Advances in Early Diagnosis and Minimally Invasive Management in Breast Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 655

Special Issue Editor


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Department of Life Science, Health and Health Professions, Link Campus University of Rome School of Medicine, 00165 Rome, Italy
Interests: imaging; diagnostic; breast imaging; breast prevention
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Special Issue Information

Dear Colleagues,

Breast cancer remains a leading cause of mortality and morbidity worldwide. Advancements in early diagnosis and minimally invasive treatment options are critical in improving patient outcomes. This Special Issue will bring together cutting-edge research and reviews focusing on precision medicine, early detection technologies, risk prediction models, and the integration of artificial intelligence into diagnostic workflows. We welcome contributions exploring innovative therapeutic strategies, advances in image analysis, and preventative measures in breast cancer management. By consolidating these efforts, this Special Issue will advance our understanding and clinical management of breast cancer, aligning with the journal’s mission to foster impactful oncological research.

We are happy to invite you to contribute to this Special Issue, entitled "Advances in Early Diagnosis and Minimally Invasive Management in Breast Cancer". Breast cancer remains one of the main causes of oncological mortality among women worldwide. Although there has been significant progress, there are still numerous obstacles complicating early detection and the optimization of minimally invasive treatment approaches. Emerging technologies, including artificial intelligence and precision medicine, have entered the diagnostic and therapeutic landscape, opening up new and promising avenues which could soon optimize patient outcomes. This Special Issue will explore some of these improvements in depth and facilitate a dialogue among researchers and clinicians in this critical area.

For this Special Issue, we invite high-quality contributions sharing innovative approaches to the early diagnosis and minimally invasive management of breast cancer. Possible topics include new frontiers in precision medicine, updates to risk prediction models, and the integration of advanced technologies, such as AI and modern imaging.

This fits well with the scope of the journal Cancers, which is focused on the quality and translational impact of oncology research in clinical practice. We plan to publish at least 10 articles, with the aim of curating a collection that is multi-disciplinary and further advances the study of breast cancer.

For this Special Issue, we welcome original research articles and review articles.

Possible research topics include, but are not limited to, the following:

  • Precision medicine and personalized approaches in breast cancer care;
  • Innovative techniques in early detection and cancer screening;
  • Minimally invasive therapeutic strategies;
  • Artificial intelligence applications in diagnosis and treatment planning;
  • Advanced imaging and image analysis methods;
  • Risk prediction and prevention models.

We look forward to receiving your valuable contributions and to advancing our understanding and management of breast cancer through this collaborative effort.

Dr. Graziella Di Grezia
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 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. Cancers 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 2900 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

  • precision medicine
  • breast cancer prevention
  • cancer detection
  • early diagnosis
  • therapeutic strategies
  • artificial intelligence
  • image analysis
  • risk predictors

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Published Papers (1 paper)

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Research

12 pages, 456 KiB  
Article
From Variability to Standardization: The Impact of Breast Density on Background Parenchymal Enhancement in Contrast-Enhanced Mammography and the Need for a Structured Reporting System
by Graziella Di Grezia, Antonio Nazzaro, Luigi Schiavone, Cisternino Elisa, Alessandro Galiano, Gatta Gianluca, Cuccurullo Vincenzo and Mariano Scaglione
Cancers 2025, 17(15), 2523; https://doi.org/10.3390/cancers17152523 - 30 Jul 2025
Viewed by 428
Abstract
Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. [...] Read more.
Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R2 = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article
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