Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

Insights into Imaging

Original Article

Lingyun Bao¹  Yingzhao Shen¹  Nico Karssemeijer²  Ritse M. Mann³  Tao Tan⁴  Regina G. H. Beets-Tan⁵  Tianyu Zhang⁵  Jun Xu⁶  Lin Xu⁷  Alejandro Rodriguez-Ruiz⁸ 

[1] Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Xueshi Road, Hubin Street, Shangcheng District, 310006, Hangzhou, Zhejiang, China;Department of Diagnostic Imaging, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands;Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;Department of Diagnostic Imaging, Radboud University Medical Center, PO Box 9101, 6500 HB, Nijmegen, The Netherlands;Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;Faculty of Applied Science, Macao Polytechnic University, 999078, Macao, China;Department of Radiology, Netherlands Cancer Institute (NKI), Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;GROW School for Oncology and Development Biology, Maastricht University, P. O. Box 616, 6200 MD, Maastricht, The Netherlands;Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, 210044, Nanjing, China;School of Information Science and Technology, ShanghaiTech University, 201210, Shanghai, China;ScreenPoint Medical, 6525 EC, Nijmegen, The Netherlands;

关键词: Mammography;    Automated 3D breast ultrasound;    Artificial intelligence;    Breast cancer;    Deep learning;   

DOI  :  10.1186/s13244-022-01352-y

 received in 2022-07-08, accepted in 2022-12-09,  发布年份 2022

来源: Springer

【 摘 要 】

ObjectivesTo assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts.Methods430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden’s index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity.ResultsThe performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden’s index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve.ConclusionMultimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

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