Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.

Here, we aimed to develop and validate a fully automated artificial intelligence (AI)-based method for the detection and quantification of suspected prostate tumour/local recurrence, lymph node metastases, and bone metastases from [¹⁸F]PSMA-1007 positron emission tomography-computed tomography (PET-CT) images. Images from 660 patients were included. Segmentations by one expert reader were ground truth. A convolutional neural network (CNN) was developed and trained on a training set, and the performance was tested on a separate test set of 120 patients. The AI method was compared with manual segmentations performed by several nuclear medicine physicians. Assessment of tumour burden (total lesion volume (TLV) and total lesion uptake (TLU)) was performed. The sensitivity of the AI method was, on average, 79% for detecting prostate tumour/recurrence, 79% for lymph node metastases, and 62% for bone metastases. On average, nuclear medicine physicians’ corresponding sensitivities were 78%, 78%, and 59%, respectively. The correlations of TLV and TLU between AI and nuclear medicine physicians were all statistically significant and ranged from R = 0.53 to R = 0.83. In conclusion, the development of an AI-based method for prostate cancer detection with sensitivity on par with nuclear medicine physicians was possible. The developed AI tool is freely available for researchers.

The Deauville score (DS) is a clinical tool, based on the comparison between lesion and reference organ uptake of 18F-fluorodeoxyglucose (FDG), used to stratify patients with lymphoma into categories reflecting their disease status. With a plethora of positron emission tomography with computed tomography (PET-CT) hard- and software algorithms, standard uptake value (SUV) in lesions and reference organs may differ which affects DS classification and therefore medical treatment. The EANM Research Ltd. (EARL) harmonization program from the European Association of Nuclear Medicine (EANM) partly mitigates this issue, but local preferences are common in clinical practice. We have investigated the discordance in DS calculated from patients with lymphoma referred for 18F-FDG PET-CT reconstructed with three different algorithms: the newly introduced block-sequential regularization expectation-maximization algorithm commercially sold as Q. Clear (QC, GE Healthcare, Milwaukee, WI, USA), compliant with the newly proposed updated EARL recommendations, and two settings compliant with the current EARL recommendations (EARLlower and EARLupper, representing the lower and upper limit of the EARL recommendations). Fifty-two patients with non-Hodgkin and Hodgkin lymphoma were included (18 females and 34 males). Segmentation of mediastinal blood pool and liver were semi-automatically performed, whereas segmentation of lesions was done manually. From these segmentations, SUVmax and SUVpeak were obtained and DS calculated. There was a significant difference in DS between the QC algorithm and EARLlower/EARLupper (p < 0.0001 for both) but not between EARLlower and EARLupper (p = 0.102) when SUVmax was used. For SUVpeak, there was a significant difference between QC and EARLlower (p = 0.001), but not for QC vs EARLupper (p = 0.071) or EARLlower vs EARLupper (p = 0.102). Five non-responders (DS 4–5) for QC were classified as responders (DS 1–3) when EARLlower/EARLupper was used, both when SUVmax and SUVpeak were investigated. Using the proposed updated EARL recommendations compared with the current recommendations will significantly change DS classification. In select cases, the discordance would affect the choice of medical treatment. Specifically, the current EARL recommendations were more often prone to classify patients as responders.

Background: Current imaging modalities are often incapable of identifying nociceptive sources of low back pain (LBP). We aimed to characterize these by means of positron emission tomography/computed tomography (PET/CT) of the lumbar spine region applying tracers ¹⁸F‐fluorodeoxyglucose (FDG) and ¹⁸F‐sodium fluoride (NaF) targeting inflammation and active microcalcification, respectively.Methods: Using artificial intelligence (AI)‐based quantification, we compared PET findings in two sex‐ and age‐matched groups, a case group of seven males and five females, mean age 45 ± 14 years, with ongoing LBP and a similar control group of 12 pain‐free individuals. PET/CT scans were segmented into three distinct volumes of interest (VOIs): lumbar vertebral bodies, facet joints and intervertebral discs. Maximum, mean and total standardized uptake values (SUVmax, SUVmean and SUVtotal) for FDG and NaF uptake in the 3 VOIs were measured and compared between groups. Holm–Bonferroni correction was applied to adjust for multiple testing.Results: FDG uptake was slightly higher in most locations of the LBP group including higher SUVmean in the intervertebral discs (0.96 ± 0.34 vs. 0.69 ± 0.15). All NaF uptake values were higher in cases, including higher SUVmax in the intervertebral discs (11.63 ± 3.29 vs. 9.45 ± 1.32) and facet joints (14.98 ± 6.55 vs. 10.60 ± 2.97).Conclusion: Observed intergroup differences suggest acute inflammation and microcalcification as possible nociceptive causes of LBP. AI‐based quantification of relevant lumbar VOIs in PET/CT scans of LBP patients and controls appears to be feasible. These promising, early findings warrant further investigation and confirmation.

Multislice cardiac CT characterizes late stage macrocalcification in epicardial arteries as opposed to PET/CT, which mirrors early phase arterial wall changes in epicardial and transmural coronary arteries. With regard to tracer, there has been a shift from using mainly ¹⁸F-fluorodeoxyglucose (FDG), indicating inflammation, to applying predominantly ¹⁸F-sodium fluoride (NaF) due to its high affinity for arterial wall microcalcification and more consistent association with cardiovascular risk factors. To make NaF-PET/CT an indispensable adjunct to clinical assessment of cardiac atherosclerosis, the Alavi–Carlsen Calcification Score (ACCS) has been proposed. It constitutes a global assessment of cardiac atherosclerosis burden in the individual patient, supported by an artificial intelligence (AI)-based approach for fast observer-independent segmentation. Common measures for characterizing epicardial coronary atherosclerosis by NaF-PET/CT as the maximum standardized uptake value (SUV) or target-to-background ratio are more versatile, error prone, and less reproducible than the ACCS, which equals the average cardiac SUV. The AI-based approach ensures a quick and easy delineation of the entire heart in 3D to obtain the ACCS expressing ongoing global cardiac atherosclerosis, even before it gives rise to CT-detectable coronary calcification. The quantification of global cardiac atherosclerotic burden by the ACCS is suited for management triage and monitoring of disease progression with and without intervention.