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Efficient Clinical Information Extraction from Breast Radiology Reports in French.

Jamil Zaghir^1,2, Belinda Lokaj^2,3, Karen Kinkel⁴

¹Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

Studies in Health Technology and Informatics

|August 23, 2024

Summary

Logistic regression effectively classifies French breast MRI reports for automated data extraction. This approach enhances clinical support and research by improving the efficiency of identifying key patient information.

Keywords:

Breast cancer NLP Radiology report text classification

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Area of Science:

Medical Informatics
Radiology
Natural Language Processing

Background:

Radiology reports contain vital patient data beyond images.
Automated extraction of this information is crucial for clinical support and research.
Classifying breast MRI reports aids in secondary data utilization.

Purpose of the Study:

To evaluate machine learning classifiers for automated data extraction from French breast MRI reports.
To determine the best performing classifier for internal and external validation.
To assess the feasibility of automating clinical parameter extraction from radiology reports.

Main Methods:

Tested multiple classifiers on 1,218 French breast MRI reports from two Swiss clinical centers.
Utilized logistic regression, comparing its performance against other models.
Evaluated classifier performance using accuracy and macro-F1 scores for internal and external datasets.

Main Results:

Logistic regression demonstrated superior performance in classifying breast MRI reports.
Achieved high accuracy (>0.95) and macro-F1 (>0.86) on internal data.
Showed good accuracy (>0.81) and macro-F1 (>0.41) on external data, indicating generalizability.

Conclusions:

Automated classification of breast MRI reports is feasible and effective.
Logistic regression is a suitable method for this task, facilitating efficient clinical parameter extraction.
This automation provides a foundation for future annotation processes and research.