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Advances in Breast Cancer Imaging: From Detection to Personalized Diagnosis
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Advances in Breast Cancer Imaging: From Detection to Personalized Diagnosis

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 249

Special Issue Editors

Dr. Nuno Matela

E-Mail Website
Guest Editor
1. Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1649-004 Lisbon, Portugal
2. Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1649-004 Lisbon, Portugal
Interests: breast cancer imaging; digital breast tomosynthesis; positron emission mammography; image reconstruction; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Margarida Mota

E-Mail Website
Guest Editor
1. Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, 1649-004 Lisbon, Portugal
2. Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1649-004 Lisbon, Portugal
Interests: medical imaging; breast imaging; medical image processing; artificial intelligence

Special Issue Information

Dear Colleagues,

Breast cancer remains the most common malignancy among women worldwide, underscoring the need for advanced imaging techniques to improve early detection, diagnosis, and treatment planning. The rapid development of artificial intelligence (AI), radiomics, and multimodal imaging has enhanced precision in tumor characterization, enabling more personalized therapeutic approaches. This Special Issue aims to highlight recent developments in breast cancer imaging, including innovative techniques, AI-driven approaches, and clinical applications that improve diagnostic accuracy and patient management. We invite original research and review articles covering novel imaging modalities, predictive modeling, deep learning applications, and the clinical translation of advanced imaging techniques for cancer diagnosis, disease monitoring, treatment response assessment, risk stratification, and personalized medicine. Contributions addressing challenges and future directions in integrating these technologies into clinical practice are highly encouraged.

Dr. Nuno Matela
Dr. Ana Margarida Mota
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 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

  • breast cancer imaging
  • artificial intelligence
  • radiomics
  • deep learning
  • personalized diagnosis
  • multimodal imaging
  • predictive modeling
  • early detection
  • treatment response
  • clinical applications

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Published Papers (1 paper)

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Review

26 pages, 1154 KB  
Review
AI-Based Characterization of Breast Cancer in Mammography and Tomosynthesis: A Review of Radiomics and Deep Learning for Subtyping, Staging, and Prognosis
by Ana M. Mota
Cancers 2025, 17(20), 3387; https://doi.org/10.3390/cancers17203387 - 21 Oct 2025
Abstract
Background: Biopsy remains the gold standard for characterizing breast cancer, but it is invasive, costly, and may not fully capture tumor heterogeneity. Advances in artificial intelligence (AI) now allow for the extraction of biological and clinical information from medical images, raising the [...] Read more.
Background: Biopsy remains the gold standard for characterizing breast cancer, but it is invasive, costly, and may not fully capture tumor heterogeneity. Advances in artificial intelligence (AI) now allow for the extraction of biological and clinical information from medical images, raising the possibility of using imaging as a non-invasive alternative. Methods: A semi-systematic review was conducted to identify AI-based approaches applied to mammography (MM) and breast tomosynthesis (BT) for tumor subtyping, staging, and prognosis. A PubMed search retrieved 1091 articles, of which 81 studies met inclusion criteria (63 MM, 18 BT). Studies were analyzed by clinical target, modality, AI pipeline, number of cases, dataset type, and performance metrics (AUC, accuracy, or C-index). Results: Most studies focused on tumor subtyping, particularly receptor status and molecular classification. Contrast-enhanced spectral mammography (CESM) was frequently used in radiomics pipelines, while end-to-end deep learning (DL) approaches were increasingly applied to MM. Deep models achieved strong performance for ER/PR and HER2 status prediction, especially in large datasets. Fewer studies addressed staging or prognosis, but promising results were obtained for axillary lymph node (ALN) metastasis and pathological complete response (pCR). Multimodal and longitudinal approaches—especially those combining MM or BT with MRI or ultrasound—show improved accuracy but remain rare. Public datasets were used in only a minority of studies, limiting reproducibility. Conclusions: AI models can predict key tumor characteristics directly from MM and BT, showing promise as non-invasive tools to complement or even replace biopsy. However, challenges remain in terms of generalizability, external validation, and clinical integration. Future work should prioritize standardized annotations, larger multicentric datasets, and integration of histological or transcriptomic validation to ensure robustness and real-world applicability. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging: From Detection to Personalized Diagnosis)
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