. Predicting acute odynophagia during lung cancer radiotherapy using observations derived from patientâ centred nursing care. Tech Innov Patient Support Radiat Oncol. 2018; 5: 16 â 20. | |
dc.identifier.citedreference | Wijsman R, Dankers F, Troost EGC, et al. Inclusion of incidental radiation dose to the cardiac atria and ventricles does not improve the prediction of radiation pneumonitis in advanced stage nonâ small cell lung cancer patients treated with intensityâ modulated radiation therapy. Int J Radiat Oncol. 2017; 99: 434 â 441. | |
dc.identifier.citedreference | James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning: With Applications in R. New York: Springerâ Verlag; 2013. //www.springer.com/gp/book/9781461471370. Accessed March 4, 2018. | |
dc.identifier.citedreference | Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York: Springerâ Verlag; 2009. //www.springer.com/gp/book/9780387848570. Accessed March 4, 2018. | |
dc.identifier.citedreference | Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010; 33: 1 â 22. | |
dc.identifier.citedreference | Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002; 2: 18 â 22. | |
dc.identifier.citedreference | Venables WN, Ripley BD. Modern Applied Statistics with S, 4th ed. New York: Springer; 2002. http://www.stats.ox.ac.uk/pub/MASS4. | |
dc.identifier.citedreference | Karatzoglou A, Smola A, Hornik K, Zeileis A. kernlab â an S4 package for Kernel methods in R. J Stat Softw. 2004; 11: 1 â 20. | |
dc.identifier.citedreference | Tuszynski J. CaTools: Tools: Moving Window Statistics, GIF, Base64, ROC AUC, Etc; 2014. https://CRAN.R-project.org/package=caTools. | |
dc.identifier.citedreference | Therneau T, Atkinson B, Ripley B. Rpart: Recursive Partitioning and Regression Trees; 2017. https://CRAN.R-project.org/package=rpart. | |
dc.identifier.citedreference | Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiol Camb Mass. 2010; 21: 128 â 138. | |
dc.identifier.citedreference | Deist TM, Dankers FJWM, Valdes G, et al. Code for: Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. https://github.com/timodeist/classifier_selection_code. | |
dc.identifier.citedreference | Lavesson N, Davidsson P. Quantifying the impact of learning algorithm parameter tuning. In: Proceedings of the 21st National Conference on Artificial Intelligence â Volume 1. Boston, MA: AAAI Press; 2006: 395 â 400. | |
dc.identifier.citedreference | Valdes G, Luna JM, Eaton E, Ii CBS, Ungar LH, Solberg TD. MediBoost: a patient stratification tool for interpretable decision making in the era of precision medicine. Sci Rep. 2016; 6: 37854. | |
dc.identifier.citedreference | Caruana R, Lou Y, Gehrke J, Koch P, Sturm M, Elhadad N. Intelligible models for healthcare: predicting pneumonia risk and hospital 30â day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD â 15. New York, NY, USA: ACM; 2015: 1721 â 1730. | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.