Advances in Radionuclide Imaging

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3833

Special Issue Editors


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Guest Editor
1. Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
2. Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa
Interests: radionuclide imaging; inflammation and infection imaging; cardiovascular inflammation and infection; radionuclide imaging and therapy of thyroid disorders; HIV; tuberculosis; theranostics; prostate cancer

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Guest Editor
1. Department of Nuclear Medicine, University of Pretoria, Pretoria, South Africa
2. Nuclear Medicine Research Infrastructure, Steve Biko Academic Hospital, Pretoria, South Africa
Interests: radionuclide therapy; radiobiology; theranostics; targeted alpha therapy; radionuclide imaging; inflammation and infection imaging; tuberculosis; HIV; fungal infections; prostate cancer; neuroendocrine tumors; novel radiopharmaceutical design; machine learning
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Special Issue Information

Dear Colleagues,

Radionuclide imaging involves the application of radiopharmaceuticals for targeting cellular and subcellular molecular targets expressed in health and diseases. The capability to develop radiopharmaceuticals to examine molecular targets overexpressed in different disease states has contributed tremendously to the versatility of radionuclide imaging. Therefore, in the last decade, there have been tremendous advances in this field, leading to the development of novel radiopharmaceuticals, improvements in click chemistry, allowing for the simplified radiolabelling of many peptides and small molecules for radionuclide imaging within the nuclear medicine department, advances in the technological design of nuclear medicine cameras with consequent improvements in camera performance, improvements in lesion quantification and the broadening of the application of machine learning techniques in nuclear medicine image assessment, and the repurposing of existing radiopharmaceuticals for new indications. These advances have found useful applications, mostly in oncology but also in cardiology, neurology, and inflammation/infection imaging as well. These advances in radionuclide imaging have led to improvements in lesion detection, disease management, early and accurate treatment response assessment, prognostication, and in some instances, cost saving in management. The overall positive impact of radionuclide imaging on patient management has become a driving force in broadening its application in routine clinical practice.

This Special Issue therefore aims to provide a platform for original articles, review articles, opinion pieces, and case reports showcasing recent advances and improvements in current practice and the future direction in radionuclide imaging. We invite authors to submit articles on preclinical, translational, and clinical studies in the following areas: the design and application of novel radiopharmaceuticals, image quantification techniques, the application of machine learning in radionuclide imaging, improvements in lesion detection, new indications for old tracers, image-guided therapy, imaging for the prognostication of therapy outcome, novel insights on current applications of radionuclide imaging, future directions of the application of radionuclide imaging, and improvement in the practice of radionuclide imaging.

Dr. Ismaheel O. Lawal
Prof. Dr. Mike M. Sathekge
Guest Editors

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

  • PET/CT
  • PET/MR
  • SPECT
  • radionuclide imaging
  • diagnosis
  • prognosis of therapy
  • image-guided therapy
  • oncology
  • cardiology
  • neurology
  • inflammation and infection
  • machine learning
  • radiopharmaceuticals
  • recent advances
  • image quantification

Published Papers (2 papers)

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Research

11 pages, 3268 KiB  
Article
Magnetic Resonance Imaging Features of Rhino-Orbito-Cerebral Mucormycosis in Post-COVID-19 Patients: Radio-Pathological Correlation
by Rania Mostafa Hassan, Yassir Edrees Almalki, Mohammad Abd Alkhalik Basha, Mai Ahmed Gobran, Saad Misfer Alqahtani, Abdullah M. Assiri, Saeed Alqahtani, Sharifa Khalid Alduraibi, Mervat Aboualkheir, Ziyad A. Almushayti, Asim S. Aldhilan, Sameh Abdelaziz Aly and Asmaa A. Alshamy
Diagnostics 2023, 13(9), 1546; https://doi.org/10.3390/diagnostics13091546 - 25 Apr 2023
Cited by 1 | Viewed by 1477
Abstract
There has been a notable increase in rhino-orbito-cerebral mucormycosis (ROCM) post-coronavirus disease 2019 (COVID-19), which is an invasive fungal infection with a fatal outcome. Magnetic resonance imaging (MRI) is a valuable tool for early diagnosis of ROCM and assists in the proper management [...] Read more.
There has been a notable increase in rhino-orbito-cerebral mucormycosis (ROCM) post-coronavirus disease 2019 (COVID-19), which is an invasive fungal infection with a fatal outcome. Magnetic resonance imaging (MRI) is a valuable tool for early diagnosis of ROCM and assists in the proper management of these cases. This study aimed to describe the characteristic MRI findings of ROCM in post-COVID-19 patients to help in the early diagnosis and management of these patients. This retrospective descriptive study was conducted at a single hospital and included 52 patients with COVID-19 and a histopathologically proven ROCM infection who were referred for an MRI of the paranasal sinuses (PNS) due to sino-orbital manifestations. Two radiologists reviewed all the MR images in consensus. The diagnosis was confirmed by histopathological examination. The maxillary sinus was the most commonly affected PNS (96.2%). In most patients (57.7%), multiple sinuses were involved with the black turbinate sign on postcontrast images. Extrasinus was evident in 43 patients with orbital involvement. The pterygopalatine fossa was involved in four patients. Three patients had cavernous sinus extension, two had pachymeningeal enhancement, and one had epidural collection. The alveolar margin was affected in two patients, and five patients had an extension to the cheek. The awareness of radiologists by the characteristic MRI features of ROCM in post-COVID-19 patients helps in early detection, early proper management, and prevention of morbid complications. Full article
(This article belongs to the Special Issue Advances in Radionuclide Imaging)
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15 pages, 5031 KiB  
Article
The Impact of Adding Digital Breast Tomosynthesis to BI-RADS Categorization of Mammographically Equivocal Breast Lesions
by Rania Mostafa Hassan, Yassir Edrees Almalki, Mohammad Abd Alkhalik Basha, Sharifa Khalid Alduraibi, Mervat Aboualkheir, Ziyad A. Almushayti, Asim S. Aldhilan, Sameh Abdelaziz Aly and Asmaa A. Alshamy
Diagnostics 2023, 13(8), 1423; https://doi.org/10.3390/diagnostics13081423 - 15 Apr 2023
Cited by 1 | Viewed by 1836
Abstract
Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on [...] Read more.
Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization. Full article
(This article belongs to the Special Issue Advances in Radionuclide Imaging)
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