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

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.

[1]  Samuel J. Magny,et al.  Breast Imaging Reporting and Data System , 2020, Definitions.

[2]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Tianfu Wang,et al.  Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review , 2019, Journal of medical Internet research.

[5]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

[6]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  M. Yaffe,et al.  American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography , 2007, CA: a cancer journal for clinicians.

[8]  E. Fukuma,et al.  Feasibility and potential limitations of abbreviated breast MRI: an observer study using an enriched cohort , 2017, Breast Cancer.

[9]  Ukihide Tateishi,et al.  Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network , 2019, Japanese Journal of Radiology.

[10]  M. Tillich,et al.  Breast MRI used as a problem-solving tool reliably excludes malignancy. , 2015, European journal of radiology.

[11]  Ralf-Dieter Hilgers,et al.  Abbreviated breast magnetic resonance imaging (MRI): first postcontrast subtracted images and maximum-intensity projection-a novel approach to breast cancer screening with MRI. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Y. Kanda,et al.  Investigation of the freely available easy-to-use software ‘EZR' for medical statistics , 2012, Bone Marrow Transplantation.

[13]  Namkug Kim,et al.  Deep Learning in Medical Imaging , 2019, Neurospine.

[14]  Jaewoo Kang,et al.  Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network , 2018, PloS one.

[15]  Karel G M Moons,et al.  Meta-analysis of MR imaging in the diagnosis of breast lesions. , 2008, Radiology.

[16]  Hiroyuki Abe,et al.  Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks , 2018, Journal of medical imaging.

[17]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Deepa Sheth,et al.  Artificial intelligence in the interpretation of breast cancer on MRI , 2019, Journal of magnetic resonance imaging : JMRI.

[19]  Ukihide Tateishi,et al.  Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks , 2019, Diagnostics.

[20]  Rebecca L. Siegel Mph,et al.  Cancer statistics, 2018 , 2018 .

[21]  Dorit Merhof,et al.  Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. , 2019, Radiology.

[22]  Krzysztof J. Geras,et al.  Machine learning in breast MRI , 2019, Journal of magnetic resonance imaging : JMRI.

[23]  E. Mendelson,et al.  Artificial Intelligence in Breast Imaging: Potentials and Limitations. , 2019, AJR. American journal of roentgenology.

[24]  P. Hérent,et al.  Detection and characterization of MRI breast lesions using deep learning. , 2019, Diagnostic and interventional imaging.

[25]  M. Yaffe,et al.  American Cancer Society Guidelines for Breast Screening with MRI as an Adjunct to Mammography , 2007 .

[26]  F J Gilbert,et al.  Artificial intelligence in breast imaging. , 2019, Clinical radiology.

[27]  Roberto Bellotti,et al.  A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis , 2020, BMC Bioinformatics.

[28]  Paul Babyn,et al.  Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..