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Biomedicines
Special Issues
Recent Advances in Precision Biomedical Imaging
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Recent Advances in Precision Biomedical Imaging

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 493

Editor

Dr. Arosh Perera

E-Mail
Guest Editor
Faculty of Medicine & Surgery, University of Milan, Via Festa del Perdono, 7, 20122 Milano, Lombardy, Italy
Interests: radiomics and deep learning; AI; UHF MRI; photon-counting CT; microwave breast imaging

Special Issue Information

Dear Colleagues,

Biomedical imaging is undergoing a major transformation, driven by rapid progress in imaging hardware, computational methods, and artificial intelligence (AI). This Special Issue of Biomedicines focuses on recent advances that improve the detection, characterization, and monitoring of disease across a broad range of clinical and translational applications. Topics of interest include innovations in high-resolution and functional imaging modalities such as MRI, CT, ultrasound, PET/SPECT, and optical imaging, as well as emerging technologies including photon-counting CT, quantitative MRI, advanced diffusion and perfusion techniques, and hybrid imaging platforms (e.g., PET/MRI).

Key emphasis is placed on AI-enabled imaging, including accelerated acquisition, advanced reconstruction, denoising, segmentation, radiomics, and predictive modeling, particularly when combined with clinical, molecular, or multi-omics data to support precision medicine. The Special Issue also welcomes research on image-guided interventions, intraoperative and real-time imaging, workflow optimization, and validated imaging biomarkers that can improve clinical decision-making. Importantly, contributions addressing clinical translation—such as interpretability, fairness, reproducibility, external validation, and regulatory readiness—are strongly encouraged.

Overall, this Special Issue aims to highlight imaging innovations that enable more quantitative, efficient, and patient-centered healthcare.

Dr. Arosh Perera
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. Biomedicines 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 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

  • artificial intelligence (AI) in imaging
  • quantitative imaging biomarkers
  • photon-counting CT
  • advanced MRI (diffusion, perfusion, functional MRI)
  • hybrid imaging (PET/CT, PET/MRI)
  • radiomics and deep learning
  • low-dose and accelerated acquisition
  • image reconstruction and denoising
  • image-guided interventions
  • precision and personalized medicine

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

Published Papers (1 paper)

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Research

23 pages, 14863 KB  
Article
CT-Derived Radiomic Features for the Non-Invasive Differentiation of Mediastinal Lymphadenopathy in Lung Cancer and Sarcoidosis
by Demet Doğan, Coşku Öksüz, Özgür Çakır, Zuhal Güllü and Oğuzhan Urhan
Biomedicines 2026, 14(6), 1327; https://doi.org/10.3390/biomedicines14061327 - 11 Jun 2026
Viewed by 214
Abstract
Background/Objectives: Differentiating mediastinal lymphadenopathy associated with lung cancer from sarcoidosis remains a clinical challenge because of overlapping imaging findings. This study evaluated whether CT-derived radiomic features, alone and in combination with clinical variables, could support the non-invasive differentiation of these two entities. Methods: [...] Read more.
Background/Objectives: Differentiating mediastinal lymphadenopathy associated with lung cancer from sarcoidosis remains a clinical challenge because of overlapping imaging findings. This study evaluated whether CT-derived radiomic features, alone and in combination with clinical variables, could support the non-invasive differentiation of these two entities. Methods: In this retrospective single-center study, 204 histopathologically confirmed mediastinal lymph nodes were analyzed. A total of 107 radiomic features were extracted from manually segmented contrast-enhanced thoracic CT images. Multiple feature selection methods, dimensionality reduction techniques, and machine learning classifiers were evaluated using patient-level five-fold cross-validation. Additional clinical-only, combined clinical–radiomic, one-node-per-patient sensitivity, and exploratory interobserver feature stability analyses were performed. Results: Among radiomics-only models, LASSO achieved the highest ROC–AUC of 0.9108, whereas ElasticNet achieved the highest accuracy of 81.20%. The clinical-only ensemble model using age, sex, and smoking status showed strong performance, with an accuracy of 94.92% and an ROC–AUC of 0.9733. Selected combined clinical–radiomic models showed numerically higher performance; the combined correlation-filtered ensemble model achieved the highest accuracy of 97.78% and an ROC–AUC of 1.0000. Clinical integration also yielded more compact feature subsets in some methods, as combined LASSO selected 9.6 variables while improving ROC–AUC from 0.9108 to 0.9667 compared with radiomics-only LASSO. In the one-node-per-patient sensitivity analysis, clinical-only and combined models retained high performance, whereas radiomics-only LASSO showed reduced performance. Exploratory interobserver analysis showed moderate reproducibility for only a subset of radiomic features. Conclusions: CT-derived radiomic features may provide complementary information for differentiating mediastinal lymphadenopathy associated with lung cancer from that associated with sarcoidosis. However, clinical variables were highly informative, and the incremental value of radiomics should be interpreted cautiously. Further multicenter studies with external validation, standardized segmentation protocols, and clinically balanced cohorts are required before routine clinical implementation can be recommended. Full article
(This article belongs to the Special Issue Recent Advances in Precision Biomedical Imaging)
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