Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

Abstract

Background Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology “imaging biomarker”, “radiomics”, and “artificial intelligence” are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.

Methods and Results This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.

Conclusion Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.

Key Points: 

• In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.

• The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.

• This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.

• Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.

• The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review.

Citation Format

• Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 – 908

Zusammenfassung

Hintergrund Radiologische Untersuchungen nicht nur als bloße Bilder, sondern als Datenquelle zu betrachten, ist zum modernen Paradigma der diagnostischen Bildgebung geworden. Dieser Perspektivwechsel hat sich besonders in der Brustbildgebung durchgesetzt, ermöglicht er doch aus der Informatik abgeleitete Verfahren anzuwenden, innovative klinische Anwendungen zu realisieren und bereits etablierte Methoden zu verfeinern. In diesem Zusammenhang sind die Begriffe „bildgebender Biomarker“, „Radiomics“ und „künstliche Intelligenz“ von zentraler Bedeutung. Diese Methoden versprechen nichtinvasive, kostengünstige (z. B. im Vergleich zu Multigen-Arrays), Workflow-freundliche (automatisiert, nur eine Untersuchung, sofortige Ergebnisse) und klinisch relevante Informationen.

Methode und Ergebnisse Dieser Artikel wurde als narratives Review zu dem besagten Paradigma im Bereich der Brustbildgebung konzipiert. Der Schwerpunkt liegt auf den Schlüsselkonzepten und wichtigen Schlagworten. Für alle Bereiche der Brustbildgebung werden beispielhafte Studien diskutiert.

Schlussfolgerung Die Interpretation von radiologischen Untersuchungen als Datenquelle verspricht eine Optimierung der Behandlung von Brustkrebspatientinnen im Zeitalter der Präzisionsmedizin, da hiermit die Diagnose verfeinert und eine individualisierte Behandlung erreicht werden könnte.

Kernaussagen: 

• In der konventionellen Brustbildgebung werden Untersuchungen anhand von visuell erkennbaren Mustern interpretiert.

• Das Radiomics-Paradigma behandelt radiologische Brustuntersuchungen hingegen als abstrakte Datenquelle, in der Informationen zu finden sind, die über visuell erkennbare Muster hinausgehen.

• Derartige radiomische Signaturen können als bildgebende Biomarker angesehen werden, da sie diagnostische, prädiktive und prognostische Informationen liefern.

• Derartige bildgebende Biomarker können im Zeitalter der Präzisionsmedizin zur Individualisierung der Brustkrebsbehandlung eingesetzt werden.

• In diesem narrativen Übersichtsartikel stellen wir das Radiomics-Paradigma auf dem Gebiet der Brustkrebsbildgebung anhand von exemplarischen Literaturbeispielen dar.

Key words

breast - mammography - ultrasound - screening - MR imaging - artificial intelligence

• References

• 1 Sardanelli F, Aase HS, Álvarez M. et al. Position paper on screening for breast cancer by the European Society of Breast Imaging (EUSOBI) and 30 national breast radiology bodies from Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Israel, Lithuania, Moldova, The Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Spain, Sweden, Switzerland and Turkey. Eur Radiol 2017; 27: 2737-2743
• 2 Trimboli RM, Giorgi Rossi P, Battisti NML. et al. Do we still need breast cancer screening in the era of targeted therapies and precision medicine?. Insights Imaging 2020; 11: 105
• 3 McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol 2006; 15: 1159-1169
• 4 Baltzer PA, Schelhorn J, Dietzel M. et al. Breast screening programs using MRI: is there a role for computer-aided diagnosis?. Imaging Med 2010; 2: 659-673
• 5 Rodriguez-Ruiz A, Lång K, Gubern-Merida A. et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 2019; 29: 4825-4832
• 6 Sagar B, Lin YS, Castel LD. Cost drivers for breast, lung, and colorectal cancer care in a commercially insured population over a 6-month episode: an economic analysis from a health plan perspective. J Med Econ 2017; 20: 1018-1023
• 7 Tasoulis MK, Lee HB, Yang W. et al. Accuracy of Post-Neoadjuvant Chemotherapy Image-Guided Breast Biopsy to Predict Residual Cancer. JAMA Surg 2020; 155: e204103
• 8 World Health Organization. Biomarkers In Risk Assessment: Validity And Validation. 2020
• 9 FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource, Silver Spring (MD): Food and Drug Administration (US). 2016
• 10 O’Connor JPB, Aboagye EO, Adams JE. et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017; 14: 169-186
• 11 Weaver O, Leung JWT. Biomarkers and Imaging of Breast Cancer. Am J Roentgenol 2017; 210: 271-278
• 12 Fleming TR, Powers JH. Biomarkers and Surrogate Endpoints In Clinical Trials. Stat Med 2012; 31: 2973-2984
• 13 Chen JH, Baek HM, Nalcioglu O. et al. Estrogen receptor and breast MR imaging features: a correlation study. J Magn Reson Imaging JMRI 2008; 27: 825-833
• 14 Dietzel M, Kaiser C, Pinker K. et al. Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy. Breast Care Basel Switz 2017; 12: 231-236
• 15 Granzier RWY, van Nijnatten TJA, Woodruff HC. et al. Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review. Eur J Radiol 2019; 121: 108736
• 16 Ha R, Chang P, Mutasa S. et al. Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score. J Magn Reson Imaging 2019; 49: 518-524
• 17 Dietzel M, Schulz-Wendtland R, Ellmann S. et al. Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 2020; 10: 1-11
• 18 Pinker K, Chin J, Melsaether AN. et al. Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 2018; 287: 732-747
• 19 Carter CL, Allen C, Henson DE. Relation of tumor size, lymph node status, and survival in 24740 breast cancer cases. Cancer 1989; 63: 181-187
• 20 Eisenhauer EA, Therasse P, Bogaerts J. et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45: 228-247
• 21 Baltzer P, Mann RM, Iima M. et al. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2020; 30: 1436-1450
• 22 Bickel H, Pinker-Domenig K, Bogner W. et al. Quantitative apparent diffusion coefficient as a noninvasive imaging biomarker for the differentiation of invasive breast cancer and ductal carcinoma in situ. Invest Radiol 2015; 50: 95-100
• 23 Pettersson A, Graff RE, Ursin G. et al Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst 2014; 106
• 24 D’Orsi C, Sickles E, Mendelson E. et al. ACR BI-RADS^® Atlas, Breast Imaging Reporting and Data System. American College of Radiology; 2013
• 25 Le Boulc’h M, Bekhouche A, Kermarrec E. et al. Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk. Diagn Interv Imaging 2020; 101: 811-819
• 26 Destounis SV, Santacroce A, Arieno A. Update on Breast Density, Risk Estimation, and Supplemental Screening. Am J Roentgenol 2020; 214: 296-305
• 27 Wengert GJ, Helbich TH, Kapetas P. et al. Density and tailored breast cancer screening: practice and prediction – an overview. Acta Radiol Open 2018; 7: 2058460118791212
• 28 Gastounioti A, Oustimov A, Keller BM. et al. Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations. Med Phys 2016; 43: 5862
• 29 Baltzer PAT, Yang F, Dietzel M. et al. Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions. Breast J 2010; 16: 233-239
• 30 Kaiser CG, Herold M, Krammer J. et al. Prognostic Value of “Prepectoral Edema” in MR-mammography. Anticancer Res 2017; 37: 1989-1995
• 31 Cheon H, Kim HJ, Kim TH. et al. Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging. Radiology 2018; 287: 68-75
• 32 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446
• 33 Dietzel M, Baltzer PAT, Dietzel A. et al. Application of artificial neural networks for the prediction of lymph node metastases to the ipsilateral axilla – initial experience in 194 patients using magnetic resonance mammography. Acta Radiol Stockh Swed 1987 2010; 51: 851-858
• 34 Dietzel M, Zoubi R, Vag T. et al. Association between survival in patients with primary invasive breast cancer and computer aided MRI. J Magn Reson Imaging JMRI 2013; 37: 146-155
• 35 Tan H, Wu Y, Bao F. et al. Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer. Br J Radiol 2020; 93: 20191019
• 36 Mao N, Yin P, Li Q. et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study. Eur Radiol 2020; 30: 6732-6739
• 37 Dietzel M, Baltzer PAT, Vag T. et al. Application of breast MRI for prediction of lymph node metastases – systematic approach using 17 individual descriptors and a dedicated decision tree. Acta Radiol Stockh Swed 1987 2010; 51: 885-894
• 38 Song J, Yin Y, Wang H. et al. A review of original articles published in the emerging field of radiomics. Eur J Radiol 2020; 127: 108991
• 39 PubMed search: “radiomics” and “breast”. 2020
• 40 Marino MA, Leithner D, Sung J. et al. Radiomics for Tumor Characterization in Breast Cancer Patients: A Feasibility Study Comparing Contrast-Enhanced Mammography and Magnetic Resonance Imaging. Diagnostics (Basel) 2020; 10: 492
• 41 Lee SE, Han K, Kwak JY. et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma. Sci Rep 2018; 8: 13546
• 42 Zhou J, Tan H, Bai Y. et al. Evaluating the HER-2 status of breast cancer using mammography radiomics features. Eur J Radiol 2019; 121: 108718
• 43 Pickles MD, Lowry M, Gibbs P. Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients. Invest Radiol 2016; 51: 177-185
• 44 Codari M, Schiaffino S, Sardanelli F. et al. Artificial Intelligence for Breast MRI in 2008–2018: A Systematic Mapping Review. Am J Roentgenol 2019; 212: 280-292
• 45 Fazal MI, Patel ME, Tye J. et al. The past, present and future role of artificial intelligence in imaging. Eur J Radiol 2018; 105: 246-250
• 46 van Assen M, Lee SJ, De Cecco CN. Artificial intelligence from A to Z: From neural network to legal framework. Eur J Radiol 2020; 129: 109083
• 47 van Hoek J, Huber A, Leichtle A. et al. A survey on the future of radiology among radiologists, medical students and surgeons: Students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 2019; 121: 108742
• 48 Liew C. The future of radiology augmented with Artificial Intelligence: A strategy for success. Eur J Radiol 2018; 102: 152-156
• 49 Sun Q, Lin X, Zhao Y. et al. Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don’t Forget the Peritumoral Region. Front Oncol 2020; 10: 53
• 50 Schaffter T, Buist DSM, Lee CI. et al. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open 2020; 3: e200265
• 51 Sheth D, Giger ML. Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging 2020; 51: 1310-1324
• 52 Legg S, Hutter M. A Collection of Definitions of Intelligence. In: Proc. 2007 Conf. Adv. Artif. Gen. Intell. Concepts Archit. Algorithms Proc. AGI Workshop 2006 1. Aufl.. NLD: IOS Press; 2007: 17-24
• 53 Ellmann S, Wenkel E, Dietzel M. et al. Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses. PLOS ONE 2020; 15: e0228446
• 54 Baltzer PAT, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol 2013; 23: 2051-2060
• 55 Zheng X, Yao Z, Huang Y. et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 2020; 11: 1236
• 56 Kumar SN, Fred AL, Varghese PS. Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering. J Digit Imaging 2019; 32: 322-335
• 57 Saba L, Biswas M, Kuppili V. et al. The present and future of deep learning in radiology. Eur J Radiol 2019; 114: 14-24
• 58 Chan HP, Doi K, Galhotra S. et al. Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med Phys 1987; 14: 538-548
• 59 Lehman CD, Wellman RD, Buist DSM. et al. Diagnostic Accuracy of Digital Screening Mammography with and without Computer-aided Detection. JAMA Intern Med 2015; 175: 1828-1837
• 60 Rodriguez-Ruiz A, Lång K, Gubern-Merida A. et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI J Natl Cancer Inst 2019; 111: 916-922
• 61 McKinney SM, Sieniek M, Godbole V. et al. International evaluation of an AI system for breast cancer screening. Nature 2020; 577: 89-94
• 62 Lång K, Dustler M, Dahlblom V. et al Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol 2020; Online ahead of print
• 63 Yala A, Schuster T, Miles R. et al. A Deep Learning Model to Triage Screening Mammograms: A Simulation Study. Radiology 2019; 293: 38-46
• 64 Geras KJ, Mann RM, Moy L. Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives. Radiology 2019; 293: 246-259
• 65 Sechopoulos I, Teuwen J, Mann R. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art. Semin Cancer Biol 2020; S1044-579X(20)30135-8
• 66 Safdar NM, Banja JD, Meltzer CC. Ethical considerations in artificial intelligence. Eur J Radiol 2020; 122: 108768
• 67 Langlotz CP, Allen B, Erickson BJ. et al. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology 2019; 291: 781-791
• 68 Lei C, Wei W, Liu Z. et al. Mammography-based radiomic analysis for predicting benign BI-RADS category 4 calcifications. Eur J Radiol 2019; 121: 108711
• 69 Stelzer PD, Steding O, Raudner MW. et al. Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies. Eur J Radiol 2020; 132: 109309
• 70 Berg WA, Zhang Z, Lehrer D. et al. Detection of Breast Cancer with Addition of Annual Screening Ultrasound or a Single Screening MRI to Mammography in Women with Elevated Breast Cancer Risk. JAMA J Am Med Assoc 2012; 307: 1394-1404
• 71 Garra BS, Krasner BH, Horii SC. et al. Improving the Distinction between Benign and Malignant Breast Lesions: The Value of Sonographic Texture Analysis: Ultrason. Imaging 1993; 15: 267-285
• 72 Chabi ML, Borget I, Ardiles R. et al. Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist’s experience. Acad Radiol 2012; 19: 311-319
• 73 Marcon M, Ciritsis A, Rossi C. et al. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study. Eur Radiol Exp 2019; 3: 44
• 74 Zhang Q, Xiao Y, Suo J. et al. Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography. Ultrasound Med Biol 2017; 43: 1058-1069
• 75 Zhang X, Liang M, Yang Z. et al Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classification. Front Oncol 2020; 10: 1621
• 76 Theek B, Opacic T, Magnuska Z. et al. Radiomic analysis of contrast-enhanced ultrasound data. Sci Rep 2018; 8: 11359
• 77 de Fleury EFC, Marcomini K. Impact of radiomics on the breast ultrasound radiologist’s clinical practice: From lumpologist to data wrangler. Eur J Radiol 2020; 131: 109197
• 78 Guo Y, Hu Y, Qiao M. et al. Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma. Clin Breast Cancer 2018; 18: e335-e344
• 79 Qiu X, Jiang Y, Zhao Q. et al. Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?. J Ultrasound Med 2020; 39: 1897-1905
• 80 Yu FH, Wang JX, Ye XH. et al. Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. Eur J Radiol 2019; 119: 108658
• 81 Cardoso F, Kyriakides S, Ohno S. et al. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2019; 30: 1194-1220
• 82 Grueneisen J, Nagarajah J, Buchbender C. et al. Positron Emission Tomography/Magnetic Resonance Imaging for Local Tumor Staging in Patients With Primary Breast Cancer: A Comparison With Positron Emission Tomography/Computed Tomography and Magnetic Resonance Imaging. Invest Radiol 2015; 50: 505-513
• 83 Botsikas D, Kalovidouri A, Becker M. et al. Clinical utility of 18F-FDG-PET/MR for preoperative breast cancer staging. Eur Radiol 2016; 26: 2297-2307
• 84 Vogl WD, Pinker K, Helbich TH. et al Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features. Eur Radiol Exp 2019; 3: 18
• 85 Leithner D, Horvat JV, Bernard-Davila B. et al. A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis. Eur J Nucl Med Mol Imaging 2019; 46: 1878-1888
• 86 Gillies RJ, Beyer T. PET and MRI: Is the Whole Greater than the Sum of Its Parts?. Cancer Res 2016; 76: 6163-6166
• 87 Molina-García D, García-Vicente AM, Pérez-Beteta J. et al. Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate. Ann Nucl Med 2018; 32: 379-388
• 88 Li P, Wang X, Xu C. et al. 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging 2020; 47: 1116-1126
• 89 Kirchner J, Martin O, Umutlu L. et al. Impact of 18F-FDG PET/MR on therapeutic management in high risk primary breast cancer patients – A prospective evaluation of staging algorithms. Eur J Radiol 2020; 128: 108975
• 90 Ellmann S, Seyler L, Gillmann C. et al. Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model. J Vis Exp 2020;
• 91 Gibbs P, Turnbull LW. Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 2003; 50: 92-98
• 92 Bickelhaupt S, Paech D, Kickingereder P. et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J Magn Reson Imaging 2017; 46: 604-616
• 93 Gibbs P, Onishi N, Sadinski M. et al. Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis. J Magn Reson Imaging 2019; 50: 1468-1477
• 94 Milos RI, Pipan F, Kalovidouri A. et al. The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams. Eur Radiol 2020; 30: 6052-6061
• 95 Parekh VS, Jacobs MA. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 2017; 3: 43
• 96 Dietzel M, Baltzer PAT. How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay. Insights Imaging 2018; 9: 325-335
• 97 Dietzel M, Baltzer PA, Vag T. et al. Magnetic resonance mammography in small vs. advanced breast lesions – systematic comparison reveals significant impact of lesion size on diagnostic accuracy in 936 histologically verified breast lesions. RöFo Fortschritte Auf Dem Geb Röntgenstrahlen Nukl 2011; 183: 126-135
• 98 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14: 749-762
• 99 Bluemke DA, Moy L, Bredella MA. et al. Assessing Radiology Research on Artificial Intelligence: A Brief Guide for Authors, Reviewers, and Readers—From the Radiology Editorial Board. Radiology 2019; 294: 487-489
• 100 Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell 2020; 2: e200029
• 101 Wu M, Zhong X, Peng Q. et al. Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting. Eur J Radiol 2019; 114: 175-184
• 102 Liu C, Ding J, Spuhler K. et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging JMRI 2019; 49: 131-140
• 103 Goldhirsch A, Wood WC, Coates AS. et al. Strategies for subtypes – dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22: 1736-1747
• 104 Bhargava R, Clark BZ, Carter GJ. et al. The healthcare value of the Magee Decision AlgorithmTM: use of Magee EquationsTM and mitosis score to safely forgo molecular testing in breast cancer. Mod Pathol 2020; 33: 1563-1570
• 105 Li H, Zhu Y, Burnside ES. 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. Radiology 2016; 281: 382-391
• 106 Boné B, Szabó BK, Perbeck LG. et al. Can Contrast-Enhanced MR Imaging Predict Survival in Breast Cancer?. Acta Radiol 2003; 44: 373-378
• 107 Kim JH, Ko ES, Lim Y. et al. Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 2016; 282: 665-675
• 108 Baltzer PAT, Zoubi R, Burmeister HP. et al. Computer assisted analysis of MR-mammography reveals association between contrast enhancement and occurrence of distant metastasis. Technol Cancer Res Treat 2012; 11: 553-560
• 109 Pinto dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise?. Eur Radiol 2021; 31: 1-4
• 110 Park JE, Kim D, Kim HS. et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 2020; 30: 523-536