The integration of artificial intelligence with contrast-enhanced mammogram in the work up of suspicious breast lesions: what do you expect?

被引:2
作者
Mansour, Sahar [1 ,2 ]
Azzam, Heba [1 ,2 ]
El-Assaly, Hany [1 ]
机构
[1] Cairo Univ, Kasr El Ainy Hosp, Radiol Dept, Womens Imaging Unit, Cairo, Egypt
[2] Baheya Ctr Early Breast Canc & Treatment, Radiol Dept, Cairo, Egypt
关键词
Contrast-enhanced mammogram; Digital mammography; Artificial intelligence; Breast cancer; Suspicious breast lesions; SPECTRAL MAMMOGRAPHY; CANCER; WOMEN;
D O I
10.1186/s43055-023-01166-y
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background The enhancement overlaps at contrast-enhanced mammogram (CEM) between benign and malignant breast abnormalities presents a high probability of false-positive lesions and subjects females' candidate for screening and diagnostic mammograms to unnecessary biopsy and anxiety. The current work aimed to evaluate the ability of mammograms scanned by artificial intelligence (AI) to enhance the specificity of CEM and support the probability of malignancy in suspicious and malignant looking breast lesions.Methods The study included 1524 breast lesions. The AI algorithm applied to the initial mammograms and generated location information for lesions. AI scoring suggested the probability of malignancy ranged from 100% (definite cancers) and < 10% (definite non-cancer) and correlated with recombinant contrast enhanced images.Results The malignant proved abnormalities were 1165 (76.5%), and the benign ones were 359 (26.5%). BI-RADS 4 category was assigned in 704 lesions (46.2%) divided into 400 malignant (400/704, 56.8%) and 304 benign (304/704, 43.2%). BI-RADS 5 category presented by 820 lesions (53.8%), 765 of them were malignant (765/820, 93.3%) and 55 were benign (55/820, 6.7%). The sensitivity of digital mammogram whether supported by AI (93.9%) or contrast media (94.4%) was significantly increased to 97.2% (p < 0.001) when supported by both methods. Improvement of the negative predictive value (from 80.6% and 79.6% to 89.8%, p < 0.05) and the accuracy (from 91.1 and 88.8 to 94.0%, p < 0.01) was detected.Conclusions Contrast-enhanced mammogram helps in specification of different breast lesions in view of patterns of contrast uptake and morphology descriptors, yet with some overlap. The use of artificial intelligence applied on digital mammogram reduced the interpretational variability and limited attempts of re-biopsies of suspicious looking breast lesions assessed by contrast-enhanced mammograms.
引用
收藏
页数:11
相关论文
共 24 条
[1]   Contrast-enhanced Spectral Mammography: Technique, Indications, and Clinical Applications [J].
Bhimani, Chandni ;
Matta, Danielle ;
Roth, Robyn G. ;
Liao, Lydia ;
Tinney, Elizabeth ;
Brill, Kristin ;
Germaine, Pauline .
ACADEMIC RADIOLOGY, 2017, 24 (01) :84-88
[2]  
D'orsi C., 2013, Breast imaging reporting and data system: ACR BI-RADS breast imaging atlas
[3]   Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms [J].
Danala, Gopichandh ;
Patel, Bhavika ;
Aghaei, Faranak ;
Heidari, Morteza ;
Li, Jing ;
Wu, Teresa ;
Zheng, Bin .
ANNALS OF BIOMEDICAL ENGINEERING, 2018, 46 (09) :1419-1431
[4]   SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis [J].
Gao, Fei ;
Wu, Teresa ;
Li, Jing ;
Zheng, Bin ;
Ruan, Lingxiang ;
Shang, Desheng ;
Patel, Bhavika .
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2018, 70 :53-62
[5]  
GE Healthcare, Receives FDA Clearance of the Industry's First Contrast-Enhanced Mammography Solution for Biopsy
[6]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[7]   Contrast-enhanced Mammography: State of the Art [J].
Jochelson, Maxine S. ;
Lobbes, Marc B., I .
RADIOLOGY, 2021, 299 (01) :36-48
[8]   Comparison of screening CEDM and MRI for women at increased risk for breast cancer: A pilot study [J].
Jochelson, Maxine S. ;
Pinker, Katja ;
Dershaw, D. David ;
Hughes, Mary ;
Gibbons, Girard F. ;
Rahbar, Kareem ;
Robson, Mark E. ;
Mangino, Debra A. ;
Goldman, Debra ;
Moskowitz, Chaya S. ;
Morris, Elizabeth A. ;
Sung, Janice S. .
EUROPEAN JOURNAL OF RADIOLOGY, 2017, 97 :37-43
[9]   Bilateral Contrast-enhanced Dual-Energy Digital Mammography: Feasibility and Comparison with Conventional Digital Mammography and MR Imaging in Women with Known Breast Carcinoma [J].
Jochelson, Maxine S. ;
Dershaw, D. David ;
Sung, Janice S. ;
Heerdt, Alexandra S. ;
Thornton, Cynthia ;
Moskowitz, Chaya S. ;
Ferrara, Jessica ;
Morris, Elizabeth A. .
RADIOLOGY, 2013, 266 (03) :743-751
[10]   Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women [J].
Kim, Eun Young ;
Chang, Yoosoo ;
Ahn, Jiin ;
Yun, Ji-Sup ;
Park, Yong Lai ;
Park, Chan Heun ;
Shin, Hocheol ;
Ryu, Seungho .
CANCER, 2020, 126 (21) :4687-4696