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George K Acquaah-Mensah1, Boris Aguilar2, Kawther Abdilleh3

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Summary
This summary is machine-generated.

Radiomics analysis accurately predicts breast cancer (BrCA) molecular subtypes and recurrence in Black and White women, revealing racial disparities in disease characteristics and outcomes.

Keywords:
breast cancerdisease recurrencemachine learningmolecular subtyperadiomics

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Area of Science:

  • Oncology
  • Radiology
  • Machine Learning

Background:

  • Breast cancer (BrCA) is a leading cause of cancer death in women globally.
  • Distinct molecular subtypes of BrCA influence treatment strategies and prognosis.
  • Disease recurrence remains a significant challenge after initial treatment.

Purpose of the Study:

  • To predict molecular subtypes and disease recurrence in BrCA using radiomics and machine learning.
  • To identify racial disparities in BrCA molecular subtypes and recurrence between Black and White patients.
  • To explore the association of radiomic features with race in BrCA.

Main Methods:

  • Retrospective analysis of 922 invasive BrCA patients' MRI data.
  • Application of Random Forest and AdaBoostM1 machine learning algorithms to over 500 radiomics features.
  • Focused analysis on Black and White patients aged 50 or younger at diagnosis (n=346) to assess racial disparities.

Main Results:

  • Radiomics accurately predicted molecular subtype and recurrence for both racial groups, with or without gene expression data.
  • Over 40 radiomics features showed significant associations with race.
  • Breast volume (Breast_Vol) was most predictive for Black patients, while post-contrast tissue volume (TissueVol_PostCon) was most predictive for White patients within the Breast and Fibroglandular Tissue Volume category.

Conclusions:

  • Radiomics can predict racial differences in BrCA recurrence and molecular subtypes.
  • These findings suggest radiomics may impact clinical outcomes by identifying disparities.
  • Further research can leverage radiomics for personalized BrCA management across different racial groups.