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A Competency-Based Ultrasound-Guided Breast Biopsy Training Program for Radiologists From Low-and-Middle-Income

Adeleye Dorcas Omisore1,2,3, Adedeji Ayoola Egberongbe2,3,4, Lydia Eleanor Pace5

  • 1Department of Radiology, Obafemi Awolowo University, Ile-Ife, Nigeria.

Cancer Control : Journal of the Moffitt Cancer Center
|April 18, 2025
PubMed
Summary

This study introduces a mobile health-enhanced training program to equip Nigerian radiologists with ultrasound-guided breast biopsy (UGBB) skills. This initiative aims to improve early breast cancer diagnosis in low- and middle-income countries (LMICs).

Keywords:
breast biopsycompetency-basedearly diagnosislow-and-middle-income countriesmobile healthradiologistultrasound-guided

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Area of Science:

  • Medical Imaging
  • Oncology
  • Global Health

Background:

  • Ultrasound-guided breast biopsy (UGBB) is standard in high-income countries, but less accessible in low- and middle-income countries (LMICs) due to skill gaps.
  • Traditional biopsy methods in LMICs can delay breast cancer diagnosis and impact patient outcomes.
  • Mobile health (mHealth) technology offers a potential solution to bridge this gap.

Purpose of the Study:

  • To present the protocol for a competency-based UGBB training program for Nigerian radiologists in LMICs.
  • To leverage mobile health technology within a blended learning framework to enhance training delivery.
  • To establish a scalable model for improving breast cancer diagnostic capacity in resource-limited settings.

Main Methods:

  • Prospective, multi-institutional, single-arm clinical trial involving 13 Nigerian radiologists.
  • Utilized a unique competency-based curriculum with blended learning (e-learning, trainer-led), simulation, and supervised/unsupervised patient biopsies.
  • Training program spans two years, including one year for active training and recruitment, followed by one year for patient follow-up.

Main Results:

  • Primary outcomes include trainee competency (O-SCORE), radiology-pathology concordance, and complication rates.
  • Secondary outcomes focus on the diagnostic interval and positive predictive value of UGBB.
  • The study protocol is designed to rigorously evaluate the effectiveness of the training program.

Conclusions:

  • Building UGBB capacity in Nigeria and other LMICs can significantly improve breast cancer outcomes through earlier detection.
  • This mHealth-integrated training program offers a sustainable strategy to enhance diagnostic capabilities for breast cancer.
  • The adaptable training model holds potential for other image-guided procedures, contributing to global cancer control efforts.