Computational Discrimination of Breast Cancer for Korean Women Based on Epidemiologic Data Only

다국어 초록 (Multilingual Abstract)

Breast cancer is the second leading cancer for Korean women and its incidence rate has been increasing annually. If early diagnosis were implemented with epidemiologic data, the women could easily assess breast cancer risk using internet. National Cancer Institute in the United States has released a Web-based Breast Cancer Risk Assessment Tool based on Gail model. However, it is inapplicable directly to Korean women since breast cancer risk is dependent on race. Also, it shows low accuracy (58%-59%). In this study, breast cancer discrimination models for Korean women are developed using only epidemiological casecontrol data (n = 4,574). The models are configured by different classification techniques: support vector machine, artificial neural network, and Bayesian network. A 1,000-time repeated random sub-sampling validation is performed for diverse parameter conditions, respectively. The performance is evaluated and compared as an area under the receiver operating characteristic curve (AUC). According to age group and classification techniques, AUC, accuracy, sensitivity, specificity, and calculation time of all models were calculated and compared. Although the support vector machine took the longest calculation time, the highest classification performance has been achieved in the case of women older than 50 yr (AUC = 64%). The proposed model is dependent on demographic characteristics, reproductive factors, and lifestyle habits without using any clinical or genetic test. It is expected that the model could be implemented as a web-based discrimination tool for breast cancer. This tool can encourage potential breast cancer prone women to go the hospital for diagnostic tests.

참고문헌 (Reference)

1 Rockhill B, "Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention" 93 : 358-366, 2001

2 Centers for Disease Control and Prevention, "United States Cancer Statistics:1999-2011 Cancer Incidence and Mortality Data"

3 Smigal C, "Trends in breast cancer by race and ethnicity : update 2006" 56 : 168-183, 2006

4 Hecht-Nielsen R, "Theory of the backpropagation neural network" 593-605, 1989

5 "Survival analysis of Korean breast cancer patients diagnosed between 1993 and 2002 in Korea: a Nationwide Study of the Cancer Registry" 9 : 214-229, 2006

6 Cortes C, "Support-vector networks" 20 : 273-297, 1995

7 Furey TS, "Support vector machine classification and validation of cancer tissue samples using microarray expression data" 16 : 906-914, 2000

8 Rodriguez-Moguel L, "Risk of breast cancer of low differentiation in tumors with estrogen-negative receptors" 67 : 503-507, 1999

9 Shin HR, "Recent trends and patterns in breast cancer incidence among Eastern and Southeastern Asian women" 21 : 1777-1785, 2010

10 Gail MH, "Projecting individualized probabilities of developing breast cancer for white females who are being examined annually" 81 : 1879-1886, 1989

11 Levy SM, "Prognostic risk assessment in primary breast cancer by behavioral and immunological parameters" 4 : 99-113, 1985

12 Pearl J, "Probabilistic reasoning in intelligent systems: networks of plausible inference" Morgan Kaufmann Publishers Inc. 1988

13 Rokach L, "Pattern classification using ensemble methods" World Scientific Pub. Co. 2010

14 원영주, "Nationwide Cancer Incidence in Korea, 2003~2005" 대한암학회 41 (41): 122-131, 2009

15 Park B, "Korean risk assessment model for breast cancer risk prediction" 8 : e76736-, 2013

16 Boyd CR, "Evaluating trauma care: the TRISS method. Trauma Score and the Injury Severity Score" 27 : 370-378, 1987

17 Clemons M, "Estrogen and the risk of breast cancer" 344 : 276-285, 2001

18 Suzuki S, "Effect of physical activity on breast cancer risk: findings of the Japan collaborative cohort study" 17 : 3396-3401, 2008

19 Park B, "Development of sporadic and hereditary breast cancer risk assessment model in Korean women" Seoul National University 2012

20 Sun­Mi Lee, "Comparisons of predictive modeling techniques for breast cancer in Korean women" 대한의료정보학회 14 (14): 37-44, 2008

21 정규원, "Cancer Statistics in Korea: Incidence, Mortality, Survival, and Prevalence in 2008" 대한암학회 43 (43): 1-11, 2011

22 Ayer T, "Breast cancer risk estimation with artificial neural networks revisited : discrimination and calibration" 116 : 3310-3321, 2010

23 National Cancer Institute, "Breast cancer risk assessment tool"

24 Kiyan T, "Breast cancer diagnosis using statistical neural networks" 4 : 1149-1153, 2004

25 Polat K, "Breast cancer diagnosis using least square support vector machine" 17 : 694-701, 2007

26 Burnside ES, "Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results : initial experience" 240 : 666-673, 2006

27 Shapiro SS, "An analysis of variance test for normality(complete samples)" 52 : 591-611, 1965

28 McPherson K, "ABC of breast diseases. Breast cancer-epidemiology, risk factors, and genetics" 321 : 624-628, 2000

29 Peduzzi P, "A simulation study of the number of events per variable in logistic regression analysis" 49 : 1373-1379, 1996

30 최종필, "A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis" 대한의료정보학회 15 (15): 49-57, 2009