Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.8
Journals
Bioengineering
Special Issues
Advances in Breast Cancer Imaging
share announcement

Advances in Breast Cancer Imaging

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 7080

Special Issue Editors

Dr. Ana Isabel Rodrigues Gouveia

E-Mail Website
Guest Editor
1. Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1649-004 Lisboa, Portugal
2. UBI, NECE—Research Center in Business Sciences, Universidade da Beira Interior, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: breast cancer imaging; medical imaging; image processing; artificial intelligence
Dr. Hugo Alexandre Ferreira

E-Mail Website
Guest Editor
Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1649-004 Lisboa, Portugal
Interests: breast cancer diagnosis and intervention; medical imaging; magnetic resonance imaging; data analytics; artificial intelligence; virtual and augmented reality; digital health
Dr. Nuno Matela

E-Mail Website
Guest Editor
Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, 1649-004 Lisboa, Portugal
Interests: breast cancer imaging; digital breast tomosynthesis; positron emission mammography; image reconstruction; image processing

Special Issue Information

Dear Colleagues,

According to the World Health Organization, in 2020, there were over 2 million women newly diagnosed with breast cancer worldwide, and almost 8 million women alive who were diagnosed in the previous 5 years. Breast cancer is not only the most prevalent cancer of all, but it is also the one with the highest societal and economic burden, as accounted for in lost disability-adjusted life years.

Despite all screening programs and technological advances, still over half a million women died worldwide in 2020 from breast cancer. These numbers alone hide a disparate reality between high-, middle- and low-income countries, as the 5-year survival rate after diagnosis in the former is over 90% down to 66% in India and 40% in South Africa, for instance. This means that the successful diagnostic and treatment approaches used in high-income countries should be applied elsewhere. Some of the main barriers to such applications are the limited resources and human expertise in middle- and low-income countries.

We believe that recent technological advances, such as artificial intelligence, can become game changers in this context, providing optimized accessible solutions, upscaling existing medical devices, and empowering healthcare professionals. Therefore, this Special Issue on “Advances in Breast Cancer Imaging” will focus on original research papers and comprehensive reviews, dealing with the specific needs and cutting-edge imaging solutions for breast cancer screening, diagnosis, and therapeutic intervention in middle- and low-income countries, or other work that can be adapted to inspire the development of solutions for this context.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Risk assessment, screening, and diagnosis;
  • Treatment planning and prognosis;
  • Breast density classification;
  • Optimized visualization methods;
  • Image interpretation;
  • Dose assessment;
  • Image-guided assessment, biopsy, and intervention;
  • User training;
  • Artificial intelligence;
  • Virtual and augmented reality;
  • Digital health;
  • Economics and social impact.

Dr. Ana Isabel Rodrigues Gouveia
Dr. Hugo Alexandre Ferreira
Dr. Nuno Matela
Guest Editors

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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
  • diagnosis
  • mammography
  • ultrasound
  • magnetic resonance imaging
  • positron emission tomography
  • positron emission mammography
  • microwave imaging
  • image-guided intervention
  • artificial intelligence
  • virtual reality
  • augmented reality
  • digital health

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 3727 KiB  
Article
Two-Dimensional Mammography Imaging Techniques for Screening Women with Silicone Breast Implants: A Pilot Phantom Study
by Isabelle Fitton, Virginia Tsapaki, Jonathan Zerbib, Antoine Decoux, Amit Kumar, Aude Stembert, Françoise Malchair, Claire Van Ngoc Ty and Laure Fournier
Bioengineering 2024, 11(9), 884; https://doi.org/10.3390/bioengineering11090884 - 31 Aug 2024
Viewed by 364
Abstract
This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce [...] Read more.
This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce these techniques. Images were acquired on a single Hologic Selenia Dimensions® unit. The mammography of the left breast of a single clinical case was included. Three methods of image acquisition were identified. They were based on misused, recommended, and reference settings. In the clinical case, image criteria scoring and the signal-to-noise ratio on breast tissue (SNRBT) were determined for two 2D projections and compared between the three techniques. The phantom study first compared the reference and misused settings by varying the AEC sensor position and, second, the recommended settings with a reduced current-time product (mAs) setting that was 13% lower. The signal-difference-to-noise ratio (SDNR) and detectability indexes at 0.1 mm (d’ 0.1 mm) and 0.25 mm (d’ 0.25 mm) were automatically quantified using ATIA software. Average glandular dose (AGD) values were collected for each acquisition. A statistical analysis was performed using Kruskal–Wallis and corrected Dunn tests (p < 0.05). The SNRBT was 2.6 times lower and the AGD was −18% lower with the reference settings compared to the recommended settings. The SNRBT values increased by +98% with the misused compared to the recommended settings. The AGD increased by +79% with the misused settings versus the recommended settings. The median values of the reference settings were 5.8 (IQR 5.7–5.9), 1.2 (IQR 0.0), 7.0 (IQR 6.8–7.2) and 1.2 (IQR 0.0) mGy and were significantly lower than those of the misused settings (p < 0.03): 7.9 (IQR 6.1–9.7), 1.6 (IQR 1.3–1.9), 9.2 (IQR 7.5–10.9) and 2.2 (IQR 1.4–3.0) mGy for the SDNR, d’ 0.1 mm, d’ 0.25 mm and the AGD, respectively. A comparison of the recommended and reduced settings showed a reduction of −6.1 ± 0.6% (p = 0.83), −7.7 ± 0.0% (p = 0.18), −6.4 ± 0.6% (p = 0.19) and −13.3 ± 1.1% (p = 0.53) for the SDNR, d’ 0.1 mm, d’ 0.25 mm and the AGD, respectively. This study showed that the IAEA phantom could be used to reproduce the three techniques for acquiring 2D mammography images in the presence of breast implants for raising awareness and for educational purposes. It could also be used to evaluate and optimize the manufacturer’s recommended settings. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
►▼ Show Figures

Figure 1

20 pages, 4991 KiB  
Article
An Innovative Thermal Imaging Prototype for Precise Breast Cancer Detection: Integrating Compression Techniques and Classification Methods
by Khaled S. Ahmed, Fayroz F. Sherif, Mohamed S. Abdallah, Young-Im Cho and Shereen M. ElMetwally
Bioengineering 2024, 11(8), 764; https://doi.org/10.3390/bioengineering11080764 - 29 Jul 2024
Viewed by 987
Abstract
Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and [...] Read more.
Breast cancer detection at an early stage is crucial for improving patient survival rates. This work introduces an innovative thermal imaging prototype that incorporates compression techniques inspired by mammography equipment. The prototype offers a radiation-free and precise cancer diagnosis. By integrating compression and illumination methods, thermal picture quality has increased, and the accuracy of classification has improved. Essential components of the suggested thermography device include an equipment body, plates, motors, pressure sensors, light sources, and a thermal camera. We created a 3D model of the gadget using the SolidWorks software 2020 package. Furthermore, the classification research employed both cancer and normal images from the experimental results to validate the efficacy of the suggested system. We employed preprocessing and segmentation methods on the obtained dataset. We successfully categorized the thermal pictures using various classifiers and examined their performance. The logistic regression model showed excellent performance, achieving an accuracy of 0.976, F1 score of 0.977, precision of 1.000, and recall of 0.995. This indicates a high level of accuracy in correctly classifying thermal abnormalities associated with breast cancer. The proposed prototype serves as a highly effective tool for conducting initial investigations into breast cancer detection, offering potential advancements in early-stage diagnosis, and improving patient survival rates. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
►▼ Show Figures

Figure 1

12 pages, 1925 KiB  
Article
Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake
by Sylwia Nowakowska, Karol Borkowski, Carlotta Ruppert, Patryk Hejduk, Alexander Ciritsis, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss and Cristina Rossi
Bioengineering 2024, 11(6), 556; https://doi.org/10.3390/bioengineering11060556 - 31 May 2024
Viewed by 681
Abstract
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually [...] Read more.
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network’s predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

19 pages, 6789 KiB  
Review
New Frontiers in Breast Cancer Imaging: The Rise of AI
by Stephanie B. Shamir, Arielle L. Sasson, Laurie R. Margolies and David S. Mendelson
Bioengineering 2024, 11(5), 451; https://doi.org/10.3390/bioengineering11050451 - 2 May 2024
Cited by 1 | Viewed by 1939
Abstract
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer [...] Read more.
Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
►▼ Show Figures

Figure 1

21 pages, 2643 KiB  
Review
Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review
by Roxana Iacob, Emil Radu Iacob, Emil Robert Stoicescu, Delius Mario Ghenciu, Daiana Marina Cocolea, Amalia Constantinescu, Laura Andreea Ghenciu and Diana Luminita Manolescu
Bioengineering 2024, 11(3), 262; https://doi.org/10.3390/bioengineering11030262 - 7 Mar 2024
Cited by 2 | Viewed by 2249
Abstract
Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited [...] Read more.
Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop