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Improving Invasive Breast Cancer Care Using Machine Learning Technology.

Clement G Yedjou1, Solange S Tchounwou2, Jameka Grigsby3

  • 1Department of Biological Sciences, College of Science and Technology, Florida Agricultural and Mechanical University, 1610 S. Martin Luther King Blvd, Tallahassee, FL 32307, United States.

Journal of Biomedical Research & Environmental Sciences
|October 3, 2022
PubMed
Summary

Machine learning (ML) effectively analyzes breast cancer (BC) data, comparing invasive forms like infiltrating ductal carcinoma. This study used ML to assess BC patient data, improving understanding of outcomes for this common women's cancer.

Keywords:
Machine Learningbreast cancerinfiltrating ductal carcinomainfiltrating lobular carcinomamachine learningmucinous carcinomasurgery treatment

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Area of Science:

  • Oncology
  • Biomedical Informatics

Background:

  • Breast cancer (BC) is the most prevalent malignancy affecting women globally.
  • Invasive breast cancer arises from the epithelial cells of breast ducts or lobules.
  • Understanding and comparing invasive BC subtypes is crucial for effective treatment.

Purpose of the Study:

  • To employ machine learning (ML) as a novel approach for assessing and comparing invasive breast cancer subtypes.
  • To analyze a dataset of 334 BC patients, focusing on tumor type, stage, and survival rates.
  • To evaluate the utility of ML in managing and interpreting large-scale BC data.

Main Methods:

  • Utilized machine learning algorithms to analyze a publicly available dataset of 334 breast cancer patients.
  • Interpreted patient data based on BC subtype (infiltrating ductal, lobular, mucinous carcinoma), age, sex, tumor stage, and surgical intervention.
  • Calculated survival rates across different stages of breast cancer.

Main Results:

  • Infiltrating ductal carcinoma was the most common subtype (70%), followed by infiltrating lobular carcinoma (27%) and mucinous carcinoma (3%).
  • The majority of patients were diagnosed at Stage II (56.59%), with Stages I and III comprising 19.16% and 24.25%, respectively.
  • Observed survival rates of 83.4% for Stage I, 79.1% for Stage II, and 77% for Stage III breast cancer.

Conclusions:

  • Machine learning serves as a valuable tool for curating extensive breast cancer datasets.
  • ML facilitates a scientific methodology to enhance the understanding and outcomes of breast cancer.
  • The study highlights the potential of ML in advancing breast cancer research and patient care.