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Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics

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Abstract

Objective

To evaluate how breast cancers are depicted by artificial intelligence–based computer-assisted diagnosis (AI-CAD) according to clinical, radiological, and pathological factors.

Materials and methods

From January 2017 to December 2017, 896 patients diagnosed with 930 breast cancers were enrolled in this retrospective study. Commercial AI-CAD was applied to digital mammograms and abnormality scores were obtained. We evaluated the abnormality score according to clinical, radiological, and pathological characteristics. False-negative results were defined by abnormality scores less than 10.

Results

The median abnormality score of 930 breasts was 87.4 (range 0–99). The false-negative rate of AI-CAD was 19.4% (180/930). Cancers with an abnormality score of more than 90 showed a high proportion of palpable lesions, BI-RADS 4c and 5 lesions, cancers presenting as mass with or without microcalcifications and invasive cancers compared with low-scored cancers (all p < 0.001). False-negative cancers were more likely to develop in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers and DCIS compared to detected cancers.

Conclusion

Breast cancers depicted with high abnormality scores by AI-CAD are associated with higher BI-RADS category, invasive pathology, and higher cancer stage.

Key Points

• High-scored cancers by AI-CAD included a high proportion of BI-RADS 4c and 5 lesions, masses with or without microcalcifications, and cancers with invasive pathology.

• Among invasive cancers, cancers with higher T and N stage and HER2-enriched subtype were depicted with higher abnormality scores by AI-CAD.

• Cancers missed by AI-CAD tended to be in asymptomatic patients and extremely dense breasts and to be diagnosed as occult breast cancers by radiologists.

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Abbreviations

AI-CAD :

Artificial Intelligence–based computer-assisted diagnosis

BI-RADS :

American College of Radiology Breast Imaging-Reporting and Data System

DCIS:

Ductal carcinoma in situ

HER2 :

Human epidermal growth factor receptor 2

US :

Ultrasound

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Funding

The authors state that this work has not received any funding.

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Authors and Affiliations

  1. Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, 363, Dongbaekjukjeon-daero, Giheung-gu, Yongin, Gyeonggi-do, Republic of Korea

    Si Eun Lee & Eun-Kyung Kim

  2. Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea

    Kyunghwa Han, Jung Hyun Yoon & Eun-Kyung Kim

  3. Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Jung Hyun Yoon

  4. Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Ji Hyun Youk

Authors
  1. Si Eun Lee
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  2. Kyunghwa Han
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  3. Jung Hyun Yoon
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  5. Eun-Kyung Kim
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Corresponding author

Correspondence to Eun-Kyung Kim.

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Guarantor

The scientific guarantor of this publication is Eun-Kyung Kim, MD, PhD.

Conflict of interest

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.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethics approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Of 896 patients, 192 patients were included in a prior publication that compared diagnostic performance of synthetic and digital mammogram applied by AI-CAD [22]. Lee SE, Han K, Kim E-K (2021) Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms. European Radiology. DOI:10.1007/s00330-021-07796-y

Methodology

• retrospective

• cross-sectional study

• performed at one institution

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Lee, S.E., Han, K., Yoon, J.H. et al. Depiction of breast cancers on digital mammograms by artificial intelligence-based computer-assisted diagnosis according to cancer characteristics. Eur Radiol 32, 7400–7408 (2022). https://doi.org/10.1007/s00330-022-08718-2

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  • DOI: https://doi.org/10.1007/s00330-022-08718-2

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