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Advances and Innovations in Radiology: From Contrast-Enhanced Mammography to Artificial Intelligence and Beyond

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 1607

Special Issue Editor

Dr. Luca Nicosia

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Guest Editor
1. Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Via Ripamonti, 435, 20141 Milan, Italy
2. Department of Biotechnology and Life Sciences, University of Insubria, Via J.H. Dunant, 3, 21100 Varese, Italy
Interests: breast imaging; contrast-enhanced mammography; breast MRI; radiomics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of radiology is undergoing a profound transformation spurred by the integration of artificial intelligence, advanced imaging techniques, and evolving clinical workflows. Specifically, contrast-enhanced mammography (CEM) has emerged as a promising tool for breast imaging, offering diagnostic performance comparable to that of MRI in certain scenarios. Meanwhile, artificial intelligence and radiomics are opening unprecedented opportunities for image analysis, prognostic modeling, and personalized medicine.

This Special Issue aims to collect high-quality contributions addressing the most recent advances and innovations in diagnostic and interventional radiology. We seek original research, reviews, and expert perspectives that highlight how these technologies are reshaping clinical practice and advancing the frontiers of medical imaging.

Topics of Interest We welcome submissions on (but not limited to):

  • Contrast-Enhanced Mammography: clinical applications, performance comparisons, technological innovations;
  • Artificial Intelligence and Machine Learning: diagnostic algorithms, workflow optimization, ethical challenges;
  • Radiomics and Imaging Biomarkers: prognostic models, validation studies, integration with clinical data;
  • Advanced Techniques in Breast Imaging;
  • Interventional Breast Radiology: new devices, techniques, and outcomes.

Dr. Luca Nicosia
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Clinical Medicine 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

  • contrast-enhanced mammography (CEM)
  • artificial intelligence
  • radiomics
  • diagnostic imaging innovations
  • inter-ventional radiology

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Published Papers (2 papers)

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Research

11 pages, 695 KB  
Article
Ground-Glass Enhancement on Contrast-Enhanced Mammography: A CT-Inspired Qualitative Descriptor for Breast Lesion Characterization
by Luca Nicosia, Luciano Mariano, Carmen Mallardi, Filippo Pesapane, Mauro Borella, Samuele Frassoni, Vincenzo Bagnardi, Chiara Barizza, Cristian Gialain, Chiara Trentin, Anna Carla Bozzini, Daniele Maiettini, Sonia Santicchia and Enrico Cassano
J. Clin. Med. 2026, 15(3), 999; https://doi.org/10.3390/jcm15030999 - 26 Jan 2026
Viewed by 358
Abstract
Background: This study introduces a new qualitative enhancement descriptor for contrast-enhanced mammography (CEM), termed Ground-Glass Enhancement (GGE). The objective was to categorize breast lesions using this descriptor and evaluate its association with malignancy and markers of tumor aggressiveness. Methods: In this single-center retrospective [...] Read more.
Background: This study introduces a new qualitative enhancement descriptor for contrast-enhanced mammography (CEM), termed Ground-Glass Enhancement (GGE). The objective was to categorize breast lesions using this descriptor and evaluate its association with malignancy and markers of tumor aggressiveness. Methods: In this single-center retrospective study, 249 patients with a single enhancing lesion on CEM were included. Lesions were classified into pure Ground-Glass Enhancement (PGGE), Heterogeneous Ground-Glass Enhancement (HGGE), or Opaque Enhancement (OE) based on the degree of obscuration of the underlying parenchyma. Clinical, imaging, and pathological features were compared across groups. Multivariable logistic regression was used to identify independent predictors of malignancy. Results: Significant differences across enhancement patterns were found in lesion conspicuity, enhancement type, size, background enhancement, and patient age. OE lesions more frequently showed high conspicuity (83% vs. 62% in HGGE and 20% in PGGE) and a mass-like appearance (94% vs. 73% in HGGE and 81% in PGGE). HGGE lesions had the largest median size (25 mm, vs. 17 mm in OE and 13 mm in PGGE), and OE lesions most often exhibited minimal background enhancement (77%, vs. 50% in HGGE). In multivariable analysis, mass-like enhancement (OR = 4.59), larger size (OR = 1.27 per +5 mm), and high conspicuity (OR = 3.43) were independently associated with malignancy. Although GGE categories correlated with malignancy in univariable analysis, this was not confirmed in the adjusted model. OE lesions were significantly associated with higher Ki-67 expression (73% with Ki-67 >20%), indicating increased proliferative activity compared with PGGE (43%) and HGGE (57%). Conclusions: The GGE descriptor captures clinically relevant imaging features and may support visual stratification of breast lesions on CEM. While not an independent predictor of malignancy, it appears more closely related to markers of tumor aggressiveness. Full article
(This article belongs to the Special Issue Advances and Innovations in Radiology: From Contrast-Enhanced Mammography to Artificial Intelligence and Beyond)
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22 pages, 1277 KB  
Article
Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers
by Arsenii Litvinov, Egor Ushakov, Sofia Senotrusova, Kirill Lukianov, Yury Markin, Liudmila Mikhailova and Evgeny Karpulevich
J. Clin. Med. 2026, 15(1), 365; https://doi.org/10.3390/jcm15010365 - 3 Jan 2026
Viewed by 952
Abstract
Background: BI-RADS (Breast Imaging Reporting and Data System) mammogram classification is central to early breast cancer detection. Despite being an ordinal scale that reflects increasing levels of malignancy suspicion, most models treat BI-RADS as a nominal task using cross-entropy loss, thereby disregarding the [...] Read more.
Background: BI-RADS (Breast Imaging Reporting and Data System) mammogram classification is central to early breast cancer detection. Despite being an ordinal scale that reflects increasing levels of malignancy suspicion, most models treat BI-RADS as a nominal task using cross-entropy loss, thereby disregarding the inherent class order. This mismatch between the clinical severity of misclassification and the model’s optimization objective remains underexplored. Methods: We systematically evaluate whether incorporating ordinal-aware loss functions improves BI-RADS classification performance under controlled, architecture-fixed conditions and dataset imbalance. Using a unified training pipeline across multiple datasets, we compare ordinal losses to standard cross-entropy, analyzing the effect of dataset- and label-level balancing. Area under the receiver operating characteristic curve (AUROC) and macro-F1 scores are reported as averages over five seeds. Results: Balanced sampling across datasets during training led to statistically significant improvements. Ordinal loss functions, such as Earth Mover Distance (EMD), consistently achieved higher performance across multiple metrics compared to conventional cross-entropy approaches commonly reported in the literature. Improvements were particularly evident in reducing severe misclassifications, demonstrating that aligning the learning objective with the ordinal structure of BI-RADS enhances robustness and clinical relevance. Conclusions: Aligning the learning objective with the ordinal BI-RADS structure substantially improves classification accuracy without changing the underlying architecture. These findings emphasize the importance of loss design, regularization, and data-balancing strategies in medical AI, supporting more reliable breast cancer screening. Full article
(This article belongs to the Special Issue Advances and Innovations in Radiology: From Contrast-Enhanced Mammography to Artificial Intelligence and Beyond)
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