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Recent Advancements in Nuclear Medicine and Radiology
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Recent Advancements in Nuclear Medicine and Radiology

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: 1 July 2025 | Viewed by 6395

Special Issue Editor

Prof. Dr. Adriaan A. Lammertsma

E-Mail Website
Guest Editor
Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
Interests: PET; PET physics; PET data analysis; tracer kinetic modeling

Special Issue Information

Dear Colleagues,

In 1895, Wilhelm Conrad Röntgen discovered X-rays; a year later, Henri Becquerel described “mysterious” rays, later termed as radioactivity, originating from uranium. Both types of radiation rapidly found their way in medicine, stimulated of course by Röntgen's first "medical" X-ray image of his wife's hand (with ring), taken on 22 December 1895. Now, more than 125 years later, radiology is firmly embedded in the diagnostic pathway of nearly all diseases. To a lesser extent, the same is true for nuclear medicine. In addition, imaging of molecular interactions plays an increasingly important role in unravelling disease mechanisms. Over the years, there has been substantial progress in imaging equipment, resulting in state-of-the-art CT, MRI, SPECT and PET scanners. Such progress is still ongoing, with large axial field of view (total body) PET scanners being the latest major development. In parallel with these developments in imaging instrumentation, image analysis techniques have similarly evolved with artificial intelligence. Better scanners, more refined analytical techniques and a wider range of radiopharmaceuticals have resulted in an ever increasing number of clinical applications.

This Special Issue, ‘Recent Advancements in Nuclear Medicine and Radiology’ aims to present an exclusive collection of comprehensive reviews and invites researchers to submit their review papers covering the novel developments and advancements in nuclear medicine and radiology.

Prof. Dr. Adriaan A. Lammertsma
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. 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

  • PET
  • SPECT
  • CT
  • MRI
  • AI
  • image analysis
  • kinetic analysis

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

Published Papers (5 papers)

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Research

Jump to: Review

18 pages, 761 KiB  
Article
Neuroinflammation at the Neuroforamina and Spinal Cord in Patients with Painful Cervical Radiculopathy and Pain-Free Participants: An [11C]DPA713 PET/CT Proof-of-Concept Study
by Ivo J. Lutke Schipholt, Meghan A. Koop, Michel W. Coppieters, Elsmarieke M. van de Giessen, Adriaan A. Lammerstma, Bastiaan C. ter Meulen, Carmen Vleggeert-Lankamp, Bart N.M. van Berckel, Joost Bot, Hans van Helvoirt, Paul R. Depauw, Ronald Boellaard, Maqsood Yaqub and Gwendolyne Scholten-Peeters
J. Clin. Med. 2025, 14(7), 2420; https://doi.org/10.3390/jcm14072420 - 2 Apr 2025
Viewed by 550
Abstract
Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [11C]DPA713, which [...] Read more.
Background/Objectives: The complex pathophysiology of painful cervical radiculopathy is only partially understood. Neuroimmune activation in the dorsal root ganglion and spinal cord is assumed to underlie the genesis of radicular pain. Molecular positron emission tomography (PET) using the radiotracer [11C]DPA713, which targets the 18-kDa translocator protein (TSPO), offers the ability to quantify neuroinflammation in humans in vivo. The primary objectives of this study were to (1) assess whether uptake of [11C]DPA713, a metric of neuroinflammation, is higher in the neuroforamina and spinal cord of patients with painful cervical radiculopathy compared with that in pain-free participants and (2) assess whether [11C]DPA713 uptake is associated with clinical parameters, such as pain intensity. Methods: Dynamic 60 min [11C]DPA713 PET/CT scans were acquired, and regions of interest were defined for neuroforamina and spinal cord. Resulting time-activity curves were fitted to a single-tissue compartment model using an image-derived input function, corrected for plasma-to-whole blood ratios and parent fractions, to obtain the volume of distribution (VT) as the primary outcome measure. Secondary neuroinflammation metrics included 1T2k VT without metabolite correction (1T2k_WB) and Logan VT. Results: The results indicated elevated levels of 1T2k VT at the neuroforamina (p < 0.04) but not at the spinal cord (p = 0.16). Neuroforamina and spinal cord 1T2k VT lack associations with clinical parameters. Secondary neuroinflammatory metrics show associations with clinical parameters such as the likelihood of neuropathic pain. Conclusions: These findings enhance our understanding of painful cervical radiculopathy’s pathophysiology, emphasizing the neuroforamina levels of neuroinflammation as a potential therapeutic target. Full article
(This article belongs to the Special Issue Recent Advancements in Nuclear Medicine and Radiology)
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18 pages, 1772 KiB  
Article
Assessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma
by Anouk van der Hoorn, Lesley E. Manusiwa, Hiske L. van der Weide, Peter F. Sinnige, Rients B. Huitema, Charlotte L. Brouwer, Justyna Klos, Ronald J. H. Borra, Rudi A. J. O. Dierckx, Sandra E. Rakers, Anne M. Buunk, Joke M. Spikman, Remco J. Renken, Ingeborg Bosma, Roelien H. Enting, Miranda C. A. Kramer and Chris W. J. van der Weijden
J. Clin. Med. 2025, 14(2), 551; https://doi.org/10.3390/jcm14020551 - 16 Jan 2025
Viewed by 906
Abstract
Background: Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they [...] Read more.
Background: Diffusion weighted imaging (DWI) is used for monitoring purposes for lower-grade glioma (LGG). While the apparent diffusion coefficient (ADC) is clinically used, various DWI models have been developed to better understand the micro-environment. However, the validity of these models and how they relate to each other is currently unknown. Therefore, this study assesses the validity and agreement of these models. Methods: Fourteen post-treatment LGG patients and six healthy controls (HC) underwent DWI MRI on a 3T MRI scanner. DWI processing included diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), white matter tract integrity (WMTI), neurite orientation dispersion and density imaging (NODDI), and fixel-based analysis (FBA). Validity was assessed by delineating surgical cavity, peri-surgical cavity, and normal-appearing white matter (NAWM) in LGG patients, and white matter (WM) in HC. Spearman correlation assessed the agreement between DWI parameters. Results: All obtained parameters differed significantly across tissue types. Remarkably, WMTI showed that intra-axonal diffusivity was high in the surgical cavity and low in NAWM and WM. Most DWI parameters correlated well with each other, except for WMTI-derived intra-axonal diffusivity. Conclusion: This study shows that all parameters relevant for tumour monitoring and DWI-derived parameters for axonal fibre-bundle integrity (except WMTI-IAS-Da) could be used interchangeably, enhancing inter-DWI model interpretability. Full article
(This article belongs to the Special Issue Recent Advancements in Nuclear Medicine and Radiology)
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10 pages, 1492 KiB  
Article
Unsupervised Pattern Analysis to Differentiate Multiple Sclerosis Phenotypes Using Principal Component Analysis on Various MRI Sequences
by Chris W. J. van der Weijden, Milena S. Pitombeira, Débora E. Peretti, Kenia R. Campanholo, Guilherme D. Kolinger, Carolina M. Rimkus, Carlos Alberto Buchpiguel, Rudi A. J. O. Dierckx, Remco J. Renken, Jan F. Meilof, Erik F. J. de Vries and Daniele de Paula Faria
J. Clin. Med. 2024, 13(17), 5234; https://doi.org/10.3390/jcm13175234 - 4 Sep 2024
Viewed by 1256
Abstract
Background: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective: This study explores the use of scaled subprofile modelling using principal [...] Read more.
Background: Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective: This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes. Methods: MRI scans were performed on patients with RRMS (n = 30) and patients with PMS (n = 20), using the standard sequences T1w, T2w, T2w-FLAIR, and the myelin-sensitive sequences magnetisation transfer (MT) ratio (MTR), quantitative MT (qMT), inhomogeneous MT ratio (ihMTR), and quantitative inhomogeneous MT (qihMT). Results: SSM/PCA analysis of qihMT images best differentiated PMS from RRMS, with the highest specificity (87%) and positive predictive value (PPV) (83%), but a lower sensitivity (67%) and negative predictive value (NPV) (72%). Conversely, T1w data analysis showed the highest sensitivity (93%) and NPV (89%), with a lower PPV (67%) and specificity (53%). Phenotype classification agreement between T1w and qihMT was observed in 57% of patients. In the subset with concordant classifications, the sensitivity, specificity, PPV, and NPV were 100%, 88%, 90%, and 100%, respectively. Conclusions: SSM/PCA on MRI data revealed distinctive patterns for MS phenotypes. Optimal discrimination occurred with qihMT and T1w sequences, with qihMT identifying PMS and T1w identifying RRMS. When qihMT and T1w analyses align, MS phenotype prediction improves. Full article
(This article belongs to the Special Issue Recent Advancements in Nuclear Medicine and Radiology)
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16 pages, 3598 KiB  
Article
Bone Metabolism and Dental Implant Insertion as a Correlation Affecting on Marginal Bone Remodeling: Texture Analysis and the New Corticalization Index, Predictor of Marginal Bone Loss—3 Months of Follow-Up
by Tomasz Wach, Piotr Szymor, Grzegorz Trybek, Maciej Sikora, Adam Michcik and Marcin Kozakiewicz
J. Clin. Med. 2024, 13(11), 3212; https://doi.org/10.3390/jcm13113212 - 30 May 2024
Cited by 2 | Viewed by 1439
Abstract
Background/Objectives: The general condition of implantology patients is crucial when considering the long- and short-term survival of dental implants. The aim of the research was to evaluate the correlation between the new corticalization index (CI) and patients’ condition, and its impact on marginal [...] Read more.
Background/Objectives: The general condition of implantology patients is crucial when considering the long- and short-term survival of dental implants. The aim of the research was to evaluate the correlation between the new corticalization index (CI) and patients’ condition, and its impact on marginal bone loss (MBL) leading to implant failure, using only radiographic (RTG) images on a pixel level. Method: Bone near the dental implant neck was examined, and texture features were analyzed. Statistical analysis includes analysis of simple regression where the correlation coefficient (CC) and R2 were calculated. Detected relationships were assumed to be statistically significant when p < 0.05. Statgraphics Centurion version 18.1.12 (Stat Point Technologies, Warrenton, VA, USA) was used to conduct the statistical analyses. Results: The research revealed a correlation between MBL after 3 months and BMI, PTH, TSH, Ca2+ level in blood serum, phosphates in blood serum, and vitamin D. A correlation was also observed between CI and PTH, Ca2+ level in blood serum, vitamin D, LDL, HDL, and triglycerides on the day of surgery. After 3 months of the observation period, CI was correlated with PTH, TSH, Ca2+ level in blood serum, and triglycerides. Conclusion: The results of the research confirm that the general condition of patients corresponds with CI and MBL. A patient’s general condition has an impact on bone metabolism around dental implants. Implant insertion should be considered if the general condition of the patient is not stable. However, CI has not yet been fully investigated. Further studies are necessary to check and categorize the impact of corticalization on marginal bone loss near dental implants. Full article
(This article belongs to the Special Issue Recent Advancements in Nuclear Medicine and Radiology)
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Review

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41 pages, 2390 KiB  
Review
Revolutionizing Radiology with Natural Language Processing and Chatbot Technologies: A Narrative Umbrella Review on Current Trends and Future Directions
by Andrea Lastrucci, Yannick Wandael, Angelo Barra, Renzo Ricci, Antonia Pirrera, Graziano Lepri, Rosario Alfio Gulino, Vittorio Miele and Daniele Giansanti
J. Clin. Med. 2024, 13(23), 7337; https://doi.org/10.3390/jcm13237337 - 2 Dec 2024
Cited by 1 | Viewed by 1283
Abstract
The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early [...] Read more.
The application of chatbots and NLP in radiology is an emerging field, currently characterized by a growing body of research. An umbrella review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This review explores the early developments and potential future impact of these technologies in radiology. The current literature, comprising 15 systematic reviews, highlights potentialities, opportunities, areas needing improvements, and recommendations. This umbrella review offers a comprehensive overview of the current landscape of natural language processing (NLP) and natural language models (NLMs), including chatbots, in healthcare. These technologies show potential for improving clinical decision-making, patient engagement, and communication across various medical fields. However, significant challenges remain, particularly the lack of standardized protocols, which raises concerns about the reliability and consistency of these tools in different clinical contexts. Without uniform guidelines, variability in outcomes may hinder the broader adoption of NLP/NLM technologies by healthcare providers. Moreover, the limited research on how these technologies intersect with medical devices (MDs) is a notable gap in the literature. Future research must address these challenges to fully realize the potential of NLP/NLM applications in healthcare. Key future research directions include the development of standardized protocols to ensure the consistent and safe deployment of NLP/NLM tools, particularly in high-stake areas like radiology. Investigating the integration of these technologies with MD workflows will be crucial to enhance clinical decision-making and patient care. Ethical concerns, such as data privacy, informed consent, and algorithmic bias, must also be explored to ensure responsible use in clinical settings. Longitudinal studies are needed to evaluate the long-term impact of these technologies on patient outcomes, while interdisciplinary collaboration between healthcare professionals, data scientists, and ethicists is essential for driving innovation in an ethically sound manner. Addressing these areas will advance the application of NLP/NLM technologies and improve patient care in this emerging field. Full article
(This article belongs to the Special Issue Recent Advancements in Nuclear Medicine and Radiology)
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