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Medicina
Special Issues
New Developments in Diagnosis and Management of Breast Cancer
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New Developments in Diagnosis and Management of Breast Cancer

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 1410

Special Issue Editors

Prof. Dr. Daliborca Cristina Vlad

E-Mail Website
Guest Editor
Department of Pharmacology ''Victor Babes'' University of Medicine and Pharmacy Timisoara, Romania
Interests: clinical and experimental pharmacology; ethnopharmacology; immunology; immunopathology; clinical trials
Dr. Ionut Marcel Cobec

E-Mail Website
Guest Editor
1. Clinic of Obstetrics and Gynecology, Klinikum Freudenstadt, 72250 Freudenstadt, Germany
2. ANAPATMOL Research Center, Faculty of Medicine, “Victor Babes” University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
Interests: breast cancer; gynecologic oncology; gynecologic cancers

Special Issue Information

Dear Colleagues,

Breast cancer continues to be the most prevalent malignancy among women and remains the leading cancer diagnosis worldwide. Despite the significant progress made, it presents an ongoing clinical challenge due to its biological heterogeneity and variable prognosis.

The landscape of breast cancer management and therapy is rapidly evolving, driven by the advent of innovative diagnostic tools and therapeutic strategies. Modern advancements in molecular profiling, targeted therapies, and precision medicine have significantly improved disease outcomes and enhanced overall survival rates. These developments underscore the necessity for a personalized approach to both diagnosis and treatment, tailored to the unique biological characteristics of each patient’s case of the disease.

However, the dynamic nature of this field necessitates continuous research to address existing gaps and explore novel methodologies. This Special Issue will gather groundbreaking studies and comprehensive reviews that illuminate the latest trends, innovations, and scientific breakthroughs in the diagnosis and management of breast cancer. By fostering knowledge dissemination, this Special Issue will contribute meaningfully to the advancement of clinical practice and future research directions.

It is with great respect and anticipation that I invite you to submit your original research articles or systematic reviews to this Special Issue. Your contributions will not only serve as a testament to the progress already made in this critical field but will also act as a foundation for shaping future innovations and improving patient outcomes in breast cancer care.

I look forward to receiving your contributions.

Prof. Dr. Daliborca Cristina Vlad
Dr. Ionut Marcel Cobec
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. Medicina 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 2200 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
  • diagnosis
  • management
  • therapy
  • targeted therapy
  • triple-negative breast cancer
  • screening
  • overall survival
  • prognosis
  • immunotherapy
  • surgery
  • new techniques
  • staging

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

Published Papers (2 papers)

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Research

22 pages, 7258 KiB  
Article
AI in 2D Mammography: Improving Breast Cancer Screening Accuracy
by Sebastian Ciurescu, Simona Cerbu, Ciprian Nicușor Dima, Florina Borozan, Raluca Pârvănescu, Diana-Gabriela Ilaș, Cosmin Cîtu, Corina Vernic and Ioan Sas
Medicina 2025, 61(5), 809; https://doi.org/10.3390/medicina61050809 - 26 Apr 2025
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Abstract
Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast [...] Read more.
Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. This study aimed to assess the utility of AI in improving lesion detection and classification in 2D mammography. Materials and Methods: A retrospective analysis was performed on a dataset of 578 mammographic images obtained from a single radiology center. The dataset consisted of 36% pathologic and 64% normal cases, and was partitioned into training (403 images), validation (87 images), and test (88 images) sets. Image preprocessing involved grayscale conversion, contrast-limited adaptive histogram equalization (CLAHE), noise reduction, and sharpening. A convolutional neural network (CNN) model was developed using transfer learning with ResNet50. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC-ROC) curve. Results: The AI model achieved an overall classification accuracy of 88.5% and an AUC-ROC of 0.93, demonstrating strong discriminative capability between normal and pathologic cases. Notably, the model exhibited a high specificity of 92.7%, contributing to a reduction in false positives and improved screening efficiency. Conclusions: AI-assisted 2D mammography holds potential to enhance breast cancer detection by improving lesion classification and reducing false-positive findings. Although the model achieved high specificity, further optimization is required to minimize false negatives. Future efforts should aim to improve model sensitivity, incorporate multimodal imaging techniques, and validate results across larger, multicenter prospective cohorts to ensure effective integration into clinical radiology workflows. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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16 pages, 22961 KiB  
Article
Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer
by Mihriban Erdogan, Canan Kelten Talu, Zeliha Guzeloz, Gonul Demir, Ferhat Eyiler, Seval Akay, Ezgi Yilmaz and Olcun Umit Unal
Medicina 2025, 61(4), 710; https://doi.org/10.3390/medicina61040710 - 12 Apr 2025
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Abstract
Background and Objectives: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph [...] Read more.
Background and Objectives: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph node metastasis in early-stage breast cancer. High PR expression is often more prevalent in the luminal A subgroup, which is associated with a better prognosis. The aim of this study was to determine the relationship between the percentage of PR expression and the likelihood of axillary metastasis in Her-2-negative, clinical T1-T2N0 luminal type breast cancer. Materials and Methods: A hundred and ninety-nine cases with luminal type, Her-2-negative, clinically and radiologically axilla-negative T1-T2 breast cancer who received radiotherapy were evaluated retrospectively. The pathological specimens were assessed by an experienced pathologist. Results: The statistical evaluation showed that tumor diameter greater than 2 cm, (p = 0.003), presence of lymphovascular invasion (p = 0.001), and PR expression level below 80% (p = 0.037) were identified as significant predictors of lymph node positivity in breast cancer patients. Conclusions: Percentage of progesterone receptor expression along with other molecular biological markers and clinicopathological parameters should be evaluated altogether when predicting axillary metastasis risk before surgery. Full article
(This article belongs to the Special Issue New Developments in Diagnosis and Management of Breast Cancer)
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