Artificial intelligence in the interpretation of breast cancer on MRI

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, going beyond the current use in computer‐aided detection to include diagnosis, prognosis, response to therapy, and risk assessment. The automated capabilities of AI offer the potential to enhance the diagnostic expertise of clinicians, including accurate demarcation of tumor volume, extraction of characteristic cancer phenotypes, translation of tumoral phenotype features to clinical genotype implications, and risk prediction. The combination of image‐specific findings with the underlying genomic, pathologic, and clinical features is becoming of increasing value in breast cancer. The concurrent emergence of newer imaging techniques has provided radiologists with greater diagnostic tools and image datasets to analyze and interpret. Integrating an AI‐based workflow within breast imaging enables the integration of multiple data streams into powerful multidisciplinary applications that may lead the path to personalized patient‐specific medicine. In this article we describe the goals of AI in breast cancer imaging, in particular MRI, and review the literature as it relates to the current application, potential, and limitations in breast cancer.

[1]  Chulhan Kim,et al.  Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study , 2015, Cancer Imaging.

[2]  Hui Li,et al.  Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms , 2017, Journal of medical imaging.

[3]  L. Tabár,et al.  Mammography service screening and mortality in breast cancer patients: 20-year follow-up before and after introduction of screening , 2003, The Lancet.

[4]  Nico Karssemeijer,et al.  Fully automated detection of breast cancer in screening MRI using convolutional neural networks , 2018, Journal of medical imaging.

[5]  J. Wolfe,et al.  Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study. , 1987, AJR. American journal of roentgenology.

[6]  Maryellen L. Giger,et al.  Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study. , 2011, Radiology.

[7]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. , 1994, Medical physics.

[8]  M. Su,et al.  Evaluation of Clinical Breast MR Imaging Performed with Prototype Computer-aided Diagnosis Breast MR Imaging Workstation: Reader Study , 2012 .

[9]  Raymond Y Huang,et al.  Artificial intelligence in cancer imaging: Clinical challenges and applications , 2019, CA: a cancer journal for clinicians.

[10]  Nico Karssemeijer,et al.  Using deep learning to segment breast and fibroglandular tissue in MRI volumes , 2017, Medical physics.

[11]  Lars J. Grimm,et al.  Relationships Between MRI Breast Imaging‐Reporting and Data System (BI‐RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype , 2017, The breast journal.

[12]  Lubomir M. Hadjiiski,et al.  Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis , 2018, Physics in medicine and biology.

[13]  Yahong Luo,et al.  A deep learning method for classifying mammographic breast density categories , 2018, Medical physics.

[14]  C. Fan,et al.  PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer , 2012, Breast Cancer Research and Treatment.

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

[16]  M L Giger,et al.  Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. , 1998, Medical physics.

[17]  Maryellen L Giger,et al.  Machine Learning in Medical Imaging. , 2018, Journal of the American College of Radiology : JACR.

[18]  Karla Kerlikowske,et al.  Factors Associated With Rates of False-Positive and False-Negative Results From Digital Mammography Screening: An Analysis of Registry Data , 2016, Annals of Internal Medicine.

[19]  M L Giger,et al.  Computerized detection of clustered microcalcifications: evaluation of performance on mammograms from multiple centers. , 1995, Radiographics : a review publication of the Radiological Society of North America, Inc.

[20]  Hui Li,et al.  Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.

[21]  Nico Karssemeijer,et al.  Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk. , 2016, European journal of radiology.

[22]  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.

[23]  M. Giger,et al.  A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. , 2006, Academic radiology.

[24]  Hiroyuki Abe,et al.  Abbreviated MRI and Accelerated MRI for Screening and Diagnosis of Breast Cancer , 2017, Topics in magnetic resonance imaging : TMRI.

[25]  Mitchell D Schnall,et al.  Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. , 2016, Radiology.

[26]  Ying Yuan,et al.  Accuracy of MRI in prediction of pathologic complete remission in breast cancer after preoperative therapy: a meta-analysis. , 2010, AJR. American journal of roentgenology.

[27]  U. G. Dailey.  Cancer,Facts and Figures about. , 2022, Journal of the National Medical Association.

[28]  M. Giger,et al.  Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.

[29]  Wei Wei,et al.  Comparing Performance of the CADstream and the DynaCAD Breast MRI CAD Systems , 2013, Journal of Digital Imaging.

[30]  Zhimin Huo,et al.  Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. , 2002, Radiology.

[31]  R. Chang,et al.  Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. , 2012, Magnetic resonance imaging.

[32]  C. D'Orsi,et al.  Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. , 2010, Journal of the American College of Radiology : JACR.

[33]  Fabrice Andre,et al.  Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: ASCO Clinical Practice Guideline Update-Integration of Results From TAILORx. , 2019, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  S. Feig,et al.  Cost-effectiveness of mammography, MRI, and ultrasonography for breast cancer screening. , 2010, Radiologic clinics of North America.

[35]  Ahmed Bilal Ashraf,et al.  Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles. , 2014, Radiology.

[36]  R. Barzilay,et al.  Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone. , 2019, AJR. American journal of roentgenology.

[37]  Andrew Janowczyk,et al.  A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI , 2018, Medical Imaging.

[38]  Jennifer D. Brooks,et al.  Background parenchymal enhancement at breast MR imaging and breast cancer risk. , 2011, Radiology.

[39]  N. Karssemeijer,et al.  A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution. , 2015, Medical physics.

[40]  Maryellen L. Giger,et al.  Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes , 2017, European Radiology Experimental.

[41]  Lina Arbash Meinel,et al.  Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system , 2007, Journal of magnetic resonance imaging : JMRI.

[42]  M. Giger,et al.  Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. , 2006, Medical physics.

[43]  Natalia Antropova,et al.  A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets , 2017, Medical physics.

[44]  David Gur,et al.  Breast MRI contrast enhancement kinetics of normal parenchyma correlate with presence of breast cancer , 2016, Breast Cancer Research.

[45]  R N Hoover,et al.  Mammographic densities and risk of breast cancer , 1991, Cancer.

[46]  Erich P Huang,et al.  Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set , 2016, npj Breast Cancer.

[47]  E. Morris.  Diagnostic breast MR imaging: current status and future directions. , 2007, Radiologic clinics of North America.

[48]  István Csabai,et al.  Detecting and classifying lesions in mammograms with Deep Learning , 2017, Scientific Reports.

[49]  Ahmed Hosny,et al.  Data Analysis Strategies in Medical Imaging , 2018, Clinical Cancer Research.

[50]  J. Wolfe,et al.  Mammographic features and breast cancer risk: effects with time, age, and menopause status. , 1995, Journal of the National Cancer Institute.

[51]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[52]  Les Irwig,et al.  Meta-analysis of magnetic resonance imaging in detecting residual breast cancer after neoadjuvant therapy. , 2013, Journal of the National Cancer Institute.

[53]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[54]  M. Giger,et al.  Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma , 2015, Scientific Reports.

[55]  Jie Chen,et al.  Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer? , 2012, Breast Cancer Research and Treatment.

[56]  Jia Huajie,et al.  Can diffusion-weighted MR imaging and contrast-enhanced MR imaging precisely evaluate and predict pathological response to neoadjuvant chemotherapy in patients with breast cancer? , 2012 .

[57]  Victor J Pizzitola,et al.  Molecular Breast Imaging and the 2016 Update to the ACR Appropriateness Criteria for Breast Cancer Screening. , 2016, Journal of the American College of Radiology : JACR.

[58]  Maryellen L. Giger,et al.  A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1 , 2006 .

[59]  Wei Tse Yang,et al.  Identification of Intrinsic Imaging Phenotypes for Breast Cancer Tumors: Preliminary Associations with Gene Expression Profiles , 2015 .

[60]  A. Stuckey,et al.  ACR Appropriateness Criteria® Breast Cancer Screening. , 2017, Journal of the American College of Radiology : JACR.

[61]  Berkman Sahiner,et al.  Deep learning in medical imaging and radiation therapy. , 2018, Medical physics.

[62]  N. Boyd,et al.  Mammographic density and breast cancer risk: current understanding and future prospects , 2011, Breast Cancer Research.

[63]  L. Esserman,et al.  Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. , 2012, Radiology.

[64]  Nico Karssemeijer,et al.  Automated localization of breast cancer in DCE-MRI , 2015, Medical Image Anal..

[65]  M. Giger,et al.  Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. , 2013, Annual review of biomedical engineering.

[66]  Savannah C Partridge,et al.  Are Qualitative Assessments of Background Parenchymal Enhancement, Amount of Fibroglandular Tissue on MR Images, and Mammographic Density Associated with Breast Cancer Risk? , 2015, Radiology.

[67]  Bonnie N. Joe,et al.  Imaging Neoadjuvant Therapy Response in Breast Cancer. , 2017, Radiology.

[68]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[69]  T. Julian,et al.  Accuracy of Clinical Examination, Digital Mammogram, Ultrasound, and MRI in Determining Postneoadjuvant Pathologic Tumor Response in Operable Breast Cancer Patients , 2011, Annals of Surgical Oncology.

[70]  Lubomir M. Hadjiiski,et al.  Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms , 2017, Physics in medicine and biology.

[71]  Erich P Huang,et al.  MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. , 2016, Radiology.

[72]  Manuela Durando,et al.  Accuracy of mammography, digital breast tomosynthesis, ultrasound and MR imaging in preoperative assessment of breast cancer. , 2014, Anticancer research.

[73]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[74]  Nico Karssemeijer,et al.  Automated Characterization of Breast Lesions Imaged With an Ultrafast DCE-MR Protocol , 2014, IEEE Transactions on Medical Imaging.

[75]  A. Miller,et al.  Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. , 1995, Journal of the National Cancer Institute.

[76]  M. Giger,et al.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. , 2008, Medical physics.

[77]  Ruijiang Li,et al.  Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation , 2017, Journal of magnetic resonance imaging : JMRI.

[78]  Ruey-Feng Chang,et al.  Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI. , 2014, Magnetic resonance imaging.

[79]  Phoebe E. Freer,et al.  Mammographic breast density: impact on breast cancer risk and implications for screening. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[80]  Stephen W Duffy,et al.  Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I , 2014, Breast Cancer Research.

[81]  R. Bast,et al.  Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[82]  Karen Drukker,et al.  Most-enhancing tumor volume by MRI radiomics predicts recurrence-free survival “early on” in neoadjuvant treatment of breast cancer , 2018, Cancer Imaging.

[83]  Jack Cuzick,et al.  Long-term Accuracy of Breast Cancer Risk Assessment Combining Classic Risk Factors and Breast Density , 2018, JAMA oncology.

[84]  Berkman Sahiner,et al.  Association of computerized mammographic parenchymal pattern measure with breast cancer risk: a pilot case-control study. , 2011, Radiology.