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Radioligand Therapy in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 2382

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Division of Nuclear Medicine, Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
Interests: nuclear medicine; radiology
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue of Cancers on “Radioligand Therapy in Cancer”. Radioligand therapy in cancer is considered one of the most promising approaches to cancer treatment, through which precise administration of radioligand limits damage to healthy organs and allows for the administration of higher therapeutic doses. In recent years, several radioligands have successfully joined the arsenal for routine treatment of prostate cancer and neuroendocrine tumors. However, several clinical dilemmas still complicate the use of radioligands, including patient selection, dosimetry schemes, and the detailed role of imaging. This Special Issue covers all important topics in radioligand therapy, including dosimetry, patient selection, toxicity assessment and reduction, efficacy assessment and improvement, new therapeutic radioligands, the role of imaging in radioligand therapy, and artificial intelligence and machine learning in radioligand therapy.

Dr. Ahmad Shariftabrizi
Guest Editor

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Keywords

  • PSMA and SSTR targeting radiopharmaceuticals
  • old and novel targets for radioligand therapy
  • dosimetry
  • toxicity evaluation and reduction
  • machine learning and artificial intelligence in radionuclide therapy
  • novel radiopharmaceutical
  • regulatory aspects of radioligand therapy
  • patient selection optimization
  • radioligand therapy for neuroblastoma and other pediatric malignancies

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Published Papers (2 papers)

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Research

18 pages, 2704 KiB  
Article
Computer-Aided Detection (CADe) of Small Metastatic Prostate Cancer Lesions on 3D PSMA PET Volumes Using Multi-Angle Maximum Intensity Projections
by Amirhosein Toosi, Sara Harsini, Ghasemali Divband, François Bénard, Carlos F. Uribe, Felipe Oviedo, Rahul Dodhia, William B. Weeks, Juan M. Lavista Ferres and Arman Rahmim
Cancers 2025, 17(9), 1563; https://doi.org/10.3390/cancers17091563 - 3 May 2025
Viewed by 602
Abstract
Objectives: We aimed to develop and evaluate a novel computer-aided detection (CADe) approach for identifying small metastatic biochemically recurrent (BCR) prostate cancer (PCa) lesions on PSMA-PET images, utilizing multi-angle Maximum Intensity Projections (MA-MIPs) and state-of-the-art (SOTA) object detection algorithms. Methods: We fine-tuned and [...] Read more.
Objectives: We aimed to develop and evaluate a novel computer-aided detection (CADe) approach for identifying small metastatic biochemically recurrent (BCR) prostate cancer (PCa) lesions on PSMA-PET images, utilizing multi-angle Maximum Intensity Projections (MA-MIPs) and state-of-the-art (SOTA) object detection algorithms. Methods: We fine-tuned and evaluated 16 SOTA object detection algorithms (selected across four main categories of model types) applied to MA-MIPs as extracted from rotated 3D PSMA-PET volumes. Predicted 2D bounding boxes were back-projected to the original 3D space using the Ordered Subset Expectation Maximization (OSEM) algorithm. A fine-tuned Medical Segment-Anything Model (MedSAM) was then also used to segment the identified lesions within the bounding boxes. Results: The proposed method achieved a high detection performance for this difficult task, with the FreeAnchor model reaching an F1-score of 0.69 and a recall of 0.74. It outperformed several 3D methods in efficiency while maintaining comparable accuracy. Strong recall rates were observed for clinically relevant areas, such as local relapses (0.82) and bone metastases (0.80). Conclusion: Our fully automated CADe tool shows promise in assisting physicians as a “second reader” for detecting small metastatic BCR PCa lesions on PSMA-PET images. By leveraging the strength and computational efficiency of 2D models while preserving 3D spatial information of the PSMA-PET volume, the proposed approach has the potential to improve detectability and reduce workload in cancer diagnosis and management. Full article
(This article belongs to the Special Issue Radioligand Therapy in Cancer)
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11 pages, 1083 KiB  
Article
PSMA-Guided Imaging and Therapy of Advanced Adenoid Cystic Carcinomas and Other Salivary Gland Carcinomas
by Nils F. Trautwein, Andreas Brendlin, Gerald Reischl, Moritz Mattke, Frank Paulsen, Hubert Loewenheim, Lars Zender, Christian la Fougère and Helmut Dittmann
Cancers 2024, 16(22), 3843; https://doi.org/10.3390/cancers16223843 - 15 Nov 2024
Cited by 1 | Viewed by 1312
Abstract
SGCs are rare malignancies, accounting for less than 1% of all head and neck cancers [...] Full article
(This article belongs to the Special Issue Radioligand Therapy in Cancer)
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