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Breast Cancer Imaging: Current Trends and Future Direction (2nd Edition)

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 457

Special Issue Editor


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Guest Editor
Nuclear Medicine Unit, Department of Medicine, Surgery and Pharmacy, University of Sassari, Sassari, Italy
Interests: nuclear medicine; image-based diagnostics; SPECT; SPECT/CT; PET/CT; molecular breast imaging; oncology (breast cancer, lung cancer, thyroid cancer, neuroendocrine tumors, and prostate cancer); radiomics; neurodegenerative disorders; radiometabolic therapy
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Special Issue Information

Dear Colleagues,  

This Special Issue is a continuation of our previous Special Issue, “Breast Cancer Imaging: Current Trends and Future Direction” (https://www.mdpi.com/journal/cancers/special_issues/BCICTFD).

Imaging plays a key role in the management of breast cancer patients, from screening and initial diagnosis to staging, response to therapy assessment, restaging, and recurrent disease detection.

At present, in addition to routine conventional morphological imaging techniques such as mammography, ultrasound, and computed tomography, more advanced imaging procedures are increasingly used.

These latter include MRI, molecular breast imaging, PET/CT, and the newest PET/MRI, which also provide functional information and quantitative parameters on tumor metabolism and biology, adding diagnostic and prognostic data.

More recently, potential benefits for breast cancer management seem to emerge from artificial intelligence (AI), machine learning (ML), and imaging-derived radiomics.

In this Special Issue, ”Breast Cancer Imaging: Current Trends and Future Direction (2nd Edition)”, we encourage researchers to submit original papers, review articles, brief communications, or comments on the current morphological and functional diagnostic imaging procedures adopted in the management of breast cancer patients. Papers that suggest novel approaches, such as AI, ML, and radiomics, are also welcome.

Prof. Dr. Angela Spanu
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

  • breast cancer
  • mammography
  • ultrasound
  • magnetic resonance imaging—MRI
  • molecular breast imaging
  • PET/CT
  • PET/MRI
  • artificial intelligence
  • machine learning
  • radiomics

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Published Papers (1 paper)

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Research

16 pages, 975 KiB  
Article
Preliminary Evaluation of Radiomics in Contrast-Enhanced Mammography for Prognostic Prediction of Breast Cancer
by Luca Nicosia, Luciano Mariano, Aurora Gaeta, Sara Raimondi, Filippo Pesapane, Giovanni Corso, Paolo De Marco, Daniela Origgi, Claudia Sangalli, Nadia Bianco, Serena Carriero, Sonia Santicchia and Enrico Cassano
Cancers 2025, 17(12), 1926; https://doi.org/10.3390/cancers17121926 - 10 Jun 2025
Viewed by 140
Abstract
Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in [...] Read more.
Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients. Methods: From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index. Results: The study included 126 BC patients with predominantly “mass”-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10–8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33–56.98]). Radiomic and clinical–radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (p < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical–radiomic model. Conclusions: Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone. Full article
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