Frontiers in nuclear medicine (Lausanne, Switzerland)最新文献

Purpose: To calculate the physiological uptake of various tissues in patients with cancer using ¹⁸F-AlF-NOTA-FAPI-04 (¹⁸F-FAPI-04) and ¹⁸F-AlF-NOTA-FAPI-42 (¹⁸F-FAPI-42) PET/CT and to compare the variation in standard uptake values between the two scans.

Materials and methods: This retrospective analysis included 40 patients with cancer who underwent ¹⁸F-FAPI; the first 20 patients received ¹⁸F-FAPI-04 PET/CT and the remaining 20 patients received ¹⁸F-FAPI-42 PET/CT. A total of 49 normal tissues, including the brain (cerebrum/cerebellum), parotid and submandibular glands, palatine tonsils, and thyroid, were identified on CT images. For these normal tissues, maximum standardized uptake value (SUVmax) and mean standardized uptake value (SUVmean) were calculated. We also compared the SUVmean of identical tissues to explore the difference in biodistribution between the two radiotracers.

Results: The accumulation of ¹⁸F-FAPI-04 and ¹⁸F-FAPI-42 showed an analogous pattern. High uptake of both radiotracers in the gallbladder, uterus, submandibular gland, and renal pelvis was demonstrated (range: SUVmax, 4.01-5.75; SUVmean, 2.92-4.22). Furthermore, the uptake of bony tissues was slightly higher in ¹⁸F-FAPI-42 than in ¹⁸F-FAPI-04 (range: SUVmean, 0.4 ± 0.22-0.9 ± 0.34 and 0.3 ± 0.24-0.7 ± 0.18, respectively, p < 0.05), while the uptake of some soft tissues was higher in ¹⁸F-FAPI-04 than in ¹⁸F-FAPI-42 (range: SUVmean, 0.9 ± 0.24-1.5 ± 0.35 and 0.9 ± 0.26-1.2 ± 0.37, respectively, p < 0.05).

Conclusions: Both radioligands exhibited similar physiological uptake of normal tissues in patients with cancers. In addition, ¹⁸F-FAPI-42 demonstrated higher uptake of bone tissues than ¹⁸F-FAPI-04 while showing lower uptake of soft tissues than ¹⁸F-FAPI-04.

Artificial intelligence and machine learning (AI/ML) is poised to disrupt the structure and delivery of healthcare, promising to optimize care clinical care delivery and information management. AI/ML offers potential benefits in healthcare, such as creating novel clinical decision support tools, pattern recognition software, and predictive modeling systems. This raises questions about how AI/ML will impact the physician-patient relationship and the practice of medicine. Effective utilization and reliance on AI/ML also requires that these technologies are safe and reliable. Potential errors could not only pose serious risks to patient safety, but also expose physicians, hospitals, and AI/ML manufacturers to liability. This review describes how the law provides a mechanism to promote safety and reliability of AI/ML systems. On the front end, the Food and Drug Administration (FDA) intends to regulate many AI/ML as medical devices, which corresponds to a set of regulatory requirements prior to product marketing and use. Post-development, a variety of mechanisms in the law provide guardrails for careful deployment into clinical practice that can also incentivize product improvement. This review provides an overview of potential areas of liability arising from AI/ML including malpractice, informed consent, corporate liability, and products liability. Finally, this review summarizes strategies to minimize risk and promote safe and reliable AI/ML.

Alzheimer's disease (AD) is the most common neurodegenerative disease, with an increasing prevalence. Currently, there is no ideal diagnostic molecular imaging agent for diagnosing AD. Antibodies (Abs) have been proposed to close this gap as they can bind selectively and with high affinity to amyloid β (Aβ)-one of the molecular hallmarks of AD. Abs can even be designed to selectively bind Aβ oligomers or isoforms, which are difficult to target with small imaging agents. Conventionally, Abs must be labeled with long-lived radionuclides which typically results in in high radiation burden to healthy tissue. Pretargeted imaging could solve this challenge as it allows for the use of short-lived radionuclides. To develop pretargeted imaging tools that can enter the brain, AD mouse models are useful as they allow testing of the imaging approach in a relevant animal model that could predict its clinical applicability. Several mouse models for AD have been developed with different characteristics. Commonly used models are: 5xFAD, APP/PS1 and tg-ArcSwe transgenic mice. In this study, we aimed to identify which of these models were best suited to investigate pretargeted imaging approaches beyond the blood brain barrier. We evaluated this by pretargeted autoradiography using the Aβ-targeting antibody 3D6 and an ¹¹¹In-labeled Tz. Evaluation criteria were target-to-background ratios and accessibility. APP/PS1 mice showed Aβ accumulation in high and low binding brain regions and is as such less suitable for pretargeted purposes. 5xFAD and tg-ArcSwe mice showed similar uptake in high binding regions whereas low uptake in low binding regions and are better suited to evaluate pretargeted imaging approaches. 5xFAD mice are advantaged over tg-ArcSwe mice as pathology can be traced early (6 months compared to 18 months of age) and as 5xFAD mice are commercially available.

Rational: To validate a population-based input function (PBIF) model that alleviates the need for scanning since injection time in dynamic whole-body (WBdyn) PET.

Methods: Thirty-seven patients with suspected/known well-differentiated neuroendocrine tumors were included (GAPETNET trial NTC03576040). All WBdyn 68Ga-DOTATOC-PET/CT acquisitions were performed on a digital PET system (one heart-centered 6 min-step followed by nine WB-passes). The PBIF model was built from 20 image-derived input functions (IDIFs) obtained from a respective number of patients' WBdyn exams using an automated left-ventricle segmentation tool. All IDIF peaks were aligned to the median time-to-peak, normalized to patient weight and administrated activity, and then fitted to an exponential model function. PBIF was then applied to 17 independent patient studies by scaling it to match the respective IDIF section at 20-55 min post-injection time windows corresponding to WB-passes 3-7. The ratio of area under the curves (AUCs) of IDIFs and PBIF_3-7 were compared using a Bland-Altman analysis (mean bias ± SD). The Patlak-estimated mean Ki for physiological uptake (Ki-liver and Ki-spleen) and tumor lesions (Ki-tumor) using either IDIF or PBIF were also compared.

Results: The mean AUC ratio (PBIF/IDIF) was 0.98 ± 0.06. The mean Ki bias between PBIF_3-7 and IDIF was -2.6 ± 6.2% (confidence interval, CI: -5.8; 0.6). For Ki-spleen and Ki-tumor, low relative bias with low SD were found [4.65 ± 7.59% (CI: 0.26; 9.03) and 3.70 ± 8.29% (CI: -1.09; 8.49) respectively]. For Ki-liver analysis, relative bias and SD were slightly higher [7.43 ± 13.13% (CI: -0.15; 15.01)].

Conclusion: Our study showed that the PBIF approach allows for reduction in WBdyn DOTATOC-PET/CT acquisition times with a minimum gain of 20 min.

Background/rationale: PET/CT plays a crucial role in esophageal (EC) and gastroesophageal junction cancer (GEJ) diagnosis and management. Despite endorsement in clinical guidelines, variation in acceptance of PET/CT exists. The aim of this study was to assess the early use of PET/CT among EC and GEJ patients in a regionalized setting and identify factors contributing to disparity in access.

Materials and methods: Retrospective cohort study of adults with EC or GEJ between 2012 and 2014 from the Population Registry of Esophageal and Stomach Tumours of Ontario and Ontario Health (Cancer Care Ontario). Receipt of PET/CT and relevant demographics were collected, and statistical analysis performed. Continuous data were analysed with t-tests and Wilcoxon rank sum test. Categorical data were analysed with chi-square test. Kaplan-Meier methods were used to estimate median survival.

Results: Fifty-five percent of patients diagnosed with EC or GEJ between 2012 and 2014 received PET/CT (1321/2390). Eighty-four percent of patients underwent surgical resection (729/870), and 80% receiving radical treatment (496/622) underwent PET/CT. The use of PET/CT increased from 2012 to 2014. Male patients received more PET/CT than females (85% vs.78% p < 0.001).Median survival for the overall cohort was 11.1 months, 17.2 vs. 5.2 months among those who did and did not receive PET/CT and 35 vs. 27 months among the surgical cohort (p = 0.16).

Conclusions: We found that PET/CT use increased from 2012 to 2014 and that the majority of EC/GEJ patients being considered for curative therapy received PET/CT. There were also gender disparities identified. PET/CT appears to confer a potential survival benefit in our study, although our assessment is limited. Our findings may serve as learned lessons for other new imaging modalities, new indications for PET/CT or even for the introduction of new radiopharmaceuticals for PET/CT.

This article aims at presenting in a didactic way, dosimetry concepts and methods that are relevant for radio-embolization of the liver with ⁹⁰Y-microspheres. The application of the medical internal radiation dose formalism to radio-embolization is introduced. This formalism enables a simplified dosimetry, where the absorbed dose in a given tissue depends on only its mass and initial activity. This is applied in the single-compartment method, partition model, for the liver, tumour and lung dosimetry, and multi-compartment method, allowing identification of multiple tumours. Voxel-based dosimetry approaches are also discussed. This allows taking into account the non-uniform uptake within a compartment, which translates into a non-uniform dose distribution, represented as a dose-volume histogram. For this purpose, dose-kernel convolution allows propagating the energy deposition around voxel-sources in a computationally efficient manner. Alternatively, local-energy deposition is preferable when the spatial resolution is comparable or larger than the beta-particle path. Statistical tools may be relevant in establishing dose-effect relationships in a given population. These include tools such as the logistic regression or receiver operator characteristic analysis. Examples are given for illustration purpose. Moreover, tumour control probability modelling can be assessed through the linear-quadratic model of Lea and Catcheside and its counterpart, the normal-tissue complication probability model of Lyman, which is suitable to the parallel structure of the liver. The selectivity of microsphere administration allows tissue sparing, which can be considered with the concept of equivalent uniform dose, for which examples are also given. The implication of microscopic deposition of microspheres is also illustrated through a liver toxicity model, even though it is not clinically validated. Finally, we propose a reflection around the concept of therapeutic index (TI), which could help tailor treatment planning by determining the treatment safety through the evaluation of TI based on treatment-specific parameters.

Radiopharmaceutical practices are divided into large-scale industrial manufacturing and small-scale "in-house" hospital radiopharmacy unit. The recent evolution of nuclear medicine involves deep consequences in this ever-present regulatory state, and hospital radiopharmacy units cannot be considered as contract manufacturing organizations (CMO). This review provides an updated status report of the official (and non-official) guidelines supporting the regulations required to meet hospital and industry common radiopharmaceutical manufacturing standards to facilitate the current and future innovative radiopharmaceutical development.

Introduction: Brain and central nervous system (CNS) tumors are the second most common cancer type in children and adolescents. Positron emission tomography (PET) imaging with radiolabeled amino acids visualizes the amino acid uptake in brain tumor cells compared with the healthy brain tissue, which provides additional information over magnetic resonance imaging (MRI) for differential diagnosis, treatment planning, and the differentiation of tumor relapse from treatment-related changes. However, tumor delineation is a time-consuming task subject to inter-rater variability. We propose a deep learning method for the automatic delineation of O-(2-[¹⁸F]fluoroethyl)-l-tyrosine ([¹⁸F]FET PET) pediatric CNS tumors.

Methods: A total of 109 [¹⁸F]FET PET and MRI scans from 66 pediatric patients with manually delineated reference were included. We trained an artificial neural network (ANN) for automatic delineation and compared its performance against the manual reference on delineation accuracy and subsequent clinical metric accuracy. For clinical metrics, we extracted the biological tumor volume (BTV) and tumor-to-background mean and max (TBR_mean and TBR_max).

Results: The ANN produced high tumor overlap (median dice-similarity coefficient [DSC] of 0.93). The clinical metrics extracted with the manual reference and the ANN were highly correlated (r ≥ 0.99). The spatial location of TBR_max was identical in almost all cases (96%). The ANN and the manual reference produced similar changes in the clinical metrics between baseline and follow-up scans.

Conclusion: The proposed ANN achieved high concordance with the manual reference and may be an important tool for decision aid, limiting inter-reader variance and improving longitudinal evaluation in clinical routine, and for future multicenter studies of pediatric CNS tumors.

Imaging biomarkers have changed the way we study Alzheimer's disease and related dementias, develop new therapeutics to treat the disease, and stratify patient populations in clinical trials. With respect to protein aggregates comprised of amyloid-β plaques and tau neurofibrillary tangles, Positron Emission Tomography (PET) has become the gold standard imaging modality for quantitative visualization. Due to high infrastructural costs, the availability of PET remains limited to large urban areas within high income nations. This limits access to leading edge medical imaging, and potentially access to new treatments, by millions of rural and remote residents in those regions as well as billions of people in middle- and low-income countries. Single Photon Emission Computed Tomography (SPECT) is a more widely available imaging alternative with lower infrastructural costs and decades of familiarity amongst nuclear medicine professionals. Recent technological advances have closed the gap in spatial resolution and quantitation between SPECT and PET. If effective SPECT radiotracers were available to visualize amyloid-β plaques, geographic barriers to imaging could be circumvented. In this review, we will discuss past efforts to develop SPECT radiotracers targeting amyloid-β plaques which incorporate the most used radionuclide in nuclear medicine: technetium-99m (^99mTc; t _1/2 = 6.01 h; γ = 140 keV). While reviewing the various chemical scaffolds and chelates employed, the focus will be upon the impact to the pharmacological properties of putative ^99mTc-based amyloid-targeting radiotracers.