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Diagnostics
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PET/CT Diagnostics and Theranostics
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PET/CT Diagnostics and Theranostics

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 2521

Special Issue Editor

Prof. Dr. Salvatore Annunziata

E-Mail Website
Guest Editor
Unità di Medicina Nucleare, TracerGLab, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico A. Gemelli IRCCS, 00168 Roma, Italy
Interests: PET; radiomics; AI; lymphoma; radiopharmaceuticals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue of "PET/CT Diagnostics and Theranostics" delves into the cutting-edge advancements in the field of combined positron emission tomography (PET) and computed tomography (CT) imaging. It explores the latest research in diagnostic imaging, highlighting the unique capabilities of PET/CT in detecting and staging diseases. Additionally, it discusses the emerging role of PET/CT in theranostics, a field that combines diagnosis and targeted therapy. This Special Issue aims to provide a comprehensive overview of the latest developments in PET/CT technology and its clinical applications.

Prof. Dr. Salvatore Annunziata
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

  • PET/CT
  • diagnostics
  • theranostics
  • medical imaging
  • development

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Published Papers (3 papers)

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Research

19 pages, 12844 KiB  
Article
Inter-Software Reproducibility of Quantitative Values of Myocardial Blood Flow and Coronary Flow Reserve Acquired by [13N]NH3 MPI PET/CT and the Effect of Motion Correction Tools
by Oscar Isaac Mendoza-Ibañez, Riemer H. J. A. Slart, Erick Alexanderson-Rosas, Tonantzin Samara Martinez-Lucio, Friso M. van der Zant, Remco J. J. Knol and Sergiy V. Lazarenko
Diagnostics 2025, 15(5), 613; https://doi.org/10.3390/diagnostics15050613 - 4 Mar 2025
Viewed by 488
Abstract
Background: The choice of software package (SP) for image processing affects the reproducibility of myocardial blood flow (MBF) values in [13N]NH3 PET/CT scans. However, the impact of motion correction (MC) tools—integrated software motion correction (ISMC) or data-driven motion correction (DDMC)—on [...] Read more.
Background: The choice of software package (SP) for image processing affects the reproducibility of myocardial blood flow (MBF) values in [13N]NH3 PET/CT scans. However, the impact of motion correction (MC) tools—integrated software motion correction (ISMC) or data-driven motion correction (DDMC)—on the inter-software reproducibility of MBF has not been studied. This research aims to evaluate reproducibility among three commonly used SPs and the role of MC. Methods: Thirty-six PET/CT studies from patients without myocardial ischemia or infarction were processed using QPET, Corridor-4DM (4DM), and syngo.MBF (syngo). MBF and coronary flow reserve (CFR) values were obtained without motion correction (NMC) and with ISMC and DDMC. Intraclass correlation coefficients (ICC) and Bland-Altman (BA) plots were used to analyze agreement. Results: Good or excellent reproducibility (ICC ≥ 0.77) was found for rest-MBF values, regardless of the SPs or use of MC. In contrast, stress-MBF and CFR values presented mostly a moderate agreement when NMC was used. The RCA territory consistently had the lowest agreement in stress-MBF and CFR in the comparisons involving QPET. The use of MC, particularly DDMC, enhanced the reproducibility of most of the stress-MBF and CFR values by improving ICCs and reducing bias and limits of agreement (LoA) in BA analysis. Conclusions: MBF quantification agreement between SPs is strong for rest-MBF values but suboptimal for stress-MBF and CFR values. MC tools, especially DDMC, are recommended for improving reproducibility in stress-MBF assessments, although differences in SP reproducibility up to 0.77 mL/g/min in global stress-MBF and up to 0.88 in global CFR remain despite the use of MC. Full article
(This article belongs to the Special Issue PET/CT Diagnostics and Theranostics)
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16 pages, 2296 KiB  
Article
The Predictive Role of Baseline 18F-FDG PET/CT Radiomics in Follicular Lymphoma on Watchful Waiting: A Preliminary Study
by Daria Maccora, Michele Guerreri, Rosalia Malafronte, Francesco D’Alò, Stefan Hohaus, Marco De Summa, Vittoria Rufini, Roberto Gatta, Luca Boldrini, Lucia Leccisotti and Salvatore Annunziata
Diagnostics 2025, 15(4), 432; https://doi.org/10.3390/diagnostics15040432 - 11 Feb 2025
Viewed by 533
Abstract
Background: Patients with low tumour burden follicular lymphoma (FL) are managed with an initial watchful waiting (WW) approach. The way to better predict the time-to-treatment (TTT) is still under investigation for its possible clinical impact. This study explored whether radiomic features extracted [...] Read more.
Background: Patients with low tumour burden follicular lymphoma (FL) are managed with an initial watchful waiting (WW) approach. The way to better predict the time-to-treatment (TTT) is still under investigation for its possible clinical impact. This study explored whether radiomic features extracted from baseline 18F-FDG PET/CT could predict TTT in FL patients on WW. Methods: Thirty-eight patients on initial WW (grade 1–3a) were retrospectively included from 2010 to 2019. Eighty-one PET/CT morphological and first-level intensity radiomic features were extracted from the total metabolic tumour burden (TMTV), the lesion having the highest SUVmax and a reference volume-of-interest placed on the healthy liver. Models using linear regression (LR) and support vector machine (SVM) were constructed to assess the feasibility of using radiomic features to predict TTT. A leave-one-out cross-validation approach was used to assess the performance. Results: For LR models, we found a root-mean-squared error of 29.4, 28.6, 26.4 and 26.8 and an R2 of 0.03, 0.08, 0.21 and 0.20, respectively, incrementing the features from one to four. Accordingly, the best model included three features: the liver minimum SUV value, the liver SUV skewness and the sum of squared SUV values in the TMTV. For SVM models, accuracies of 0.79, 0.63, 0.76 and 0.68 and areas under the curve of 0.80, 0.72, 0.77 and 0.63 were found, respectively, incrementing the features from one to four. The best performing model used one feature, namely the median value of the lesion containing the SUVmax value. Conclusions: The baseline PET/CT radiomic approach has the potential to predict TTT in FL patients on WW. Integrating radiomics with clinical parameters could further aid in patient stratification. Full article
(This article belongs to the Special Issue PET/CT Diagnostics and Theranostics)
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11 pages, 2352 KiB  
Article
Feasibility of Using 18F-FDG PET/CT Radiomics and Machine Learning to Detect Drug-Induced Interstitial Lung Disease
by Charlotte L. C. Smith, Gerben J. C. Zwezerijnen, Sanne E. Wiegers, Yvonne W. S. Jauw, Pieternella J. Lugtenburg, Josée M. Zijlstra, Maqsood Yaqub and Ronald Boellaard
Diagnostics 2024, 14(22), 2531; https://doi.org/10.3390/diagnostics14222531 - 12 Nov 2024
Cited by 1 | Viewed by 1064
Abstract
Background: Bleomycin is an oncolytic and antibiotic agent used to treat various human cancers because of its antitumor activity. Unfortunately, up to 46% of the patients treated with bleomycin develop drug-induced interstitial lung disease (DIILD) and potentially life-threatening interstitial pulmonary fibrosis. Tools and [...] Read more.
Background: Bleomycin is an oncolytic and antibiotic agent used to treat various human cancers because of its antitumor activity. Unfortunately, up to 46% of the patients treated with bleomycin develop drug-induced interstitial lung disease (DIILD) and potentially life-threatening interstitial pulmonary fibrosis. Tools and biomarkers for predicting and detecting DIILD are limited. Therefore, we aimed to evaluate the feasibility of 18F-FDG PET/CT, PET radiomics, and machine learning in distinguishing DIILD in an explorative pilot study. Methods: Eighteen Hodgkin’s lymphoma (HL) patients, of whom 10 developed DIILD after treatment with bleomycin, were retrospectively included. Five diffuse large B-cell lymphoma (DLBCL) patients were included as a control group since they were not treated with bleomycin. All patients underwent 18F-FDG PET/CT scans before (baseline) and during treatment (interim). Structural changes were assessed by changes in Hounsfield Units (HUs). The 18F-FDG PET scans were used to assess metabolic changes by examining the feasibility of 504 radiomics features, including the mean activity of the lungs (SUVmean). A Random Forest (RF) classifier evaluated the identification and prediction of DIILD based on PET radiomics features. Results: HL patients who developed DIILD showed a significant increase in standard SUV metrics (SUVmean; p = 0.012, median increase 37.4%), and in some regional PET radiomics features (texture strength; p = 0.009, median increase 101.6% and zone distance entropy; p = 0.019, median increase 18.5%), while this was not found in HL patients who did not develop DIILD and DLBCL patients. The RF classifier correctly identified DIILD in 72.2% of the patients and predicted the development of DIILD correctly in 50% of the patients. There were no significant differences in HUs over time within all three patient groups. Conclusions: Our explorative longitudinal pilot study suggests that certain regional 18F-FDG PET radiomics features can effectively identify DIILD in HL patients treated with bleomycin, as significant longitudinal increases were observed in SUVmean, texture strength, and zone distance entropy after the development of DIILD. The metabolic activity of these features did not significantly increase over time in DLBCL patients and HL patients who did not develop DIILD. This indicates that 18F-FDG PET radiomics, with and without machine learning, might serve as potential biomarkers for detecting DIILD. Full article
(This article belongs to the Special Issue PET/CT Diagnostics and Theranostics)
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