A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning

Abstract

Background: Oncotype DX(ODX) is a 21-gene breast cancer recurrence score(RS) assay that aids in decision-making for chemotherapy in early-stage hormone receptor-positive(HR+)breast cancer. We developed a prediction tool using machine learning for high- or low-risk ODX criteria (i.e., RS < 11 for low-risk; RS > 25 for high-risk).

Methods: We performed a retrospective review of 301 breast cancer patients who underwent surgery between April 2011 and July 2017 and then an ODX test at Samsung Medical Center in Seoul, Korea. Among them, 208 cases were defined as the modeling group and 76 cases were defined as the validation group. We built a supervised machine learning classification model using the Azure ML platform.

Results: For the high RS group, accuracy was 0.903 through Two-class Decision Jungle method in test set. For the low RS group, the accuracy was 0.726 when the Two-class Neural Network method was applied. The AUC of the ROC curve was 0.917 in the high RS group and 0.744 in the low RS group in test set. In addition, we conducted an internal validation using 76 patients who underwent ODX testing between January 2017 and July 2017. The accuracy of validation was 0.880 in the high RS group and 0.790 in the low RS group.

Conclusion: We developed a predictive model using machine learning that could represent a useful and easy-to-access tool for the selection of high ODX RS patients. After additional evaluation with large data and external validation, worldwide use of our model could be expected.

Keywords: Breast neoplasm; Machine learning; Prediction.