Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
3.4
Journals
Current Oncology
Special Issues
AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of...
share announcement

AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of Tumor Load and Microenvironment

A special issue of Current Oncology (ISSN 1718-7729).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2280

Special Issue Editor

Dr. Christian Furth

E-Mail Website
Guest Editor
Department of Nuclear Medicine, Charité – Universitätsmedizin Berlin, 13353 Berlin, Germany
Interests: nuclear medicine; radiology; theranostics; molecular imaging; PET/CT; PSMA; radioligand therapy; artificial intelligence; radiomics; immunotherapy response assessment; prostate cancer; lung cancer

Special Issue Information

Dear Colleagues,

Recent advances in artificial intelligence (AI) and machine learning have profoundly impacted oncologic nuclear medicine, opening new avenues for precision diagnostics and theranostics. This Special Issue, “AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of Tumor Load and Microenvironment”, aims to showcase cutting-edge research on how AI methods enhance the quantitative and biological understanding of tumor burden and its microenvironment. Topics include the integration of radiomics and deep learning into PET and SPECT imaging workflows, automated tumor segmentation, predictive modeling for therapy response, and multi-omics approaches that bridge imaging, pathology, and immunology. By moving beyond conventional SUV metrics, AI-driven tools allow for a more nuanced characterization of tumor heterogeneity, immune contexture, and treatment dynamics. We particularly welcome original research and reviews that address the clinical translation of these technologies, regulatory and standardization challenges, as well as real-world implementation in oncology. Our goal is to provide a platform for interdisciplinary exchange and to accelerate the integration of AI-powered nuclear medicine into personalized cancer care.

Dr. Christian Furth
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. Current Oncology 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 2200 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

  • AI in nuclear medicine
  • PET- and SPECT-imaging
  • radiomics and deep learning
  • tumor microenvironment
  • tumor burden quantification
  • theranostics
  • precision oncology
  • immuno-oncology imaging
  • clinical translation
  • predictive modeling

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 882 KB  
Article
Automated PROMISE V2 Scoring from PSMA PET/CT Reports Using Large Language Models: A Comparative Evaluation of Prompt Design and Model Performance
by Tilman Speicher, Isa Ethem Demirkol, Arne Blickle, Moritz B. Bastian, Stephan Maus, Andrea Schaefer-Schuler, Mark Bartholomä, Caroline Burgard, Samer Ezziddin and Florian Rosar
Curr. Oncol. 2026, 33(6), 349; https://doi.org/10.3390/curroncol33060349 - 9 Jun 2026
Viewed by 145
Abstract
Large language models (LLMs) are increasingly explored for clinical use. However, the extent to which such models can reliably support physicians in reporting, staging, and the assessment of classification remains an active area of research. This study aimed to evaluate and compare multiple [...] Read more.
Large language models (LLMs) are increasingly explored for clinical use. However, the extent to which such models can reliably support physicians in reporting, staging, and the assessment of classification remains an active area of research. This study aimed to evaluate and compare multiple LLMs for automated PROMISE V2 classification for prostate cancer. A total of 126 unambiguous German-language PSMA PET/CT text reports were retrospectively analyzed, with reference standards established by expert consensus based on image interpretation and the original report text. Five LLMs (GPT-5.4, DeepSeek-V3.2, Claude Sonnet 4.6, Gemini 3 Flash and Grok 4) were assessed using two English-language prompting strategies of varying complexity. Agreement with the reference standard served as the primary endpoint. Performance varied in the short-prompt setting (36.5–79.4%) but improved consistently with the long prompt (74.6–86.5%), with Gemini 3 Flash achieving the highest agreement. Across PROMISE V2 subcategories, agreement rates were high (miT: 81.0–92.1%, miN: 92.9–96.0%, miM: 92.9–95.2%), despite inter-model differences. In conclusion, contemporary LLMs demonstrate promising performance in deriving PROMISE V2 scores from unambiguous original report texts, particularly when guided by detailed prompts. Full article
(This article belongs to the Special Issue AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of Tumor Load and Microenvironment)
►▼ Show Figures

Figure 1

16 pages, 3412 KB  
Article
CT Radiomics Models Did Not Outperform Experts in Predicting [68Ga]Ga-PSMA-PET Positivity in Prostate Cancer Lymph Node Staging
by Thula Cannon Walter-Rittel, Boris Gorodetski, Alexander Hartenstein, Julian Rogasch, Imke Schatka, Holger Amthauer, Marcus Makowski, Charlie Alexander Hamm and Tobias Penzkofer
Curr. Oncol. 2026, 33(3), 146; https://doi.org/10.3390/curroncol33030146 - 2 Mar 2026
Viewed by 749
Abstract
Background: The use of [68Ga]Ga-PSMA-PET/CT for prostate cancer (PCa) staging is limited by cost and availability. This study evaluates whether radiomic features from contrast-enhanced (CE) CT can predict PSMA-positive lymph nodes (LNs) as a surrogate for metastasis. Methods: A [...] Read more.
Background: The use of [68Ga]Ga-PSMA-PET/CT for prostate cancer (PCa) staging is limited by cost and availability. This study evaluates whether radiomic features from contrast-enhanced (CE) CT can predict PSMA-positive lymph nodes (LNs) as a surrogate for metastasis. Methods: A retrospective study of 447 patients included 2537 segmented LNs (425 PET-positive, 2112 PET-negative). Two uroradiologists assessed 417 LNs on CE-CT using a four-point Likert scale. Radiomic features were extracted, selected using four algorithms, and analyzed with six model-building methods. Model performance was compared to radiologist ratings. Results: Radiomic models achieved an accuracy of 0.77–0.85, sensitivity of 0.85–0.91, and specificity of 0.74–0.85. Compared to radiologists, models had higher NPV (0.97–0.98 vs. 0.96) and sensitivity (0.85–0.91 vs. 0.76), but radiologists had superior accuracy (0.95 vs. 0.77–0.85) and specificity (0.97–0.98 vs. 0.74–0.85). In a subanalysis of LNs rated as probably benign or malignant, expert radiologists outperformed the algorithm with greater specificity and PPV (p < 0.005). A density threshold of >27 HU predicted PSMA-positive LNs with 0.79 accuracy, 0.87 sensitivity, and 0.78 specificity. Conclusions: While radiomics did not outperform expert radiologists, the single first-order parameter CT density >27 HU was predictive of PSMA-positive LNs. Clinical Relevance Statement: Radiomic models did not outperform expert uroradiologists. However, in high-volume or resource-limited settings lacking access to [68Ga]Ga-PSMA-PET/CT, they may help improve LN assessment in PCa patients with CT alone. Full article
(This article belongs to the Special Issue AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of Tumor Load and Microenvironment)
►▼ Show Figures

Graphical abstract

15 pages, 1198 KB  
Article
Independent Validation of a Machine Learning Classifier for Predicting Mediastinal Lymph Node Metastases in Non-Small Cell Lung Cancer Using Routinely Obtainable [18F]FDG-PET/CT Parameters
by Agata Wdowiak, Julian M. M. Rogasch, Georg L. Baumgärtner, Nikolaj Frost, Jens-Carsten Rückert, Jens Neudecker, Sebastian Ochsenreither, Manuela Gerhold, Bernd Schmidt, Mareike Graff, Holger Amthauer, Tobias Penzkofer and Christian Furth
Curr. Oncol. 2025, 32(12), 679; https://doi.org/10.3390/curroncol32120679 - 1 Dec 2025
Viewed by 903
Abstract
In non-small cell lung cancer (NSCLC), [18F]FDG-PET/CT is limited in pretherapeutic lymph node (LN) staging by false-positives. We previously demonstrated that a machine learning (ML) classifier using routine [18F]FDG-PET/CT and clinical variables can improve diagnostic accuracy compared to visual [...] Read more.
In non-small cell lung cancer (NSCLC), [18F]FDG-PET/CT is limited in pretherapeutic lymph node (LN) staging by false-positives. We previously demonstrated that a machine learning (ML) classifier using routine [18F]FDG-PET/CT and clinical variables can improve diagnostic accuracy compared to visual assessment. The present study aimed at independent validation. Cohort 1 (Charité) included 87 NSCLC patients (surgical and non-surgical), prospectively enrolled at our institution. Cohort 2 (TCIA) comprised 124 patients with primary surgery from the multi-institution NSCLC Radiogenomics dataset. Our ML classifier for differentiating N0/1 vs. N2/3 status was applied without modification. As comparator, the combined standard PET/CT criterion of “mediastinal LN uptake > mediastinum and/or short-axis > 10 mm” was used. Histology of N2/3 LNs served as reference standard. Prevalence of pN2/3 differed significantly between cohorts (Charité: 40%, TCIA: 12%; p < 0.001). Specificity was similar between ML and the standard PET/CT criterion in the Charité cohort (65% vs. 60%; p = 0.5) but significantly higher with ML in TCIA (90% vs. 70%; p < 0.001). Sensitivity for pN2/3 was comparable between the two comparators in both the Charité cohort (97% each; p = 1.0) and TCIA (27% vs. 33%; p = 1.0). Lower sensitivity in TCIA patients reflects the preselection of surgical patients who had already been clinically staged and deemed suitable for surgery. The diagnostic performance of the ML classifier and its (potentially) superior specificity were thus successfully validated in two independent cohorts. Full article
(This article belongs to the Special Issue AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of Tumor Load and Microenvironment)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop