Cited 0 times in Scipus Cited Count

Development of novel breast cancer recurrence prediction model using support vector machine

Authors
Kim, W | Kim, KS  | Lee, JE | Noh, DY | Kim, SW | Jung, YS  | Park, MY | Park, RW
Citation
Journal of breast cancer, 15(2). : 230-238, 2012
Journal Title
Journal of breast cancer
ISSN
1738-67562092-9900
Abstract
PURPOSE: The prediction of breast cancer recurrence is a crucial factor for successful treatment and follow-up planning. The principal objective of this study was to construct a novel prognostic model based on support vector machine (SVM) for the prediction of breast cancer recurrence within 5 years after breast cancer surgery in the Korean population, and to compare the predictive performance of the model with the previously established models.



METHODS: Data on 679 patients, who underwent breast cancer surgery between 1994 and 2002, were collected retrospectively from a Korean tertiary teaching hospital. The following variables were selected as independent variables for the prognostic model, by using the established medical knowledge and univariate analysis: histological grade, tumor size, number of metastatic lymph node, estrogen receptor, lymphovascular invasion, local invasion of tumor, and number of tumors. Three prediction algorithms, with each using SVM, artificial neural network and Cox-proportional hazard regression model, were constructed and compared with one another. The resultant and most effective model based on SVM was compared with previously established prognostic models, which included Adjuvant! Online, Nottingham prognostic index (NPI), and St. Gallen guidelines.



RESULTS: The SVM-based prediction model, named 'breast cancer recurrence prediction based on SVM (BCRSVM),' proposed herein outperformed other prognostic models (area under the curve=0.85, 0.71, 0.70, respectively for the BCRSVM, Adjuvant! Online, and NPI). The BCRSVM evidenced substantially high sensitivity (0.89), specificity (0.73), positive predictive values (0.75), and negative predictive values (0.89).



CONCLUSION: As the selected prognostic factors can be easily obtained in clinical practice, the proposed model might prove useful in the prediction of breast cancer recurrence. The prediction model is freely available in the website (http://ami.ajou.ac.kr/bcr/).
DOI
10.4048/jbc.2012.15.2.230
PMID
22807942
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Surgery
Journal Papers > School of Medicine / Graduate School of Medicine > Biomedical Informatics
Ajou Authors
김, 구상  |  박, 래웅  |  정, 용식
Full Text Link
Files in This Item:
22807942.pdfDownload
Export

qrcode

해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse