Journal of nuclear medicine technology最新文献

Tumor differentiation is a biologic process that significantly influences molecular imaging patterns and therapeutic response. This review examines how tumor differentiation shapes theranostic strategy across thyroid, neuroendocrine, and prostate cancers, with particular attention to the inverse association between tumor differentiation and glucose metabolism. In thyroid and neuroendocrine tumors, well-differentiated disease retains tissue-specific transporters and receptors, such as the sodium-iodide symporter and somatostatin receptors, enabling function-based imaging and targeted therapy. Well-differentiated neuroendocrine tumors upregulate somatostatin receptor expression. Conversely, poorly differentiated tumors in these categories lose these specialized features and exhibit enhanced glycolytic metabolism, resulting in increased [¹⁸F]FDG uptake. In prostate cancer, the differentiation-metabolism relationship is more complex. Prostate-specific membrane antigen (PSMA) is a membrane protein expressed on the apical surface of endothelial cells in prostate and nonprostate tissue. It is weakly expressed in normal prostate tissue but upregulated in prostate cancer and several solid malignancies. Within prostate adenocarcinoma, PSMA expression generally increases with higher Gleason score and tumor aggressiveness, making dedifferentiating tumors and metastatic disease highly PSMA-avid on imaging and suitable for PSMA-targeted therapy. However, in treatment-emergent neuroendocrine prostate cancer, PSMA expression may be lost despite intense [¹⁸F]FDG uptake, reflecting lineage plasticity rather than classical dedifferentiation. This tumor-specific differentiation-driven model has significant implications for patient management, treatment planning, and prognosis. Understanding these tumor-specific differentiation patterns allows clinicians to optimize diagnostic imaging and therapeutic strategies in precision oncology and personalized medicine.

The recent reinvigoration of theranostics comes with advances in computing technology, radiochemistry, and instrumentation that synergize with developments in artificial intelligence (AI). There is a wide array of applications of AI in nuclear medicine that have translational benefits to theranostics, including attenuation correction, artifact and noise reduction, enhanced workflow, and lesion characterization, and segmentation and quantitation, among many others. For theranostics, there are potentially significant applications that could move closer to precision medicine. Perhaps the most important application is predictive dosimetry from diagnostic images to optimize therapeutic dose. There are also valuable benefits from AI-augmented radioligand design and development, preclinical imaging, and practice sustainability. Generative AI has also emerged as a powerful tool to support decision-making, information dissemination, and medical image analysis. There are, however, several ongoing challenges that must be considered pertaining to the development and application of AI tools in theranostics.

Radioligand therapy (RLT) is a recent term for long established nuclear medicine practices. Although radioiodine provides the historical foundations of theranostics, the prototype RLTs include ⁶⁸Ga-DOTATATE and ¹⁷⁷Lu-DOTATATE, which target somatostatin receptor subtype 2 in neuroendocrine tumors, and ⁶⁸Ga-PSMA-617 and ¹⁷⁷Lu-PSMA-617, which target prostate cancer. There are several challenges and opportunities in the realization of precision medicine through RLT. RLT, weaponized in cancer management through advances in instrumentation and radiochemistry, is transforming the nuclear oncology landscape. Equitable access to these advanced tools remains a global consideration.

Theranostics is changing how we approach treatment in nuclear medicine. Combining targeted imaging with radiopharmaceutical therapy allows us to personalize care in ways that were not previously possible. As this field grows, so does the need for strong coordination, clear protocols, and effective teamwork. From the technologist's perspective, these treatments are rewarding but also complex, requiring attention to detail and flexibility in clinical care. This article shares lessons learned from working directly with patients and teams involved in delivering theranostics care. It covers practical takeaways in patient selection, imaging setup, scan preparation, and therapy workflows, with specific examples from thyroid cancer, neuroendocrine tumors, and prostate cancer. We also reflect on how communication, posttherapy follow-up, and technologist-led problem-solving can significantly impact patient outcomes and satisfaction. Through every phase, we highlight the importance of collaboration, clarity, and preparation in making these therapies successful. We share these lessons as a reflection of what we have learned thus far in this evolving field. Every step has taught us something new about the care we provide, the teams with whom we work, and the impact we can have. As technologists, we are proud to be part of this next chapter in nuclear medicine.

Theranostics, the combination of targeted diagnostic imaging and treatment, is rapidly expanding its role beyond oncology into various noncancerous diseases. Recent advances in radiopharmaceuticals, molecular imaging, and nanoparticle-based technologies are enabling the detection and treatment of conditions in cardiology, neurology, autoimmune, and bone marrow disorders. These innovations include targeted imaging and therapy for atherosclerosis and cardiac amyloidosis, as well as neurodegenerative disorders such as Alzheimer disease. Additionally, they encompass biomarkers such as fibroblast activation protein inhibitor and radiolabeled glucocorticoids in autoimmune and inflammatory diseases, as well as the selective ablation of diseased tissue in bone marrow conditioning. Despite the promise of these developments, several challenges must be considered, including the integration of theranostic strategies into standard practice and establishing their efficacy through robust clinical trials. This review examines the emerging nononcologic applications of theranostics, highlighting current research and future potential.

With the evolving scope of nuclear medicine, including PET, radiopharmaceutical therapy, and hybrid imaging with CT and MRI, ensuring technologist competency across multiple modalities is critical. Oregon Health and Science University has implemented Smartsheet-a collaborative work management platform-to organize, document, and streamline onboarding and competency tracking for technologists in nuclear medicine, PET/CT, radiopharmaceutical therapy, and CT. Beyond supporting compliance with the expectations of accrediting bodies, this tool fosters a culture of continuous improvement and staff empowerment.

Theranostics, the integration of diagnostic imaging with targeted radiopharmaceutical therapy, is reshaping the practice of nuclear medicine worldwide. This article provides a global perspective on how multidisciplinary teams deliver theranostics care in diverse health care systems. Contributions from experts across Asia, Europe, South Africa, Australia, Latin America, and the United States highlight the shared and region-specific roles of nuclear medicine physicians, technologists, radiopharmacists, physicists, and nurses in patient selection, radiopharmaceutical preparation, treatment delivery, and posttherapy care. A consistent theme across regions is the central role of the nuclear medicine physician in treatment oversight and the expanding responsibilities of nuclear medicine technologists in imaging protocols, radiopharmaceutical administration, patient care, and dosimetry. The roles of radiopharmaceutical scientists, medical physicists, nurses, and trial coordinators are variable but overall beneficial in delivering a successful theranostics service. Workforce shortages, financial barriers, and regulatory challenges continue to be significant obstacles to equitable patient access. Despite these challenges, multidisciplinary collaboration, tumor board integration, and shared care models are improving patient outcomes and advancing the field.

In recent years, the field of nuclear medicine has seen a massive surge in both the use of existing radiopharmaceuticals and the development of entirely new agents, particularly those classified under the subcategory of theranostics. Theranostics involves the use of radiopharmaceuticals in diagnostic and therapeutic pairs, usually to treat oncology patients. Although these advancements have greatly benefited patients and the field of nuclear medicine, they have also intensified existing challenges within departments and introduced new ones. This article identifies key nonnuclear medicine stakeholders, highlights methods for disseminating important information to those identified groups, and describes tangible processes for assessing competency in theranostics treatments for those with hands-on involvement.

Recent technologic advancements in PET, including silicon photomultipliers and block-sequential regularized expectation maximization (BSREM) tools, have allowed for renewed assessment of the optimal acquisition and reconstruction parameters in pediatric imaging. This work evaluates the performance of BSREM reconstruction and varied count density (CD) in digital [¹⁸F]FDG PET/CT to investigate the feasibility of reducing the injected activity or duration of acquisition in children with cancer. Methods: Five hundred unique reconstructions from 20 pediatric patients evaluated with PET/CT per clinical standard of care (SOC) were included in this retrospective study. Three-dimensional, whole-body imaging was acquired on a silicon photomultiplier PET/CT system in list mode with time-of-flight modeling. Imaging volumes were reconstructed with varying time per bed position (180, 120, 90, 60, and 45 s) to simulate a range of CDs using conventional iterative techniques (ordered-subset expectation maximization) and BSREM with varied regularization strength (β, 175-700). Two pediatric nuclear medicine physicians individually scored all studies, with patient information, reconstruction method, and CD concealed, rating technical quality and overall diagnostic satisfaction on a 5-point Likert scale. Quantitative SUV measurements on all reconstructions were compared with the clinical SOC. Results: Reconstruction with BSREM with a β of 500 or greater significantly improved overall scores across all CDs when compared with ordered-subset expectation maximization. Noise performance improved after application of a higher regularization parameter. Spatial resolution (sharpness) was greatest with a β of 350. Mean overall image quality at 25% CD using a β of 500 or greater was considered diagnostic. Mean liver and blood-pool SUV-to-noise ratio performed best with the highest β and CD. SUV_max behavior was complex, varying with reconstruction strength and CD, with measurements at β of 500 or greater differing from the SOC by no more than 15% across all CDs, and specific combinations varying by 10% or less. Conclusion: Clinical evaluation of whole-body [¹⁸F]FDG PET/CT in pediatric patients was diagnostic at all reductions in CD when using BSREM with a β of 500 or greater. Quantitative performance was variable, yet SUV_max differences of 10% or less were achievable with the appropriate selection of acquisition and reconstruction parameters. This study found that customized imaging parameters can reduce injected activity (radiation dose) and imaging time to best suit the pediatric patient.