Seminars in nuclear medicine最新文献

The field of nuclear medicine has undergone remarkable advances, particularly with the introduction of new devices, radionuclides for imaging and therapy, new clinical applications, and emergence of medical evidence. As this dynamic field continues its rapid expansion, there is an urgent need to increase the number of well-trained professionals globally. Consequently, advocating for nuclear medicine as a thriving field of study and work for women becomes paramount in ensuring the establishment of a robust workforce capable of meeting the growing demands. True gender equality will only be achieved when there is equal representation across the spectrum of the nuclear medicine professions, including nuclear medicine technologists, radiopharmacists, radiochemist, medical physicists, nuclear medicine physicians, administrators, academics, and leaders. Currently, the workforce exhibits an imbalance, with females predominating among nuclear medicine technologists, while the number of female physicians, and those in leadership positions remains comparatively lower. There are various factors which contribute to the existing inequities. Societal expectations often impose traditional gender roles that somehow discourage women from pursuing a career in the science, technology, and mathematics (STEM) fields, including nuclear medicine. Additionally, prevailing unequal work conditions and gender biases within the workplace can create barriers that hinder women's professional growth and development. Ways of addressing inequalities includes ensuring female participation at all levels of education and training and promoting the field at undergraduate level in medical school. Mentorship programs have demonstrated great success in guiding and supporting women at various stages of their careers. Therefore, there is a need for their expansion and enhancement. Furthermore, female role models play a pivotal role in shattering gender stereotypes and inspiring other women to pursue careers in nuclear medicine and its related fields. By addressing the existing imbalances and fostering an environment that actively encourages and supports women, we can harness the full potential of all professionals, thus ensuring the ongoing progress and advancement of nuclear medicine.

Prostate-specific membrane antigen (PSMA)-targeted PET agents have revolutionized the care of patients with prostate cancer, supplanting traditional methods of imaging prostate cancer, and improving the selection and delivery of therapies. This has led to a rapid expansion in both the number of PSMA PET scans performed and the imaging specialists required to interpret those scans. To aid those imagers and clinicians who are new to the interpretation of PSMA PET, this review provides an overview of the interpretation of PSMA PET/CT imaging and pearls for overcoming commonly encountered pitfalls. We discuss the physiologic distribution of the clinically available PSMA-targeted radiotracers, the commonly encountered patterns of prostate cancer spread, as well as the benign and malignant mimics of prostate cancer. Additionally, we review the standardized PSMA PET reporting systems and the role of PSMA in selecting appropriate patients for PSMA-targeted therapies.

In the last years, PSMA-PET imaging and multiparametric MRI (mpMRI) have improved the clinical management of prostate cancer (PCa) patients. Currently, mpMRI is recommended by the EAU (European Association of Urology) guidelines for the primary diagnosis of PCa, whereas PSMA-PET is reserved for disease staging, particularly in high risk localized or locally advanced disease, as well as for biochemical recurrence after surgery. Nevertheless, several studies have explored the added value of PSMA-PET in other clinical scenarios, including primary diagnosis and especially for the detection of clinically significant PCa (csPCa). In the present contribution, we will provide an overview and an update on the current literature on imaging detection of csPCa, with a particular focus on mpMRI, PSMA-PET and their comparison.

[⁶⁸Ga]Ga-PentixaFor, a PET agent targeting CXCR4 is emerging as a versatile radiotracer with promising applications in oncology, cardiology and inflammatory disease. Preclinical work in various cancer cell lines have demonstrated high specificity and selectivity. In human investigations of several tumors, the most promising applications may be in multiple myeloma, certain lymphomas and myeloproliferative neoplasms. In the nononcologic setting, [⁶⁸Ga]Ga-PentixaFor could greatly improve detection for primary aldosteronism and other endocrine abnormalities. Similarly, atherosclerotic disease and other inflammatory conditions could also benefit from enhanced identification by CXCR4 targeting. Rapidly cleared from the body with a favorable imaging and radiation dosimetry profile that has been already studied in over 1000 patients, [⁶⁸Ga]Ga-PentixaFor is a worthy agent for further clinical exploration with potential for theranostic applications in hematologic malignancies.

Artificial intelligence (AI) has evolved significantly in the past few decades. This thriving trend has also been seen in medicine in recent years, particularly in the field of imaging. Machine learning (ML), deep learning (DL), and their methods (eg, SVM, CNN), as well as radiomics, are the terminologies that have been introduced to this field and, to some extent, become familiar to the expert clinicians. PET is one of the modalities that has been enhanced via these state-of-the-art algorithms. This robust imaging technique further merged with anatomical modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), to provide reliable hybrid modalities, PET/CT and PET/MRI. Applying AI-based algorithms on the different components (PET, CT, and MRI) has resulted in promising results, maximizing the value of PET imaging. However, [¹⁸F]F-FDG, the most commonly utilized tracer in molecular imaging, has been mainly in the spotlight. Thus, we aimed to look into the less discussed tracers in this review, moving beyond [¹⁸F]F-FDG. The novel non-[¹⁸F]F-FDG agents also showed to be valuable in various clinical tasks, including lesion detection and tumor characterization, accurate delineation, and prognostic impact. Regarding prostate patients, PSMA-based models were highly accurate in determining tumoral lesions’ location and delineating them, particularly within the prostate gland. However, they also could assess whole-body images to detect extra-prostatic lesions in a patient automatically. Considering the prognostic value of prostate-specific membrane antigen (PSMA) PET using AI, it could predict response to treatment and patient survival, which are crucial in patient management. Choline imaging, another non-[¹⁸F]F-FDG tracer, similarly showed acceptable results that may be of benefit in the clinic, though the current evidence is significantly more limited than PSMA. Lastly, different subtypes of DOTA ligands were found to be valuable. They could diagnose tumoral lesions in challenging sites and even predict histopathology grade, being a highly advantageous noninvasive tool. In conclusion, the current limited investigations have shown promising results, leading us to a bright future for AI in molecular imaging beyond [¹⁸F]F-FDG.

This paper describes the evolution of nuclear cardiology techniques in the setting of acute coronary syndromes. Since the 1970s, the contribution of nuclear cardiology has been fundamental in delineating the physiopathology and diagnosis of acute myocardial infarction, when electrocardiogram (ECG) did not provide the diagnosis and when cardiac enzyme assessments were at a very early stage. In this clinical situation, at that time the role of pyrophosphate scintigraphy and antimyosin antibodies was important in ensuring diagnostic precision. However, these methods showed limitations and were abandoned in the late 80s and early 90s when therapeutic applications such as thrombolytic therapy, and primary-and rescue-percutaneous coronary intervention (PCI) were introduced. Beginning in the mid-80s, the introduction and widespread use of perfusion tracers such as 99mTc labelled compounds and technological advances such as SPECT, allowed to assess the efficacy of thrombolysis and early revascularization, as well as to assess in depth myocardial salvage. Currently, perfusion SPECT, especially using fast imaging techniques and dedicated cardiac SPECT with solid-state detectors, allows a quick confirmation or exclusion of acute coronary syndromes, particularly in low-to-intermediate likelihood of coronary artery disease (CAD), especially when there are absolute or relative contraindications to the use of coronary computed tomographic angiography (CCTA).

Prostate-specific membrane antigen (PSMA) is a highly expressed protein in prostate cancer (PCa) and has become an increasingly popular target for molecular imaging in recent years. PSMA based positron-emission-tomography/computed tomography (PET/CT) is a well characterised hybrid imaging modality that combines the high sensitivity of PET with the high spatial resolution of CT imaging. The combination of these two imaging modalities provides an accurate tool for detecting and managing PCa. Several diagnostic accuracy and clinical management studies investigating the role of PSMA PET/CT in PCa have been published recently. This study aimed to perform an updated systematic review and meta-analysis to evaluate the diagnostic performance of PSMA PET/CT in localised, lymph node metastatic (LNM) and recurrent PCa patients and assess its impact on the clinical management of primary and recurrent PCa. Using Medline, Embase, PubMed and Cochrane Library databases, studies reporting the diagnostic accuracy and clinical management of PSMA PET/CT were analysed based on the PRISMA guidelines. Statistical analyses were conducted using random-effects models, and meta-regression explored observed heterogeneity. Results indicate that the sensitivity and specificity of PSMA PET/CT for localised PCa were 71.0% (95% confidence interval (CI): 58.0, 81.0) and 92.0% (95% CI: 86.0, 96.0), respectively (N = 10; n = 404 patients). Sensitivity and specificity in LNM were 57.0% (95% CI: 49.0, 64.0) and 96.0% (95% CI: 95.0, 97.0) (N = 36; n = 3,659 patients). For patients with biochemical recurrence (BCR), sensitivity was 84.0% (95% CI: 74.0, 90.0), and specificity was 97.0% (95% CI: 88.0, 99.0) (N = 9; n = 818 patients). The pooled proportion of management changes in primary (N = 16; n = 1,099 patients) and recurrent (N = 40; n = 5,398 patients) PCa was 28.0% (95% CI: 23.0, 34.0) and 54.0% (95% CI: 50.0, 58.0), respectively. In conclusion, PSMA PET/CT shows moderate sensitivity and high specificity in localised and LNM disease, while the accuracy in BCR patients was high. PSMA PET/CT also had a large impact on the clinical management of PCa patients. This is the most extensive and first systematic review to include three subgroups of PCa with histologically verified diagnostic accuracy and clinical management change reported separately in primary and recurrent disease settings.

This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.