Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Saboury, Babak; Bradshaw, Tyler; Boellaard, Ronald; Buvat, Irène; Dutta, Joyita; Hatt, Mathieu; Jha, Abhinav K.; Li, Quanzheng; Liu, Chi; McMeekin, Helena; Morris, Michael; Scott, Peter J. H.; Siegel, Eliot L.; Sunderland, John; Pandit‐Taskar, Neeta; Wahl, Richard L.; Zuehlsdorff, Sven; Rahmim, Arman

doi:10.2967/jnumed.121.263703

Cited by 20 publications

(9 citation statements)

References 76 publications

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“…Artificial Intelligence (AI), a discipline that, in its broadest definition, encompasses the development of systems that can exhibit human-like characteristics such as intelligence or behavior [ 37 ], has been increasingly impacting a wide range of applications. AI is poised to bring about a paradigm shift in all facets of society, with especially transformative changes to the medical field [ 38 ], including radiology and nuclear medicine [ 39 , 40 ]. With regard to nuclear medicine, AI-based algorithms show promise for implementation in many areas, such as image acquisition, reconstruction, post-processing, and segmentation, as well as diagnostics and prognostics [ 41 ].…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry

Georgiou,

Nielsen,

Chiriboga

et al. 2023

JCM

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Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases. RAIT dosimetry can be employed to determine the optimal treatment dose of I-131 to effectively treat cancer cells while safeguarding against undesirable radiation effects such as bone marrow toxicity or radiation pneumonitis. Conventional dosimetry protocols for RAIT, however, are complex and time-consuming, involving multiple days of imaging and blood sampling. This study explores the use of Artificial Intelligence (AI) in simplifying and optimizing RAIT. A retrospective analysis was conducted on 83 adult patients with DTC who underwent RAIT dosimetry at our institution between 1996 and 2023. The conventional MIRD-based dosimetry protocol involved imaging and blood sampling at 4, 24, 48, 72, and 96 h post-administration of a tracer activity of I-131. An AI system based on a deep-learning neural network was developed to predict the maximum permissible activity (MPA) for RAIT using only the data obtained from the initial 4, 24, and 48 h time points. The AI system predicted the MPA values with high accuracy, showing no significant difference compared to the results obtained from conventional MIRD-based analysis utilizing a paired t-test (p = 0.351, 95% CI). The developed AI system offers the potential to streamline the dosimetry process, reducing the number of imaging and blood sampling sessions while also optimizing resource allocation. Additionally, the AI approach can uncover underlying relationships in data that were previously unknown. Our findings suggest that AI-based dosimetry may be a promising method for patient-specific treatment planning in differentiated thyroid carcinoma, representing a step towards applying precision medicine for thyroid cancer. Further validation and implementation studies are warranted to assess the clinical applicability of the AI system.

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Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry

Georgiou,

Nielsen,

Chiriboga

et al. 2023

JCM

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“…Saboury et al [153] proposed a roadmap toward trustworthy AI ecosystems in nuclear medicine. The authors referred to the twelve key elements of trustworthiness AI systems entitled 'human agency', 'oversight', 'technical robustness', 'safety', 'privacy', 'predetermined change control plan', 'security', 'diversity and bias awareness', 'stakeholder participation', 'transparency and explainability', 'sustainability of societal well-being', and the last but not least is 'fairness and supportive context of implementation'.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting SSTR and PSMA

Yazdani,

Geramifar,

Karamzade-Ziarati

et al. 2023

Preprint

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Radiotheranostics refers to pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radio-pharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiother-apeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI) as important areas in quantitative image analysis are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radio-theranostics, focusing on pairs of SSTR- or PSMA‑targeting radioligands, describing the funda-mental concepts and specific imaging/treatment features. Our review includes ligands radio-labeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions by radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restag-ing, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

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“…AI has the potential to revolutionise the interpretation and analysis of medical images, leading to better accuracy, efficiency, and patient care ( Fig. 1 E) 88 89 . These imaging and radiomics data could, for example, support clinicians in the diagnosis of cancer 90 .…”

Section: Applications Of Artificial Intelligence In Routine Clinical ...mentioning

confidence: 99%

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

Fuchs,

Kaiser,

Müller

et al. 2023

Nuklearmedizin

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Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.

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Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem

Abstract: Financial support: None (individual COIs for each author are listed in the Disclosures section).

Cited by 20 publications

References 76 publications

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting SSTR and PSMA

Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data

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