Colorectal Cancer (CRC) remains a leading cause of cancer-related mortality in the United States. A key therapeutic dilemma in the treatment of CRC is whether patients with stage II and stage III disease require adjuvant chemotherapy after surgical resection. Attempts to improve identification of patients at increased risk of recurrence have yielded many predictive models based on gene expression data, but none are FDA approved and none are used in standard clinical practice.
To improve recurrence prediction, we utilize an ensemble learning approach to predict recurrence status at 3 years after diagnosis. Multiple views of a microarray dataset were generated then used to train a diverse pool of base learners using 10x 10-fold cross-validation. Stacked generalization was used to train an ensemble model. Our results demonstrate that molecular data predicts recurrence significantly better than basic clinical data. We also demonstrate that the performance of the multiple-view multiple learner (MVML) supervised learning framework exceeds or matches that of the best base learners across all performance metrics.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-06142016-101117 |
| Date | 15 June 2016 |
| Creators | Castellanos, Jason Alfred |
| Contributors | Qi Liu, R. Daniel Beauchamp, Bing Zhang |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-06142016-101117/ |
| Rights | restrictone, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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