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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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A predictive model for high/low risk group according to oncotype DX recurrence score using machine learning.

Isaac Kim1, Hee Jun Choi1, Jai Min Ryu1

  • 1Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, Republic of Korea.

European Journal of Surgical Oncology : the Journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
|October 24, 2018
PubMed
Summary

Machine learning predicts Oncotype DX (ODX) recurrence scores for early-stage breast cancer. This tool aids in identifying high-risk patients, potentially improving chemotherapy decisions.

Keywords:
Breast neoplasmMachine learningPrediction

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Area of Science:

  • Oncology
  • Genomics
  • Machine Learning

Background:

  • The Oncotype DX (ODX) recurrence score (RS) is crucial for chemotherapy decisions in early-stage hormone receptor-positive (HR+) breast cancer.
  • Accurate risk stratification is essential for personalized treatment strategies.

Purpose of the Study:

  • To develop a machine learning-based prediction tool for identifying high- or low-risk ODX criteria.
  • To create an accessible tool for selecting patients with high ODX RS.

Main Methods:

  • A retrospective review of 301 breast cancer patients was conducted.
  • A supervised machine learning classification model was built using the Azure ML platform.
  • The model was trained on 208 cases and validated on 76 cases.

Main Results:

  • The Two-class Decision Jungle model achieved 0.903 accuracy for the high RS group (RS > 25).
  • The Two-class Neural Network model achieved 0.726 accuracy for the low RS group (RS < 11).
  • Internal validation showed 0.880 accuracy for the high RS group and 0.790 for the low RS group.

Conclusions:

  • A machine learning model was successfully developed to predict ODX RS risk categories.
  • The model shows potential as a useful and accessible tool for patient selection.
  • Further validation with larger datasets could support worldwide clinical application.