Prognostic models for mesothelioma : variable selection and machine learning

Malignant pleural mesothelioma is a rare and lethal form of cancer affecting the external lining of the lungs. Extrapleural pneumonectomy (EPP), which involves the removal of the affected lung, is one of the few treatments that has been shown to have some effectiveness in treatment of the disease [39], but this procedure carries with it a high risk of mortality and morbidity [8]. This paper is concerned with building models using gene expression levels to predict patient survival following EPP; these models could potentially be used to guide patient treatment. A study by Gordon et al built a predictor based on ratios of gene expression levels that was 88% accurate on the set of 29 independent test samples, in terms of classifying whether or not the patients survived shorter or longer than the median survival [15]. These results were recreated both on the original data set used by Gordon et al and on a newer data set which contained the same samples but was generated using newer software. The predictors were evaluated using N-fold cross validation. In addition, other methods of variable selection and machine learning were investigated to build different types of predictive models. These analyses used a random training set from the newer data set. These models were evaluated using N-fold cross validation and the best of each of the four main types of models -

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2005

Author dc:creator

• Vantzelfde, Nathan Hans

Advisor dc:contributor.advisor

• Lucila Ohno-Machado.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/33370

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/33370