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Advanced Segmentation in Multimodal Nuclear Imaging of Solid Tumors and Lymphomas: Tools, Challenges, and Clinical Integration

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 419

Special Issue Editors

1. Department of Nuclear Medicine, University Hospital CHU UCL, Godinne, Belgium
2. Faculté de Médecine, Université Catholique de Louvain UCLouvain, Ottignies-Louvain-la-Neuve, Belgium
Interests: multimodal nuclear imaging; artificial intelligence in nuclear medicine; radiomics in nuclear medicine; tumor segmentation; segmentation techniques; segmentation tools; outcome prediction; radioligand therapy

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Guest Editor
1. Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
2. Department of Radiation Oncology, Cantonal Hospital Winterthur, Winterthur, Switzerland
Interests: lung cancer; prostate cancer; SBRT; radiosurgery

Special Issue Information

Dear Colleagues,

We invite submissions for this Special Issue focusing on the evolving role of segmentation in Multimodal Nuclear Imaging of Solid Tumors and Lymphomas. As personalized medicine and theranostics gain momentum, accurate lesion segmentation has become critical, not only for radiomics and quantitative analysis but also for guiding treatment decisions and monitoring therapeutic response.

Despite technological advances, current segmentation approaches face major challenges, such as limited reproducibility, a lack of standardization, and barriers to clinical integration. The transition from manual or semi-automated techniques to advanced AI-based tools offers exciting opportunities, but it also raises new questions about robustness, transparency, and regulatory validation.

This Special Issue seeks high-quality original research, technical developments, and expert reviews addressing the following topics:

  • Tool comparison and standardization (manual vs. semi-automated vs. fully automated);
  • Multimodal segmentation (SPECT/CT, PET/CT, PET/MRI, MRI);
  • Radiomics, segmentation and prognostication in solid tumors and lymphomas;
  • Radiomics, segmentation and prognostication in radioligand therapies;
  • Clinical integration, training, and implementation strategies.

We particularly welcome interdisciplinary contributions that demonstrate real-world utility, clinical impact, or translational potential. Join us in shaping the future of oncologic imaging by contributing to this timely and clinically relevant Special Issue.

We look forward to receiving your contributions.

Dr. Ken Kudura
Dr. Robert Förster
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 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. Cancers 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 2900 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

  • multimodal nuclear imaging
  • artificial intelligence in nuclear medicine
  • radiomics in nuclear medicine
  • tumor segmentation
  • segmentation techniques
  • segmentation tools
  • outcome prediction
  • radioligand therapy

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Published Papers (1 paper)

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Research

20 pages, 4807 KB  
Article
Divergent Prognostic Value of Primary Tumor Segmentation Metrics on Baseline FDG PET/CT in Colorectal Cancer
by Ken Kudura, Nando Ritz, Yves Schaulin, Arkadiusz Miszczyszyn, Tim Kutzker, Rebecca Engel, Marco von Strauss und Torney, Wolfgang Harms and Robert Foerster
Cancers 2025, 17(21), 3592; https://doi.org/10.3390/cancers17213592 - 6 Nov 2025
Viewed by 331
Abstract
Background: Colorectal cancer (CRC) remains a major global health concern, with increasing incidence and mortality projected over the coming decades. Despite the central role of staging systems, substantial heterogeneity in clinical outcomes persists among patients within the same stage, highlighting the need for [...] Read more.
Background: Colorectal cancer (CRC) remains a major global health concern, with increasing incidence and mortality projected over the coming decades. Despite the central role of staging systems, substantial heterogeneity in clinical outcomes persists among patients within the same stage, highlighting the need for additional prognostic biomarkers. This study aimed to evaluate whether segmentation-derived morphological and metabolic features of the primary tumor could serve as prognostic biomarkers associated with subsequent tumor evolution in CRC. Methods: In this retrospective, single-center study, 91 patients with histologically confirmed CRC who underwent baseline FDG PET/CT prior to treatment were analyzed. Morphological (tumor shape, cranio-caudal extension, volume) and metabolic (SUVmean, SUVmax, MTV, TLG) parameters of the primary tumor were extracted using 3D segmentation. Clinical benefit (CB) was defined according to RECIST criteria at six months. Logistic regression and Cox proportional hazards models were applied to identify predictors of short- and long-term outcomes, with performance assessed using ROC curves and Kaplan–Meier survival analyses. Results: Cranio-caudal extension was the strongest prognostic biomarker of short-term clinical benefit (AUC = 0.89), with a threshold of 6.2 cm discriminating favorable from unfavorable outcomes. In multivariate analysis, early UICC stage and lower cranio-caudal extension were independently associated with CB. For long-term outcomes, MTV emerged as a consistent prognostic factor: higher MTV predicted shorter progression-free survival (HR = 1.03, p < 0.01) and overall survival (HR = 1.03, p < 0.01). In addition, UICC stage IV significantly increased the risk of progression (HR = 9.65, p < 0.01). Conclusions: Segmentation of the primary tumor on baseline FDG PET/CT provides valuable prognostic information in CRC. While cranio-caudal extension was the strongest prognostic biomarker of short-term treatment response, MTV was independently associated with long-term outcomes, particularly progression-free survival. These findings highlight the complementary prognostic roles of morphological and metabolic tumor features and support the integration of PET/CT-based biomarkers into personalized treatment strategies for colorectal cancer. Full article
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