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Related Experiment Video

Updated: Feb 9, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

Published on: March 20, 2020

Contralateral Breast Cancer Event Detection Using Nature Language Processing.

Zexian Zeng¹, Xiaoyu Li², Sasa Espino³

¹Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|June 2, 2018

Summary

This study introduces a novel method to identify contralateral breast cancer using progress notes and pathology reports. The approach achieved high accuracy, aiding large cohort studies in cancer research.

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Last Updated: Feb 9, 2026

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Area of Science:

Oncology
Medical Informatics
Data Science

Background:

Accurate identification of contralateral breast cancer is crucial for large cohort studies.
Existing methods may lack efficiency or scalability for comprehensive analysis.

Purpose of the Study:

To develop and validate a novel, replicable method for detecting contralateral breast cancer events.
To leverage both narrative text and structured data from pathology reports.

Main Methods:

Extracted medical concepts and combinations from progress notes to identify contralateral events.
Utilized the number of pathology reports for each breast side as additional features.
Employed a support vector machine (SVM) classifier for event detection.

Main Results:

Achieved an area under the curve (AUC) score of 0.93 in cross-validation.
Attained an AUC score of 0.89 in held-out tests.
Demonstrated high accuracy in identifying contralateral breast cancer events.

Conclusions:

The proposed method offers a simple yet effective approach for contralateral breast cancer event identification.
The feature generation process is straightforward, ensuring replicability.
This technique can significantly facilitate large-scale epidemiological studies in breast cancer.