Literature DB >> 34689871

Clinical Artificial Intelligence Applications: Breast Imaging.

Qiyuan Hu1, Maryellen L Giger2.   

Abstract

This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast cancer; Computer-aided diagnosis; Deep learning; Diagnosis; Machine learning; Medical imaging; Screening; Treatment response

Mesh:

Year:  2021        PMID: 34689871      PMCID: PMC9075017          DOI: 10.1016/j.rcl.2021.07.010

Source DB:  PubMed          Journal:  Radiol Clin North Am        ISSN: 0033-8389            Impact factor:   1.947


  90 in total

1.  Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study.

Authors:  Akiko Shimauchi; Maryellen L Giger; Neha Bhooshan; Li Lan; Lorenzo L Pesce; John K Lee; Hiroyuki Abe; Gillian M Newstead
Journal:  Radiology       Date:  2011-01-06       Impact factor: 11.105

2.  Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.

Authors:  Weijie Chen; Maryellen L Giger; Hui Li; Ulrich Bick; Gillian M Newstead
Journal:  Magn Reson Med       Date:  2007-09       Impact factor: 4.668

3.  Preoperative MRI improves prediction of extensive occult axillary lymph node metastases in breast cancer patients with a positive sentinel lymph node biopsy.

Authors:  Christopher Loiselle; Peter R Eby; Janice N Kim; Kristine E Calhoun; Kimberly H Allison; Vijayakrishna K Gadi; Sue Peacock; Barry E Storer; David A Mankoff; Savannah C Partridge; Constance D Lehman
Journal:  Acad Radiol       Date:  2014-01       Impact factor: 3.173

Review 4.  Machine Learning in Medical Imaging.

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

5.  Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients.

Authors:  Karen Drukker; Alexandra Edwards; Christopher Doyle; John Papaioannou; Kirti Kulkarni; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2019-09-30

6.  Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.

Authors:  Hui Li; Kayla R Mendel; Li Lan; Deepa Sheth; Maryellen L Giger
Journal:  Radiology       Date:  2019-02-12       Impact factor: 29.146

7.  Using Convolutional Neural Networks for Enhanced Capture of Breast Parenchymal Complexity Patterns Associated with Breast Cancer Risk.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Meng-Kang Hsieh; Lauren Pantalone; Emily F Conant; Despina Kontos
Journal:  Acad Radiol       Date:  2018-02-01       Impact factor: 3.173

8.  Population-Based Assessment of the Association Between Magnetic Resonance Imaging Background Parenchymal Enhancement and Future Primary Breast Cancer Risk.

Authors:  Vignesh A Arasu; Diana L Miglioretti; Brian L Sprague; Nila H Alsheik; Diana S M Buist; Louise M Henderson; Sally D Herschorn; Janie M Lee; Tracy Onega; Garth H Rauscher; Karen J Wernli; Constance D Lehman; Karla Kerlikowske
Journal:  J Clin Oncol       Date:  2019-01-09       Impact factor: 50.717

9.  Quantitative assessment of background parenchymal enhancement in breast MRI predicts response to risk-reducing salpingo-oophorectomy: preliminary evaluation in a cohort of BRCA1/2 mutation carriers.

Authors:  Shandong Wu; Susan P Weinstein; Michael J DeLeo; Emily F Conant; Jinbo Chen; Susan M Domchek; Despina Kontos
Journal:  Breast Cancer Res       Date:  2015-05-19       Impact factor: 6.466

10.  Abbreviated breast MRI for screening women with dense breast: the EA1141 trial.

Authors:  Christiane K Kuhl
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

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  3 in total

Review 1.  Updates in Artificial Intelligence for Breast Imaging.

Authors:  Manisha Bahl
Journal:  Semin Roentgenol       Date:  2021-12-31       Impact factor: 0.709

2.  Clinical Application of Computer-Aided Diagnosis for Breast Ultrasonography: Factors That Lead to Discordant Results in Radial and Antiradial Planes.

Authors:  Ying Zhu; Weiwei Zhan; Xiaohong Jia; Juan Liu; Jianqiao Zhou
Journal:  Cancer Manag Res       Date:  2022-02-23       Impact factor: 3.989

3.  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
  3 in total

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