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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Digital breast tomosynthesis (DBT) is an advanced mammography technique.
  • Early cancer detection is crucial for improving patient outcomes.
  • Artificial intelligence (AI) is increasingly being integrated into medical imaging workflows.

Purpose of the Study:

  • To evaluate the impact of AI on radiologists' cancer detection performance using DBT.
  • To analyze how AI affects the detection of cancers based on specific characteristics like density, size, stage, and histopathology.

Main Methods:

  • Retrospective analysis of mammography audit data from four institutions.
  • Comparison of two periods: pre-AI implementation (2018-2020) and post-AI implementation (2020-2022).
  • Inclusion of data from nine dedicated breast radiologists, analyzing cancer detection rate (CDR), recall rate, tumor size, stage, and histopathology.

Main Results:

  • Cancer detection rate (CDR) increased by 22% post-AI (6.23 to 7.57 per 1000 exams).
  • Detection of invasive cancers improved by 26%, with a decrease in mean tumor size and stage.
  • AI use led to increased detection of cancers in dense breasts (45.0% vs. 37.2%) and doubled the detection rate of lobular cancers.
  • Recall rates remained stable (6.97% pre-AI vs. 6.96% post-AI).

Conclusions:

  • Concurrent use of AI with DBT interpretation by radiologists enhances cancer detection.
  • AI improves the detection of invasive cancers, particularly in dense breast tissue, and facilitates earlier diagnosis.
  • AI demonstrates potential to improve screening mammography effectiveness without increasing patient recall rates.