Diagnostic performance of artificial intelligence-based computer-aided diagnosis for breast microcalcification on mammography

Yoon Ah Do, Mijung Jang, Bo La Yun, Sung Ui Shin, Bohyoung Kim, Sun Mi Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The present study evaluated the diagnostic performance of artificial intelligence-based computer-aided diagnosis (AI-CAD) compared to that of dedicated breast radiologists in characterizing suspicious microcalcification on mammography. We retrospectively analyzed 435 unilateral mammographies from 420 patients (286 benign; 149 malignant) undergoing biopsy for suspicious microcalcification from June 2003 to November 2019. Commercial AI-CAD was applied to the mammography images, and malignancy scores were calculated. Diagnostic performance was compared between radiologists and AI-CAD using the area under the receiving operator characteristics curve (AUC). The AUCs of radiologists and AI-CAD were not significantly different (0.722 vs. 0.745, p = 0.393). The AUCs of the adjusted category were 0.726, 0.744, and 0.756 with cutoffs of 2%, 10%, and 38.03% for AI-CAD, respectively, which were all significantly higher than those for radiologists alone (all p < 0.05). None of the 27 cases downgraded to category 3 with a cutoff of 2% were confirmed as malignant on pathological analysis, suggesting that unnecessary biopsies could be avoided. Our findings suggest that the diagnostic performance of AI-CAD in characterizing suspicious microcalcification on mammography was similar to that of the radiologists, indicating that it may aid in making clinical decisions regarding the treatment of breast microcalcification.

Original languageEnglish
Article number1409
JournalDiagnostics
Volume11
Issue number8
DOIs
StatePublished - Aug 2021

Keywords

  • Artificial intelligence
  • Breast cancer
  • Computer-aided diagnosis
  • Diagnosis
  • Mammography
  • Radiology

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