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Diagnostics
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Innovations in Medical Imaging for Precision Diagnostics
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Innovations in Medical Imaging for Precision Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1506

Special Issue Editor

Prof. Dr. Tim Duong

E-Mail Website
Guest Editor
Department of Radiology, Montefiore Health System and Albert Einstein College of Medicine, Bronx, NY 10461, USA
Interests: COVID-19; machine learning; biomedical imaging; radiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue of Diagnostics dedicated to exploring the latest advancements in magnetic resonance imaging (MRI), computed tomography (CT), mammography, and positron emission tomography (PET)—technologies that continue to reshape the landscape of modern medicine through early and precise disease detection and characterization.

With the accelerating integration of artificial intelligence (AI), radiomics, machine learning, and hybrid imaging techniques, medical imaging has become more than a visualization tool—it now offers rich quantitative data that support predictive modeling, personalized treatment planning, and real-time decision-making.

This Special Issue aims to highlight cutting-edge developments that advance the diagnostic potential of imaging technologies across a wide array of clinical applications. We welcome high-quality submissions in the following areas:

Topics of interest include (but are not limited to) the following:

  • AI-powered diagnostic tools for image interpretation, lesion detection, and prognosis;
  • Radiomics analysis for risk stratification and disease characterization;
  • Hybrid imaging systems (e.g., PET/MRI, PET, and CT) for multi-modality diagnostics;
  • Advanced contrast agents and techniques for enhanced tissue differentiation;
  • Imaging biomarkers for early detection and therapy monitoring;
  • Computational imaging and reconstruction algorithms that improve resolution, speed, accuracy, specificity, sensitivity, or dose efficiency;
  • Integration of imaging with clinical decision support systems and electronic health records (EHR).

Prof. Dr. Tim Duong
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 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. Diagnostics 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

  • deep learning
  • multimodal imaging
  • precision medicine
  • single-photon emission computed tomography (SPECT)
  • mammography
  • tomosynthesis
  • ultrasound
  • neurology
  • oncology
  • medicine
  • psychiatry

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

Published Papers (3 papers)

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Research

10 pages, 1132 KB  
Article
Photon-Counting Computed Tomography of the Paranasal Sinuses Improves Intraoperative Accuracy of Image-Guided Surgery
by Benjamin Philipp Ernst, Iris Burck, Stefanie Schliwa, Sven Becker, Tobias Albrecht, Thomas J. Vogl, Jan-Erik Scholtz, Anna Levi, Andreas German Loth, Friederike Bärhold, Sebastian Strieth, Matthias F. Froelich, Alexander Hertel, Yannik Christian Layer, Daniel Kuetting and Jonas Eckrich
Diagnostics 2025, 15(21), 2777; https://doi.org/10.3390/diagnostics15212777 - 31 Oct 2025
Viewed by 443
Abstract
Background: Computed tomography (CT)-based image-guided surgery (IGS) is of great importance in functional endoscopic sinus surgery (FESS) and requires IGS-specific imaging protocols to ensure high intraoperative accuracy. This study aimed to compare photon-counting CT (PCCT), dual-energy dual-source CT (DECT), and spectral detector CT [...] Read more.
Background: Computed tomography (CT)-based image-guided surgery (IGS) is of great importance in functional endoscopic sinus surgery (FESS) and requires IGS-specific imaging protocols to ensure high intraoperative accuracy. This study aimed to compare photon-counting CT (PCCT), dual-energy dual-source CT (DECT), and spectral detector CT (SDCT) of the paranasal sinuses with respect to image quality, IGS accuracy and radiation dose. Methods: A formalin-fixed cadaver skull was examined using PCCT, DECT and SDCT at 100 kV tube voltage with descending tube currents (mAs). The setup of electromagnetic IGS was evaluated using a visual analog scale. Accuracy was analyzed endoscopically using defined anatomical landmarks. Diagnostic image quality as well as bone and soft tissue noise were assessed qualitatively using a 5-point Likert scale and quantitatively by determination of signal-to-noise ratio. Radiation dose was evaluated using the dose length product. Results: While PCCT datasets could be registered and navigated accurately down to 10 mAs (1.5 mm error at 10 mAs), both DECT and SDCT exhibited significantly increased inaccuracies below 40 mAs (4.35/5.15 mm for DECT/SDCT at 25 mAs). Using PCCT therefore enabled a 45% radiation dose reduction at the minimally required dose length product using PCCT. Quantitative and qualitative image quality were superior for PCCT compared to DECT and SDCT. Conclusions: PCCT provides excellent accuracy of anatomical landmarks in IGS with superior image quality of the paranasal sinuses in low-mA scans and substantially reduced radiation exposure. Full article
(This article belongs to the Special Issue Innovations in Medical Imaging for Precision Diagnostics)
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18 pages, 4726 KB  
Article
Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio
by Jiangqin Ma, Xiling Gu, Zhonglin Zhang, Jun Chen, Yunfan Liu, Yang Qiu, Guangyong Ai, Xuxiang Jia, Zhenghao Li, Bo Xiang and Xiaojing He
Diagnostics 2025, 15(21), 2722; https://doi.org/10.3390/diagnostics15212722 - 27 Oct 2025
Viewed by 300
Abstract
Objectives: This study aimed to develop and validate a biparametric MRI (bpMRI)-based radiomics model for the noninvasive prediction of tumor–stroma ratio (TSR) in prostate cancer (PCa). Additionally, we sought to explore lesion distribution patterns in the peripheral zone (PZ) and transition zone (TZ) [...] Read more.
Objectives: This study aimed to develop and validate a biparametric MRI (bpMRI)-based radiomics model for the noninvasive prediction of tumor–stroma ratio (TSR) in prostate cancer (PCa). Additionally, we sought to explore lesion distribution patterns in the peripheral zone (PZ) and transition zone (TZ) to provide deeper insights into the biological behavior of PCa. Methods: This multicenter retrospective study included 223 pathologically confirmed PCa patients, with 146 for training and 39 for internal validation at Center 1, and 38 for external testing at Center 2. All patients underwent preoperative bpMRI (T2WI, DWI acquired with a b-value of 1400 s/mm2, and ADC maps), with TSR histopathologically quantified. Regions of interest (ROIs) were manually segmented on bpMRI images using ITK-SNAP software (version 4.0.1), followed by high-throughput radiomic feature extraction. Redundant features were eliminated via Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression. Five machine learning (ML) classifiers—Logistic Regression (LR), Support Vector Machine (SVM), BernoulliNBBayes, Ridge, and Stochastic Gradient Descent (SGD)—were trained and optimized. Model performance was rigorously evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The Ridge demonstrated superior diagnostic performance, achieving AUCs of 0.846, 0.789, and 0.745 in the training, validation, and test cohorts, respectively. Lesion distribution analysis revealed no significant differences between High-TSR and Low-TSR groups (p = 0.867), suggesting that TSR may not be strongly associated with zonal localization. Conclusions: This exploratory study suggests that a bpMRI-based radiomic model holds promise for noninvasive TSR estimation in prostate cancer and may provide complementary insights into tumor aggressiveness beyond conventional pathology. Full article
(This article belongs to the Special Issue Innovations in Medical Imaging for Precision Diagnostics)
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22 pages, 5380 KB  
Article
Lesion Stiffness Measured by Magnetic Resonance Elastography: A Novel Biomarker for Differentiating Benign, Premalignant and Malignant Prostate Lesions
by Süheyl Poçan and Levent Karakaş
Diagnostics 2025, 15(20), 2603; https://doi.org/10.3390/diagnostics15202603 - 16 Oct 2025
Viewed by 417
Abstract
Background/Objectives: This study aimed to assess whether magnetic resonance elastography (MRE)-derived stiffness measurements of the central gland, entire gland, and lesions of the prostate differ among benign, premalignant, and malignant lesions and to evaluate their diagnostic performance in distinguishing these groups. Methods [...] Read more.
Background/Objectives: This study aimed to assess whether magnetic resonance elastography (MRE)-derived stiffness measurements of the central gland, entire gland, and lesions of the prostate differ among benign, premalignant, and malignant lesions and to evaluate their diagnostic performance in distinguishing these groups. Methods: This prospective study enrolled 113 men (mean age, 62.7 ± 7.2 years). Patients were categorized into benign (n = 75), premalignant (n = 15; atypical small acinar proliferation and high-grade prostatic intraepithelial neoplasia), and malignant (n = 23; adenocarcinoma) lesion groups based on histopathological findings. MRE-derived stiffness was measured at the lesion, central gland, and entire gland levels. Other evaluated parameters included diffusion restriction, contrast retention, prostate-specific antigen (PSA) levels, prostate volume, and Prostate Imaging Reporting and Data System (PI-RADS) score. Results: Mean central gland stiffness did not differ between benign and premalignant lesions, but was markedly higher in the malignant group (Benign: 3.3 ± 0.2 vs. Premalignant: 3.4 ± 0.2 vs. Malignant: 3.6 ± 0.3 kPa; p < 0.001). A similar pattern was observed for entire gland stiffness (Benign: 3.3 ± 0.4 vs. Premalignant: 3.3 ± 0.4 vs. Malignant: 4.1 ± 0.6 kPa; p < 0.001). Median lesion stiffness increased stepwise from benign to premalignant to malignant lesions (Benign: 3.6 vs. Premalignant: 5.8 vs. Malignant: 7.7 kPa; p < 0.001). Central and entire gland stiffness distinguished malignant lesions but failed to differentiate premalignant lesions from benign lesions. Lesion stiffness demonstrated superior diagnostic accuracy in distinguishing premalignant from benign (AUC 0.82; accuracy 83.3%) and malignant lesions from premalignant lesions (AUC 0.86; accuracy 82.5%) compared to central and entire gland stiffness. Conclusions: MRE-derived lesion stiffness is a promising diagnostic biomarker, effectively distinguishing benign, premalignant, and malignant prostate lesions. Prostate gland stiffness measured by MRE, especially lesion-specific measurements, may be considered as an additional candidate procedure that can be accommodated in multiparametric magnetic resonance imaging. Full article
(This article belongs to the Special Issue Innovations in Medical Imaging for Precision Diagnostics)
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