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Diagnostics
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Updates on Breast Cancer: Diagnosis and Management
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Updates on Breast Cancer: Diagnosis and Management

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 1915

Special Issue Editor

Prof. Dr. Kefah Mokbel

E-Mail Website
Guest Editor
The London Breast Institute, Princess Grace Hospital, 42-52 Nottingham Place, London W1U 5NY, UK
Interests: breast cancer; stem cells; gene profiling; autophagy; liquid biopsy; microRNA
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am delighted to invite you to contribute articles to a Special Issue of Diagnostics, a renowned journal with an impact factor of 3.6. As the Guest Editor of this Special Issue, titled "Breast Disease, Diagnosis, and Management," I am eager to showcase the latest advances in breast cancer diagnosis and treatment.

Breast cancer remains a significant healthcare challenge, and through this Special Issue, we aim to highlight innovative approaches and emerging trends that are shaping the landscape of breast cancer management. Your valuable contributions can illuminate novel diagnostic techniques, therapeutic interventions, prognostic markers, and multidisciplinary approaches, revolutionizing how we understand and treat this disease.

We welcome original research articles, review papers, and perspectives on breast cancer diagnosis and management. Your insights and expertise will undoubtedly enrich the discourse and contribute to advancing our collective understanding of breast cancer.

This invitation is an opportunity to showcase your research to a global audience of clinicians, researchers, and healthcare professionals. We look forward to receiving your submissions and collaborating to make this Special Issue successful.

Prof. Dr. Kefah Mokbel
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 cancer
  • breast tumor
  • mammography
  • tissue biopsy
  • breast ultrasound
  • diagnosis and management
  • prognosis
  • treatment

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

Published Papers (2 papers)

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24 pages, 7554 KiB  
Article
Comparative Evaluation of Machine Learning-Based Radiomics and Deep Learning for Breast Lesion Classification in Mammography
by Alessandro Stefano, Fabiano Bini, Eleonora Giovagnoli, Mariangela Dimarco, Nicolò Lauciello, Daniela Narbonese, Giovanni Pasini, Franco Marinozzi, Giorgio Russo and Ildebrando D’Angelo
Diagnostics 2025, 15(8), 953; https://doi.org/10.3390/diagnostics15080953 - 9 Apr 2025
Viewed by 141
Abstract
Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation [...] Read more.
Background: Breast cancer is the second leading cause of cancer-related mortality among women, accounting for 12% of cases. Early diagnosis, based on the identification of radiological features, such as masses and microcalcifications in mammograms, is crucial for reducing mortality rates. However, manual interpretation by radiologists is complex and subject to variability, emphasizing the need for automated diagnostic tools to enhance accuracy and efficiency. This study compares a radiomics workflow based on machine learning (ML) with a deep learning (DL) approach for classifying breast lesions as benign or malignant. Methods: matRadiomics was used to extract radiomics features from mammographic images of 1219 patients from the CBIS-DDSM public database, including 581 cases of microcalcifications and 638 of masses. Among the ML models, a linear discriminant analysis (LDA) demonstrated the best performance for both lesion types. External validation was conducted on a private dataset of 222 images to evaluate generalizability to an independent cohort. Additionally, a deep learning approach based on the EfficientNetB6 model was employed for comparison. Results: The LDA model achieved a mean validation AUC of 68.28% for microcalcifications and 61.53% for masses. In the external validation, AUC values of 66.9% and 61.5% were obtained, respectively. In contrast, the EfficientNetB6 model demonstrated superior performance, achieving an AUC of 81.52% for microcalcifications and 76.24% for masses, highlighting the potential of DL for improved diagnostic accuracy. Conclusions: This study underscores the limitations of ML-based radiomics in breast cancer diagnosis. Deep learning proves to be a more effective approach, offering enhanced accuracy and supporting clinicians in improving patient management. Full article
(This article belongs to the Special Issue Updates on Breast Cancer: Diagnosis and Management)
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10 pages, 882 KiB  
Systematic Review
Assessing the Efficacy of Radioactive Iodine Seed Localisation in Targeted Axillary Dissection for Node-Positive Early Breast Cancer Patients Undergoing Neoadjuvant Systemic Therapy: A Systematic Review and Pooled Analysis
by Munaser Alamoodi, Umar Wazir, Janhavi Venkataraman, Reham Almukbel and Kefah Mokbel
Diagnostics 2024, 14(11), 1175; https://doi.org/10.3390/diagnostics14111175 - 2 Jun 2024
Cited by 1 | Viewed by 1319
Abstract
Targeted axillary dissection (TAD), employing marked lymph node biopsy (MLNB) alongside sentinel lymph node biopsy (SLNB), is increasingly recognised for its efficacy in reducing false negative rates (FNRs) in node-positive early breast cancer patients receiving neoadjuvant systemic therapy (NST). One such method, 125 [...] Read more.
Targeted axillary dissection (TAD), employing marked lymph node biopsy (MLNB) alongside sentinel lymph node biopsy (SLNB), is increasingly recognised for its efficacy in reducing false negative rates (FNRs) in node-positive early breast cancer patients receiving neoadjuvant systemic therapy (NST). One such method, 125I radioactive seed localisation (RSL), involves implanting a seed into a biopsy-proven lymph node either pre- or post-NST. This systematic review and pooled analysis aimed to assess the performance of RSL in TAD among node-positive patients undergoing NST. Six studies, encompassing 574 TAD procedures, met the inclusion criteria. Results showed a 100% successful deployment rate, with a 97.6% successful localisation rate and a 99.8% retrieval rate. Additionally, there was a 60.0% concordance rate between SLNB and MLNB. The FNR of SLNB alone was significantly higher than it was for MLNB (18.8% versus 5.3%, respectively; p = 0.001). Pathological complete response (pCR) was observed in 44% of cases (248/564). On average, the interval from 125I seed deployment to surgery was 75.8 days (range: 0–272). These findings underscore the efficacy of RSL in TAD for node-positive patients undergoing NST, enabling precise axillary pCR identification and facilitating the safe omission of axillary lymph node dissection. Full article
(This article belongs to the Special Issue Updates on Breast Cancer: Diagnosis and Management)
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