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      Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

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          Abstract

          BACKGROUND

          Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT).

          METHODS

          A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher’s linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase).

          RESULT

          The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy.

          CONCLUSION

          The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

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          Most cited references27

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          Nearest neighbor pattern classification

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            The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma.

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              A study of cross-validation and bootstrap for accuracy estimation and model selection in

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                Author and article information

                Journal
                Cancer Inform
                Cancer Inform
                Cancer Informatics
                Cancer Informatics
                Libertas Academica
                1176-9351
                2014
                01 December 2014
                : 13
                : 141-152
                Affiliations
                [1 ]National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA.
                [2 ]Universidad de Oviedo, Spain.
                [3 ]Biomodels, LLC, Watertown, MA, USA.
                Author notes
                Article
                cin-13-2014-141
                10.4137/CIN.S19745
                4251540
                25506196
                cd2b78dc-dfa1-4c0c-9b4b-8652acdfeb42
                © 2014 the author(s), publisher and licensee Libertas Academica Ltd.

                This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.

                History
                : 27 August 2014
                : 23 October 2014
                : 23 October 2014
                Categories
                Methodology

                Oncology & Radiotherapy
                cancer-related fatigue,radiation therapy,prostate cancer,fisher’s linear discriminant analysis (lda),k-nn backward recursive feature elimination

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