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Computer-aided evaluation as an adjunct to revised BI-RADS Atlas: Improvement in positive predictive value at screening breast MRI

  • Hye Mi Gweon
  • , Nariya Cho
  • , Mirinae Seo
  • , A. Jung Chu
  • , Woo Kyung Moon

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Objectives: To investigate whether kinetic features via magnetic resonance (MR)-computer-aided evaluation (CAE) can improve the positive predictive value (PPV) of morphological descriptors for suspicious lesions at screening breast MRI. Methods: One hundred and sixteen consecutive, suspiciously enhancing lesions detected at contralateral breast MRI screening in 116 women with newly-diagnosed breast cancers were included. Morphological descriptors according to the revised BI-RADS Atlas and kinetic features from MR-CAE were analysed. The PPV of each descriptor was analysed to identify subgroups in which PPV could be improved by the addition of MR-CAE. Results: When biopsy recommendations were downgraded to follow-up in cases where there were both the absence of enhancement at a 50 % threshold and the absence of delayed washout, PPV increased from 0.328 (95 % CI, 0.249-0.417) to 0.500 (95 % CI, 0.387- 0.613). Two ductal carcinoma in situ (DCIS) non-mass enhancement (NME) lesions were missed. Application of downgrading criteria to foci or masses led to increased PPV from 0.310 (95 % CI, 0.216-0.419) to 0.437 (95 % CI, 0.331-0.547) without missing cancers. Conclusions: MR-CAE has the potential to improve the PPV of breast MR imaging by reducing the number of false positives. When suspicious mass lesions do not show enhancement at a 50 % threshold nor delayed washout, follow-up rather than biopsy can be considered. • MR-CAE has the potential to increase PPV at breast MRI screening. •Lesions without enhancement at 50 % threshold and washout might be downgraded. •DCIS non-mass lesions might be false-negative cases at MR-CAE.

Original languageEnglish
Pages (from-to)1800-1807
Number of pages8
JournalEuropean Radiology
Volume24
Issue number8
DOIs
Publication statusPublished - Aug 2014

Bibliographical note

Funding Information:
The scientific guarantor of this publication is Nariya Cho. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A1006722). No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Breast
  • Computer-aided evaluation
  • Kinetic feature
  • Magnetic resonance imaging
  • Positive predictive value

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