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

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/).

Keywords: Artificial intelligence; Breast neoplasms; Neural networks; Recurrence; Risk factors.

Figure 1

Patient cohort. Patient cohort fulfilled the criteria as data. ^*Recurrence of breast cancer within 5 years after the primary breast cancer surgery

Figure 2

The basic idea of support vector machine. The data are specified as feature vectors, and then these feature vectors are mapped into a feature space. A hyperplane is computed in the feature space to optimally separate two groups of vectors.

Figure 3

The receiver operating characteristic (ROC) curves of the algorithms and prognostic models at 5 years. (A) The area under the ROC (AUC) was 0.73, 0.8, and 0.85 for the Cox regression, artificial neural network (ANN), and support vector machine (SVM), respectively. (B) AUC was 0.85, 0.71, and 0.7 for breast cancer recurrence prediction based on SVM (BCRSVM), Adjuvant! Online, and Nottingham prognostic index (NPI), reprectively.

Figure 4

Prediction of disease-free survival in breast cancer patients using the three prognostic models. (A) Breast cancer recurrence prediction based on SVM (BCRSVM). (B) Adjuvant! Online. (C) Nottingham prognostic index. The log-rank test was applied for each comparison.

Figure 5

Website for the 'breast cancer recurrence prediction based on SVM (BCRSVM)' for easy use of the model in the clinical practice.