Literature DB >> 34392105

AI-enhanced breast imaging: Where are we and where are we heading?

Almir Bitencourt1, Isaac Daimiel Naranjo2, Roberto Lo Gullo3, Carolina Rossi Saccarelli4, Katja Pinker5.   

Abstract

Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Artificial Intelligence; Breast neoplasms; Deep learning; Magnetic resonance imaging; Mammography; Ultrasound

Mesh:

Year:  2021        PMID: 34392105      PMCID: PMC8387447          DOI: 10.1016/j.ejrad.2021.109882

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   4.531


  93 in total

1.  Preliminary Study on Molecular Subtypes of Breast Cancer Based on Magnetic Resonance Imaging Texture Analysis.

Authors:  Xinru Sun; Bing He; Xin Luo; Yuhua Li; Jinfeng Cao; Jinlan Wang; Jun Dong; Xiaoyu Sun; Guangxia Zhang
Journal:  J Comput Assist Tomogr       Date:  2018 Jul/Aug       Impact factor: 1.826

2.  Breast cancer molecular subtype classifier that incorporates MRI features.

Authors:  Elizabeth J Sutton; Brittany Z Dashevsky; Jung Hun Oh; Harini Veeraraghavan; Aditya P Apte; Sunitha B Thakur; Elizabeth A Morris; Joseph O Deasy
Journal:  J Magn Reson Imaging       Date:  2016-01-12       Impact factor: 4.813

3.  Combined texture analysis and machine learning in suspicious calcifications detected by mammography: Potential to avoid unnecessary stereotactical biopsies.

Authors:  P D Stelzer; O Steding; M W Raudner; G Euller; P Clauser; P A T Baltzer
Journal:  Eur J Radiol       Date:  2020-09-28       Impact factor: 3.528

Review 4.  Machine Learning in Medical Imaging.

Authors:  Maryellen L Giger
Journal:  J Am Coll Radiol       Date:  2018-02-02       Impact factor: 5.532

Review 5.  Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment.

Authors:  Katja Pinker; Joanne Chin; Amy N Melsaether; Elizabeth A Morris; Linda Moy
Journal:  Radiology       Date:  2018-06       Impact factor: 11.105

6.  Development of a Deep Learning-Based Model for Diagnosing Breast Nodules With Ultrasound.

Authors:  Jianming Li; Yunyun Bu; Shuqiang Lu; Hao Pang; Chang Luo; Yujiang Liu; Linxue Qian
Journal:  J Ultrasound Med       Date:  2020-08-08       Impact factor: 2.153

7.  Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data.

Authors:  Wentian Guo; Hui Li; Yitan Zhu; Li Lan; Shengjie Yang; Karen Drukker; Elizabeth Morris; Elizabeth Burnside; Gary Whitman; Maryellen L Giger; Yuan Ji
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-23

8.  Radiogenomics Monitoring in Breast Cancer Identifies Metabolism and Immune Checkpoints as Early Actionable Mechanisms of Resistance to Anti-angiogenic Treatment.

Authors:  Shaveta Mehta; Nick P Hughes; Sonia Li; Adrian Jubb; Rosie Adams; Simon Lord; Lefteris Koumakis; Ruud van Stiphout; Anwar Padhani; Andreas Makris; Francesca M Buffa; Adrian L Harris
Journal:  EBioMedicine       Date:  2016-07-16       Impact factor: 8.143

9.  Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results.

Authors:  Doris Leithner; Joao V Horvat; Maria Adele Marino; Blanca Bernard-Davila; Maxine S Jochelson; R Elena Ochoa-Albiztegui; Danny F Martinez; Elizabeth A Morris; Sunitha Thakur; Katja Pinker
Journal:  Breast Cancer Res       Date:  2019-09-12       Impact factor: 6.466

10.  An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies.

Authors:  Nina Pötsch; Matthias Dietzel; Panagiotis Kapetas; Paola Clauser; Katja Pinker; Stephan Ellmann; Michael Uder; Thomas Helbich; Pascal A T Baltzer
Journal:  Eur Radiol       Date:  2021-03-20       Impact factor: 5.315

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  4 in total

1.  Editorial: Impact of Breast MRI on Breast Cancer Treatment and Prognosis.

Authors:  Almir Bitencourt; Mami Iima; Georg Langs; Katja Pinker
Journal:  Front Oncol       Date:  2022-03-11       Impact factor: 6.244

2.  Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging.

Authors:  Wenyi Yue; Hongtao Zhang; Juan Zhou; Guang Li; Zhe Tang; Zeyu Sun; Jianming Cai; Ning Tian; Shen Gao; Jinghui Dong; Yuan Liu; Xu Bai; Fugeng Sheng
Journal:  Front Oncol       Date:  2022-08-11       Impact factor: 5.738

Review 3.  Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine.

Authors:  Srushti S Mahant; Anuj R Varma
Journal:  Cureus       Date:  2022-09-08

4.  A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses.

Authors:  Matteo Interlenghi; Christian Salvatore; Veronica Magni; Gabriele Caldara; Elia Schiavon; Andrea Cozzi; Simone Schiaffino; Luca Alessandro Carbonaro; Isabella Castiglioni; Francesco Sardanelli
Journal:  Diagnostics (Basel)       Date:  2022-01-13
  4 in total

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