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Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort

Xuetong Tao1, Ziba Gandomkar1, Tong Li2,3

  • 1Discipline of Medical Imaging Science, Faculty of Health Sciences, The University of Sydney, Sydney, NSW 2006, Australia.

Journal of Personalized Medicine
|June 28, 2023
PubMed
Summary

This study used radiomics and machine learning to predict errors in mammography interpretation. The approach successfully identified false positive and false negative errors, aiding in developing targeted educational strategies for radiologists.

Keywords:
diagnostic errorsmachine learningmammographymammography interpretationradiomics

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Area of Science:

  • Radiology
  • Machine Learning
  • Medical Imaging Analysis

Background:

  • Mammography interpretation is complex, leading to significant diagnostic error rates.
  • Improving accuracy in mammography reading is crucial for effective breast cancer screening.

Purpose of the Study:

  • To develop a radiomics-based machine learning model to predict diagnostic errors in mammography.
  • To map mammographic characteristics against radiologist errors to identify patterns.

Main Methods:

  • Radiomic features were extracted from regions of interest in mammographic cases.
  • Random forest models were trained to predict diagnostic errors (false positives, false negatives, location errors).
  • Model performance was evaluated using sensitivity, specificity, accuracy, and AUC.

Main Results:

  • The radiomics approach successfully predicted false positive and false negative errors in mammography interpretation.
  • Predictability of errors varied between radiologist cohorts, with cohort B errors being less predictable.
  • Feature normalization and vendor differences did not significantly impact model performance.

Conclusions:

  • A novel radiomics-based machine learning pipeline can predict specific types of mammography reading errors.
  • This method offers potential for creating tailored educational interventions to enhance radiologist performance.
  • The findings support the use of AI in improving mammography diagnostic accuracy.