Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

被引:45
作者
Adachi, Mio [1 ]
Fujioka, Tomoyuki [2 ]
Mori, Mio [2 ]
Kubota, Kazunori [2 ,3 ]
Kikuchi, Yuka [2 ]
Wu Xiaotong [2 ]
Oyama, Jun [2 ]
Kimura, Koichiro [2 ]
Oda, Goshi [1 ]
Nakagawa, Tsuyoshi [1 ]
Uetake, Hiroyuki [1 ]
Tateishi, Ukihide [2 ]
机构
[1] Tokyo Med & Dent Univ, Dept Surg, Breast Surg, Tokyo 1138510, Japan
[2] Tokyo Med & Dent Univ, Dept Diagnost Radiol, Tokyo 1138510, Japan
[3] Dokkyo Med Univ, Dept Radiol, Mibu, Tochigi 3210293, Japan
关键词
breast imaging; magnetic resonance imaging; deep learning; convolutional neural network; object detection; artificial intelligence; MRI;
D O I
10.3390/diagnostics10050330
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.
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页数:13
相关论文
共 27 条
[1]  
[Anonymous], PROC CVPR IEEE
[2]  
[Anonymous], 2020, IEEE T PATTERN ANAL, DOI DOI 10.1109/TPAMI.2018.2858826
[3]   Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks [J].
Antropova, Natalia ;
Abe, Hiroyuki ;
Giger, Maryellen L. .
JOURNAL OF MEDICAL IMAGING, 2018, 5 (01)
[4]  
D'Orsi C.J., 2013, ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System, V5th ed.
[5]   A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis [J].
Fanizzi, Annarita ;
Basile, Teresa M. A. ;
Losurdo, Liliana ;
Bellotti, Roberto ;
Bottigli, Ubaldo ;
Dentamaro, Rosalba ;
Didonna, Vittorio ;
Fausto, Alfonso ;
Massafra, Raffaella ;
Moschetta, Marco ;
Popescu, Ondina ;
Tamborra, Pasquale ;
Tangaro, Sabina ;
La Forgia, Daniele .
BMC BIOINFORMATICS, 2020, 21 (Suppl 2)
[6]   Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks [J].
Fujioka, Tomoyuki ;
Mori, Mio ;
Kubota, Kazunori ;
Kikuchi, Yuka ;
Katsuta, Leona ;
Adachi, Mio ;
Oda, Goshi ;
Nakagawa, Tsuyoshi ;
Kitazume, Yoshio ;
Tateishi, Ukihide .
DIAGNOSTICS, 2019, 9 (04)
[7]   Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network [J].
Fujioka, Tomoyuki ;
Kubota, Kazunori ;
Mori, Mio ;
Kikuchi, Yuka ;
Katsuta, Leona ;
Kasahara, Mai ;
Oda, Goshi ;
Ishiba, Toshiyuki ;
Nakagawa, Tsuyoshi ;
Tateishi, Ukihide .
JAPANESE JOURNAL OF RADIOLOGY, 2019, 37 (06) :466-472
[8]   Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review [J].
Gardezi, Syed Jamal Safdar ;
Elazab, Ahmed ;
Lei, Baiying ;
Wang, Tianfu .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2019, 21 (07)
[9]   Detection and characterization of MRI breast lesions using deep learning [J].
Herent, P. ;
Schmauch, B. ;
Jehanno, P. ;
Dehaene, O. ;
Saillard, C. ;
Balleyguier, C. ;
Arfi-Rouche, J. ;
Jegou, S. .
DIAGNOSTIC AND INTERVENTIONAL IMAGING, 2019, 100 (04) :219-225
[10]   Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network [J].
Jung, Hwejin ;
Kim, Bumsoo ;
Lee, Inyeop ;
Yoo, Minhwan ;
Lee, Junhyun ;
Ham, Sooyoun ;
Woo, Okhee ;
Kang, Jaewoo .
PLOS ONE, 2018, 13 (09)