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Current Oncology
Special Issues
AI-Powered Oncologic Nuclear Medicine in Clinical Translation: Advanced Assessment of...
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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: 30 April 2026 | Viewed by 292

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

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Published Papers (1 paper)

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Research

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 167
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)
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