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Bioengineering
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Breast Cancer: From Precision Medicine to Diagnostics
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Breast Cancer: From Precision Medicine to Diagnostics

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 7082

Special Issue Editor

Dr. Kosuke Kawaguchi

E-Mail Website
Guest Editor
Department of Breast Surgery, Kyoto University Hospital, Graduate School of Medicine, Shogoin Sakyo-ku, Kyoto 606-8507, Japan
Interests: breast cancer; breast imaging

Special Issue Information

Dear Colleagues,

Breast cancer is a complex and heterogeneous disease that affects millions of women worldwide. In recent years, significant advancements have been made in the fields of precision medicine, diagnostics, and novel treatment options for patients with breast cancer. Precision medicine allows for more personalized and targeted treatment strategies based on an individual's unique genetic makeup, helping to improve patient outcomes and reduce the risk of recurrence. Diagnostic tools, such as genetic testing and imaging techniques, have also evolved to provide more accurate and early detection of breast cancer, leading to better prognosis and survival rates.

Novel treatment options, such as immunotherapy and targeted therapies, are revolutionizing the way breast cancer is treated, offering more effective and less toxic alternatives to traditional chemotherapy. These innovative approaches are paving the way for more personalized and tailored treatment plans for patients with breast cancer, ultimately improving survival rates and patients’ quality of life. In this era of rapid advancements in breast cancer research, it is crucial to continue exploring new avenues in precision medicine, diagnostics, and treatment options to further improve outcomes for patients battling this devastating disease.

Dr. Kosuke Kawaguchi
Guest Editor

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Keywords

  • breast cancer
  • precision medicine
  • diagnostics
  • novel treatment options
  • personalized treatment
  • tumor microenvironment
  • breast imaging

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Published Papers (5 papers)

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Research

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32 pages, 14159 KB  
Article
Microwave Breast Imaging System Modules, Enhancing Scan Quality and Reliability of Diagnostic Outputs During Clinical Testing
by Giannis Papatrechas, Angie Fasoula, Petros Arvanitis, Luc Duchesne, Alexis Raveneau, Julio Daniel Gil Cano, John O’ Donnell, Sami Abd Elwahab and Michael Kerin
Bioengineering 2025, 12(10), 1079; https://doi.org/10.3390/bioengineering12101079 - 3 Oct 2025
Viewed by 729
Abstract
Microwave Breast Imaging (MWBI) is an emerging imaging modality aiming to detect breast lesions, which are dielectrically contrasted against the background healthy tissue, in the microwave frequency spectrum. MWBI holds potential to outperform X-ray mammography’s low sensitivity in young and dense breasts, thus [...] Read more.
Microwave Breast Imaging (MWBI) is an emerging imaging modality aiming to detect breast lesions, which are dielectrically contrasted against the background healthy tissue, in the microwave frequency spectrum. MWBI holds potential to outperform X-ray mammography’s low sensitivity in young and dense breasts, thus supporting timelier detection of interval cancers, as a supplemental screening or diagnostic imaging method. The specificity of MWBI remains unknown, however, as management of false positives has not been systematically addressed yet. An earlier First-In-Human clinical investigation on 24 symptomatic patients provided proof-of-concept for the Wavelia MWBI sectorized multi-static radar imaging technology, which generates clinically meaningful 3D images of the breast, performs semi-automated detection of breast lesions and extracts diagnostic features to distinguish malignant from benign lesions. This paper focuses on a set of technological upgrades, accessories and data processing modules, designed and implemented in the 2nd generation prototype of Wavelia, to handle the diversity in breast geometry, tissue consistency and deformability, in a larger clinical investigation reporting on the bilateral MWBI scan of 62 patients. The presented add-on modules contribute to enhanced quality of scan and a more valid reference reporting space for the MWBI imaging outputs, with a direct positive impact on overall specificity. Full article
(This article belongs to the Special Issue Breast Cancer: From Precision Medicine to Diagnostics)
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14 pages, 4621 KB  
Article
Radiomics for Detecting Metaplastic Histology in Triple-Negative Breast Cancer: A Step Towards Personalized Therapy
by Rana Gunoz Comert, Gorkem Durak, Ravza Yilmaz, Halil Ertugrul Aktas, Zeynep Tuz, Hongyi Pan, Jun Zeng, Aysel Bayram, Baran Mollavelioglu, Sukru Mehmet Erturk and Ulas Bagci
Bioengineering 2025, 12(9), 973; https://doi.org/10.3390/bioengineering12090973 - 12 Sep 2025
Viewed by 642
Abstract
This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 [...] Read more.
This study aims to develop and validate a multisequence MRI-based radiomics approach for distinguishing metaplastic breast cancer (MBC) from non-metaplastic triple-negative breast cancer (TNBC) at the initial diagnosis, which could facilitate optimal treatment selection. In this retrospective study, we analyzed 105 patients (27 MBC, 78 non-metaplastic TNBC) who underwent standardized breast magnetic resonance imaging (MRI), which included T1-weighted contrast-enhanced (T1W-CE) and short-tau inversion recovery (STIR) sequences. Two radiologists performed ground truth lesion segmentation, verified by a senior radiologist. We extracted 214 radiomic features (using PyRadiomics) and used least absolute shrinkage and selection operator (LASSO) regression for feature selection. Seven machine learning classifiers were thoroughly evaluated using five-fold cross-validation, with performance assessed through ROC analysis and accuracy metrics. The combined T1W-CE and STIR analysis demonstrated superior diagnostic performance for distinguishing MBC from non-metaplastic TNBC (AUC = 0.845; accuracy = 81%) compared with either sequence alone (T1W only AUC = 0.805; accuracy = 80%; STIR only AUC:0.768; accuracy = 77%). Multisequence MRI radiomics can reliably distinguish between MBC and TNBC at the time of initial diagnosis. This could potentially facilitate the selection of more appropriate treatments and help avoid ineffective chemotherapy for MBC patients. Full article
(This article belongs to the Special Issue Breast Cancer: From Precision Medicine to Diagnostics)
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39 pages, 30587 KB  
Article
Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis
by Md. Redwan Ahmed, Hamdadur Rahman, Zishad Hossain Limon, Md Ismail Hossain Siddiqui, Mahbub Alam Khan, Al Shahriar Uddin Khondakar Pranta, Rezaul Haque, S M Masfequier Rahman Swapno, Young-Im Cho and Mohamed S. Abdallah
Bioengineering 2025, 12(6), 651; https://doi.org/10.3390/bioengineering12060651 - 13 Jun 2025
Cited by 4 | Viewed by 1997
Abstract
Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we [...] Read more.
Early and accurate detection of breast cancer is essential for reducing mortality rates and improving clinical outcomes. However, deep learning (DL) models used in healthcare face significant challenges, including concerns about data privacy, domain-specific overfitting, and limited interpretability. To address these issues, we propose BreastSwinFedNetX, a federated learning (FL)-enabled ensemble system that combines four hierarchical variants of the Swin Transformer (Tiny, Small, Base, and Large) with a Random Forest (RF) meta-learner. By utilizing FL, our approach ensures collaborative model training across decentralized and institution-specific datasets while preserving data locality and preventing raw patient data exposure. The model exhibits strong generalization and performs exceptionally well across five benchmark datasets—BreakHis, BUSI, INbreast, CBIS-DDSM, and a Combined dataset—achieving an F1 score of 99.34% on BreakHis, a PR AUC of 98.89% on INbreast, and a Matthews Correlation Coefficient (MCC) of 99.61% on the Combined dataset. To enhance transparency and clinical adoption, we incorporate explainable AI (XAI) through Grad-CAM, which highlights class-discriminative features. Additionally, we deploy the model in a real-time web application that supports uncertainty-aware predictions and clinician interaction and ensures compliance with GDPR and HIPAA through secure federated deployment. Extensive ablation studies and paired statistical analyses further confirm the significance and robustness of each architectural component. By integrating transformer-based architectures, secure collaborative training, and explainable outputs, BreastSwinFedNetX provides a scalable and trustworthy AI solution for real-world breast cancer diagnostics. Full article
(This article belongs to the Special Issue Breast Cancer: From Precision Medicine to Diagnostics)
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19 pages, 620 KB  
Article
Local Extremum Mapping for Weak Supervision Learning on Mammogram Classification and Localization
by Minjuan Zhu, Lei Zhang, Lituan Wang, Zizhou Wang, Yan Wang and Guangwu Qian
Bioengineering 2025, 12(4), 325; https://doi.org/10.3390/bioengineering12040325 - 21 Mar 2025
Viewed by 858
Abstract
The early and accurate detection of breast lesions through mammography is crucial for improving survival rates. However, the existing deep learning-based methods often rely on costly pixel-level annotations, limiting their scalability in real-world applications. To address this issue, a novel local extremum mapping [...] Read more.
The early and accurate detection of breast lesions through mammography is crucial for improving survival rates. However, the existing deep learning-based methods often rely on costly pixel-level annotations, limiting their scalability in real-world applications. To address this issue, a novel local extremum mapping (LEM) mechanism is proposed for mammogram classification and weakly supervised lesion localization. The proposed method first divides the input mammogram into multiple regions and generates score maps through convolutional neural networks. Then, it identifies the most informative regions by filtering local extrema in the score maps and aggregating their scores for final classification. This strategy enables lesion localization with only image-level labels, significantly reducing annotation costs. Experiments on two public mammography datasets, CBIS-DDSM and INbreast, demonstrate that the proposed method achieves competitive performance. On the INbreast dataset, LEM improves classification accuracy to 96.3% with an AUC of 0.976. Furthermore, the proposed method effectively localizes lesions with a dice similarity coefficient of 0.37, outperforming Grad-CAM and other baseline approaches. These results highlight the practical significance and potential clinical applications of our approach, making automated mammogram analysis more accessible and efficient. Full article
(This article belongs to the Special Issue Breast Cancer: From Precision Medicine to Diagnostics)
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Review

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15 pages, 1538 KB  
Review
Breathomics: A Non-Invasive Approach for the Diagnosis of Breast Cancer
by Hélène Yockell-Lelièvre, Romy Philip, Palash Kaushik, Ashok Prabhu Masilamani and Sarkis H. Meterissian
Bioengineering 2025, 12(4), 411; https://doi.org/10.3390/bioengineering12040411 - 12 Apr 2025
Viewed by 2403
Abstract
Breast cancer is the most commonly diagnosed cancer worldwide, underscoring the critical need for effective early detection methods to reduce mortality. Traditional detection techniques, such as mammography, present significant limitations, particularly in women with dense breast tissue, highlighting the need for alternative screening [...] Read more.
Breast cancer is the most commonly diagnosed cancer worldwide, underscoring the critical need for effective early detection methods to reduce mortality. Traditional detection techniques, such as mammography, present significant limitations, particularly in women with dense breast tissue, highlighting the need for alternative screening approaches. Breathomics, based on the analysis of Volatile Organic Compounds (VOCs) present in exhaled breath, offers a non-invasive, potentially transformative diagnostic tool. These VOCs are metabolic byproducts from various organs of the human body whose presence and varying concentrations in breath are reflective of different health conditions. This review explores the potential of breathomics, highlighting its promise as a rapid, cost-effective screening approach for breast cancer, facilitated through the integration of portable solutions like electronic noses (e-noses). Key considerations for clinical translation—including patient selection, environmental confounders, and different breath collection methods—will be examined in terms of how each of them affects the breath profile. However, there are also challenges such as patient variability in VOC signatures, and the need for standardization in breath sampling protocols. Future research should prioritize standardizing sampling and analytical procedures and validating their clinical utility through large-scale clinical trials. Full article
(This article belongs to the Special Issue Breast Cancer: From Precision Medicine to Diagnostics)
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