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Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study

 Hyo-Eun Kim  ;  Hak Hee Kim  ;  Boo-Kyung Han  ;  Ki Hwan Kim  ;  Kyunghwa Han  ;  Hyeonseob Nam  ;  Eun Hye Lee  ;  Eun-Kyung Kim 
 LANCET DIGITAL HEALTH, Vol.2(3) : 138-148, 2020 
Journal Title
Issue Date
Background Mammography is the current standard for breast cancer screening. This study aimed to develop an
artificial intelligence (AI) algorithm for diagnosis of breast cancer in mammography, and explore whether it could
benefit radiologists by improving accuracy of diagnosis.
Methods In this retrospective study, an AI algorithm was developed and validated with 170 230 mammography
examinations collected from five institutions in South Korea, the USA, and the UK, including 36 468 cancer positive
confirmed by biopsy, 59 544 benign confirmed by biopsy (8827 mammograms) or follow-up imaging
(50 717 mammograms), and 74 218 normal. For the multicentre, observer-blinded, reader study, 320 mammograms
(160 cancer positive, 64 benign, 96 normal) were independently obtained from two institutions. 14 radiologists
participated as readers and assessed each mammogram in terms of likelihood of malignancy (LOM), location of
malignancy, and necessity to recall the patient, first without and then with assistance of the AI algorithm. The
performance of AI and radiologists was evaluated in terms of LOM-based area under the receiver operating
characteristic curve (AUROC) and recall-based sensitivity and specificity.
Findings The AI standalone performance was AUROC 0·959 (95% CI 0·952–0·966) overall, and 0·970 (0·963–0·978)
in the South Korea dataset, 0·953 (0·938–0·968) in the USA dataset, and 0·938 (0·918–0·958) in the UK dataset. In
the reader study, the performance level of AI was 0·940 (0·915–0·965), significantly higher than that of the
radiologists without AI assistance (0·810, 95% CI 0·770–0·850; p<0·0001). With the assistance of AI, radiologists’
performance was improved to 0·881 (0·850–0·911; p<0·0001). AI was more sensitive to detect cancers with mass
(53 [90%] vs 46 [78%] of 59 cancers detected; p=0·044) or distortion or asymmetry (18 [90%] vs ten [50%] of 20 cancers
detected; p=0·023) than radiologists. AI was better in detection of T1 cancers (73 [91%] vs 59 [74%] of 80; p=0·0039)
or node-negative cancers (104 [87%] vs 88 [74%] of 119; p=0·0025) than radiologists.
Interpretation The AI algorithm developed with large-scale mammography data showed better diagnostic performance
in breast cancer detection compared with radiologists. The significant improvement in radiologists’ performance
when aided by AI supports application of AI to mammograms as a diagnostic support tool.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
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