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Updated: Jun 19, 2025

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Comparison of Explainable Artificial Intelligence Model and Radiologist Review Performances to Detect Breast Cancer

Pelin Seher Oztekin1, Oguzhan Katar2, Tulay Omma3

  • 1Department of Radiology, University of Health Sciences, Ankara Training and Research Hospital, Ankara, Turkey.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|July 25, 2024
PubMed
Summary

An artificial intelligence (AI) model, X²GAI, accurately classifies breast cancer lesions, improving diagnostic reliability and reducing unnecessary biopsies. This AI tool shows promise in assisting radiologists and enhancing patient care.

Keywords:
breast cancerexplainable AImachine learningultrasound

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate breast cancer diagnosis is crucial to avoid unnecessary patient anxiety and invasive procedures like biopsies.
  • Current diagnostic methods, including radiologic examinations, can sometimes lead to false positives and misdiagnoses.
  • There is a significant need for advanced, reliable methods to improve the accuracy of breast cancer detection.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based method for automated classification of breast solid mass lesions as benign or malignant.
  • To create and utilize a novel breast cancer dataset (Breast-XD) for training and testing AI classifiers.
  • To enhance the reliability and interpretability of AI models in breast cancer diagnosis.

Main Methods:

  • A new dataset, Breast-XD, comprising 791 solid mass lesions from 752 patients, was curated.
  • Six machine learning classifiers, including SVM, K-NN, RF, DT, LR, and XGBoost, were trained on the dataset.
  • An explainable XGBoost model (X²GAI) was developed for classification and reliability assessment.

Main Results:

  • The X²GAI model achieved the highest classification accuracy of 94.34% on unseen test data.
  • The model demonstrated strong performance, particularly in cases where radiologists had previously provided false positive diagnoses.
  • An explainable structure was integrated into the model to enhance diagnostic trust and transparency.

Conclusions:

  • The developed X²GAI model shows comparable or superior performance to experienced radiologists in classifying breast lesions.
  • The AI model's ability to correctly identify malignant lesions can help reduce false positives and the need for unnecessary biopsies.
  • This AI-driven approach offers a promising tool for improving the accuracy and efficiency of breast cancer diagnosis.