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Diagnostics
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Artificial Intelligence in Breast Imaging
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Artificial Intelligence in Breast Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 11253

Special Issue Editor

Dr. Fernando Collado-Mesa

E-Mail Website
Guest Editor
Department of Radiology, University of Miami Leonard M. Miller School of Medicine, Miami, FL, USA
Interests: digital mammography; digital breast tomosynthesis; breast ultrasound; breast MRI; image-guided breast procedures; breast cancer; benign breast disease; artificial intelligence; machine learning; natural language processing

Special Issue Information

Dear Colleagues, 

Over the last few years, artificial intelligence (AI) has seen extraordinary advances. These advances have been made possible by the availability of a large quantity of digital data, powerful computational resources, state-of-the-art graphic processing units, and advanced neural networks, amongst others. Within the field of medicine, breast imaging radiology has seen exponential growth in the number of AI applications, marked and cleared by regulatory agencies, for use in clinical practice. The tasks these applications perform range from breast cancer risk and breast tissue density assessment to triage and diagnostic tools. It is expected that both the development and the implementation into clinical practice of these types of applications will continue to increase into the near future. In accordance with the policy of the journal Diagnostics, the aim of this Special Issue is to review relevant topics related to the development, implementation, and evaluation of AI-based applications in breast imaging radiology.

Dr. Fernando Collado-Mesa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • breast imaging
  • artificial intelligence
  • machine learning
  • clinical applications
  • clinical implementation

Published Papers (5 papers)

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Research

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12 pages, 1554 KiB  
Article
Mammographic Breast Density Model Using Semi-Supervised Learning Reduces Inter-/Intra-Reader Variability
by Alyssa T. Watanabe, Tara Retson, Junhao Wang, Richard Mantey, Chiyung Chim and Homa Karimabadi
Diagnostics 2023, 13(16), 2694; https://doi.org/10.3390/diagnostics13162694 - 16 Aug 2023
Cited by 2 | Viewed by 1301
Abstract
Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of [...] Read more.
Breast density is an important risk factor for breast cancer development; however, imager inconsistency in density reporting can lead to patient and clinician confusion. A deep learning (DL) model for mammographic density grading was examined in a retrospective multi-reader multi-case study consisting of 928 image pairs and assessed for impact on inter- and intra-reader variability and reading time. Seven readers assigned density categories to the images, then re-read the test set aided by the model after a 4-week washout. To measure intra-reader agreement, 100 image pairs were blindly double read in both sessions. Linear Cohen Kappa (κ) and Student’s t-test were used to assess the model and reader performance. The model achieved a κ of 0.87 (95% CI: 0.84, 0.89) for four-class density assessment and a κ of 0.91 (95% CI: 0.88, 0.93) for binary non-dense/dense assessment. Superiority tests showed significant reduction in inter-reader variability (κ improved from 0.70 to 0.88, p ≤ 0.001) and intra-reader variability (κ improved from 0.83 to 0.95, p ≤ 0.01) for four-class density, and significant reduction in inter-reader variability (κ improved from 0.77 to 0.96, p ≤ 0.001) and intra-reader variability (κ improved from 0.89 to 0.97, p ≤ 0.01) for binary non-dense/dense assessment when aided by DL. The average reader mean reading time per image pair also decreased by 30%, 0.86 s (95% CI: 0.01, 1.71), with six of seven readers having reading time reductions. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Imaging)
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9 pages, 1137 KiB  
Article
Deep Learning Analysis of Mammography for Breast Cancer Risk Prediction in Asian Women
by Hayoung Kim, Jihe Lim, Hyug-Gi Kim, Yunji Lim, Bo Kyoung Seo and Min Sun Bae
Diagnostics 2023, 13(13), 2247; https://doi.org/10.3390/diagnostics13132247 - 3 Jul 2023
Cited by 1 | Viewed by 1779
Abstract
The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women [...] Read more.
The purpose of this study was to develop a mammography-based deep learning (DL) model for predicting the risk of breast cancer in Asian women. This retrospective study included 287 examinations in 153 women in the cancer group and 736 examinations in 447 women in the negative group, obtained from the databases of two tertiary hospitals between November 2012 and March 2022. All examinations were labeled as either dense breast or nondense breast, and then randomly assigned to either training, validation, or test sets. DL models, referred to as image-level and examination-level models, were developed. Both models were trained to predict whether or not the breast would develop breast cancer with two datasets: the whole dataset and the dense-only dataset. The performance of DL models was evaluated using the accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). On a test set, performance metrics for the four scenarios were obtained: image-level model with whole dataset, image-level model with dense-only dataset, examination-level model with whole dataset, and examination-level model with dense-only dataset with AUCs of 0.71, 0.75, 0.66, and 0.67, respectively. Our DL models using mammograms have the potential to predict breast cancer risk in Asian women. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Imaging)
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12 pages, 1918 KiB  
Article
A Deep Learning Approach for Diagnosis Support in Breast Cancer Microwave Tomography
by Stefano Franceschini, Maria Maddalena Autorino, Michele Ambrosanio, Vito Pascazio and Fabio Baselice
Diagnostics 2023, 13(10), 1693; https://doi.org/10.3390/diagnostics13101693 - 10 May 2023
Cited by 2 | Viewed by 1720
Abstract
In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great [...] Read more.
In this paper, a deep learning technique for tumor detection in a microwave tomography framework is proposed. Providing an easy and effective imaging technique for breast cancer detection is one of the main focuses for biomedical researchers. Recently, microwave tomography gained a great attention due to its ability to reconstruct the electric properties maps of the inner breast tissues, exploiting nonionizing radiations. A major drawback of tomographic approaches is related to the inversion algorithms, since the problem at hand is nonlinear and ill-posed. In recent decades, numerous studies focused on image reconstruction techniques, in same cases exploiting deep learning. In this study, deep learning is exploited to provide information about the presence of tumors based on tomographic measures. The proposed approach has been tested with a simulated database showing interesting performances, in particular for scenarios where the tumor mass is particularly small. In these cases, conventional reconstruction techniques fail in identifying the presence of suspicious tissues, while our approach correctly identifies these profiles as potentially pathological. Therefore, the proposed method can be exploited for early diagnosis purposes, where the mass to be detected can be particularly small. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Imaging)
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Review

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12 pages, 452 KiB  
Review
Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning’s Role in Breast Imaging beyond Screening Mammography
by Tara A. Retson and Mohammad Eghtedari
Diagnostics 2023, 13(13), 2133; https://doi.org/10.3390/diagnostics13132133 - 21 Jun 2023
Cited by 1 | Viewed by 2123
Abstract
Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve [...] Read more.
Artificial intelligence (AI) applications in mammography have gained significant popular attention; however, AI has the potential to revolutionize other aspects of breast imaging beyond simple lesion detection. AI has the potential to enhance risk assessment by combining conventional factors with imaging and improve lesion detection through a comparison with prior studies and considerations of symmetry. It also holds promise in ultrasound analysis and automated whole breast ultrasound, areas marked by unique challenges. AI’s potential utility also extends to administrative tasks such as MQSA compliance, scheduling, and protocoling, which can reduce the radiologists’ workload. However, adoption in breast imaging faces limitations in terms of data quality and standardization, generalizability, benchmarking performance, and integration into clinical workflows. Developing methods for radiologists to interpret AI decisions, and understanding patient perspectives to build trust in AI results, will be key future endeavors, with the ultimate aim of fostering more efficient radiology practices and better patient care. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Imaging)
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18 pages, 661 KiB  
Review
Natural Language Processing for Breast Imaging: A Systematic Review
by Kareem Mahmoud Diab, Jamie Deng, Yusen Wu, Yelena Yesha, Fernando Collado-Mesa and Phuong Nguyen
Diagnostics 2023, 13(8), 1420; https://doi.org/10.3390/diagnostics13081420 - 14 Apr 2023
Cited by 4 | Viewed by 3691
Abstract
Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP [...] Read more.
Natural Language Processing (NLP) has gained prominence in diagnostic radiology, offering a promising tool for improving breast imaging triage, diagnosis, lesion characterization, and treatment management in breast cancer and other breast diseases. This review provides a comprehensive overview of recent advances in NLP for breast imaging, covering the main techniques and applications in this field. Specifically, we discuss various NLP methods used to extract relevant information from clinical notes, radiology reports, and pathology reports and their potential impact on the accuracy and efficiency of breast imaging. In addition, we reviewed the state-of-the-art in NLP-based decision support systems for breast imaging, highlighting the challenges and opportunities of NLP applications for breast imaging in the future. Overall, this review underscores the potential of NLP in enhancing breast imaging care and offers insights for clinicians and researchers interested in this exciting and rapidly evolving field. Full article
(This article belongs to the Special Issue Artificial Intelligence in Breast Imaging)
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