عنوان انگلیسی مقاله:
A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning
ترجمه فارسی عنوان مقاله:
یک مدل پیش بینی برای گروه پرخطر و کم خطر با توجه به نمره عود توموری DX با استفاده از یادگیری ماشین
Sciencedirect - Elsevier - European Journal of Surgical Oncology, 45 (2019) 134-140: doi:10:1016/j:ejso:2018:09:011
Isaac Kim, Hee Jun Choi, Jai Min Ryu, Se Kyung Lee, Jong Han Yu, Seok Won Kim, Seok Jin Nam, Jeong Eon Lee
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(HRþ)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, accuracywas 0.903 through Two-class Decision Junglemethod in test set. For
the lowRS group, the accuracywas 0.726when the Two-class NeuralNetwork methodwas 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 validationwas 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 | Prediction | Machine learning