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.
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.
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.
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.
Hyo-Eun Kim, PhD, Hak Hee Kim, MD, Boo-Kyung Han, MD, Ki Hwan Kim, MD ,Kyunghwa Han, PhD, Hyeonseob Nam, MS, Eun Hye Lee, MD, Eun-Kyung Kim, MD
Photometric Transformer Networks and Label Adjustment for Breast Density Prediction
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study
Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms
Reducing Domain Gap by Reducing Style Bias
유방촬영의 위양성 판정에 관한 전통적 진단보조프로그램과 인공지능 기반 진단보조프로그램의 비교
인공지능 기반 컴퓨터 보조진단을 이용한 선별 유방촬영술에서의 간격암에 대한 후향 분석
Mammographic Surveillance After Breast Conserving Therapy: Impact of Digital Breast Tomosynthesis and Artificial Intelligence-Based Computer-Aided Detection
Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Diagnosis for Breast Microcalcification on Mammography
Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?