OBJECTIVE: To assess the maintenance of efficacy of one year of tocilizumab (TCZ) monotherapy after its discontinuation in large vessel-GCA (LV-GCA).
METHODS: 17 patients with active LV-GCA were previously treated with 3 boluses of intravenous methylprednisone and weekly subcutaneous TCZ in monotherapy for 52 weeks. Patients in relapse-free clinical remission at week 52 discontinued TCZ and entered part two, which was a 26-week observational follow-up period. PET/CT was performed in all patients at the end of the 26-week observational period (week 78). End points were the variation in PET vascular activity score (PETVAS) at week 78 compared with baseline and with week 52, and the proportion of patients with relapse-free clinical remission at week 78 and at the end of the follow-up.
RESULTS: Compared with baseline, a significant reduction in PETVAS was observed at week 78, mean (95% CI) change -6.6 (-9.5 to -3.7). However, compared with week 52, PETVAS significantly increase 6 months after TCZ discontinuation (week 78), mean (95% CI) change 4.6 (0.7-8.5). The proportion of patients with relapse-free clinical remission at weeks 78 and at the end of the follow-up (median time from TCZ discontinuation 148 weeks) was 11/17 (65%, 95% CI 38-86) and 8/17 (47%, 95% CI 23-72), respectively. Age and sex-adjusted HR (95% CI) for each unit increase of PETVAS indicating subsequent relapse was 1.36 (0.92-2.00).
CONCLUSIONS: One year of TCZ monotherapy was effective in maintaining drug-free clinical remission in LV-GCA. Changes in PETVAS early after TCZ discontinuation may predict subsequent relapses.
BACKGROUND: ClinicalTrials.gov, NCT05394909.

OBJECTIVE: Accurate diagnosis and staging of prostate cancer are crucial to improving patient care. Prostate-specific membrane antigen (PSMA)-targeted positron emission tomography with computed tomography (PET/CT) imaging has demonstrated superiority for initial staging and restaging in patients with prostate cancer. Referring physicians and PET/CT readers must agree on a consistent communication method and application of information derived from this imaging modality. While several guidelines have been published, a single PSMA PET/CT reporting template has yet to be widely adopted. Based on the consensus from community and academic physicians, we developed a standardized PSMA PET/CT reporting template for radiologists and nuclear medicine physicians to report and relay key imaging findings to referring physicians. The aim was to improve the quality, clarity, and utility of imaging results reporting to facilitate patient management decisions.
METHODS: Based on community and expert consensus, we developed a standardized PSMA PET/CT reporting template to deliver key imaging findings to referring clinicians.
RESULTS: Core category components proposed include a summary of any prior treatment history; presence, location, and degree of PSMA radiopharmaceutical uptake in primary and/or metastatic tumor(s), lesions with no uptake, and incidentally found lesions with positive uptake on PET/CT.
CONCLUSIONS: This article provides recommendations on best practices for standardized reporting of PSMA PET/CT imaging. The generated reporting template is a proposed supplement designed to educate and improve data communication between imaging experts and referring physicians.

OBJECTIVE: Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance.
METHODS: The 2022 AutoPet Challenge\'s 1014 PET/CT dataset was used to train the lesion detection model based on 2D and 3D fractional-residual (F-Res) models. To extend this to PET/MRI, a network for converting MR images to synthetic CT (sCT) was developed, using 41 sets of whole-body MR and corresponding CT data. 38 patients\' PET/CT and PET/MRI data were used to verify the universal lesion recognition algorithm. Image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Total lesion glycolysis (TLG), metabolic tumor volume (MTV), and lesion count were calculated from the resultant lesion masks. Experienced physicians reviewed and corrected the model\'s outputs, establishing the ground truth. The performance of the lesion detection deep-learning model on different PET images was assessed by detection accuracy, precision, recall, and dice coefficients. Data with a detection accuracy score (DAS) less than 1 was used for analysis of outliers.
RESULTS: Compared to PET/CT, PET/MRI scans had a significantly longer delay time (135 ± 45 min vs 61 ± 12 min) and lower SNR (6.17 ± 1.11 vs 9.27 ± 2.77). However, CNR values were similar (7.37 ± 5.40 vs 5.86 ± 6.69). PET/MRI detected more lesions (with a mean difference of -3.184). TLG and MTV showed no significant differences between PET/CT and PET/MRI (TLG: 119.18 ± 203.15 vs 123.57 ± 151.58, p = 0.41; MTV: 36.58 ± 57.00 vs 39.16 ± 48.34, p = 0.33). A total of 12 PET/CT and 14 PET/MRI datasets were included in the analysis of outliers. Outlier analysis revealed PET/CT anomalies in intestines, ureters, and muscles, while PET/MRI anomalies were in intestines, testicles, and low tracer uptake regions, with false positives in ureters (PET/CT) and intestines/testicles (PET/MRI).
CONCLUSIONS: The deep learning lesion detection model performs well with both PET/CT and PET/MRI. SNR, CNR and reconstruction parameters minimally impact recognition accuracy, but delay time post-injection is significant.

Hepatic metastases of cranial meningiomas are rare, particularly when they present as a delayed, solitary metastasis, which poses a challenge for imaging-based diagnosis. [18F]FDG PET/CT facilitates diagnosis and posttreatment restaging, whereas somatostatin receptor-targeted PET demonstrates high sensitivity and specificity in the diagnosis of meningiomas and may potentially evaluate the viability of theranostics approaches, particularly for treatment-resistant meningiomas.

Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is challenging due to the heterogeneity of grayscale levels and fuzzy boundaries. To address these issues, in this paper, an efficient model-informed PET/CT tumor co-segmentation method that combines fuzzy C-means clustering and Bayesian classification information is proposed. To alleviate the grayscale heterogeneity of multi-modal images, in this method, a novel grayscale similar region term is designed based on the background region information of PET and the foreground region information of CT. An edge stop function is innovatively presented to enhance the localization of fuzzy edges by incorporating the fuzzy C-means clustering strategy. To improve the segmentation accuracy further, a unique data fidelity term is introduced based on PET images by combining the distribution characteristics of pixel points in PET images. Finally, experimental validation on datasets of head and neck tumor (HECKTOR) and non-small cell lung cancer (NSCLC) demonstrated impressive values for three key evaluation metrics, including DSC, RVD and HD5, achieved impressive values of 0.85, 5.32, and 0.17, respectively. These compelling results indicate that image segmentation methods based on mathematical models exhibit outstanding performance in handling grayscale heterogeneity and fuzzy boundaries in multi-modal images.

OBJECTIVE: This prospective study aims to evaluate the value of [18F]AlF-NOTA-fibroblast activation protein inhibitor (FAPI)-04 positron emission tomography-computed tomography (PET/CT) in predicting molecular subtypes of breast cancer.
METHODS: The study consecutively recruited patients suspected of having breast cancer from a single center who were prospectively enrolled from July 2023 to May 2024 and underwent [18F]AlF-NOTA-FAPI-04 PET/CT. This study compared the differences in tracer uptake among breast cancers with different adverse prognostic factors and molecular subtypes. The classification performance for each molecular subtype of breast cancer was assessed using a receiver operating characteristic (ROC) curve.
RESULTS: Fifty-three participants (mean age, 51 ± 11 years; 52 females) were evaluated. Breast cancer lesions with adverse prognostic factors showed higher tracer uptake. The five different molecular subtypes exhibited varying levels of uptake. The luminal A and luminal B (HER2-negative) subtypes had relatively low uptake, while the luminal B (HER2-positive), HER2-positive, and triple-negative subtypes had relatively high uptake. ROC analysis identified the max standardized uptake value (SUVmax) as a significant classifier (AUC = 0.912, P = 0.0005) for the luminal A subtype, with 100% sensitivity and 83% specificity. For predicting the luminal B (HER2-negative) subtype, SUVmax had an AUC of 0.770 (P = 0.0015). SUVmax, with an AUC of 0.781 (P = 0.003), was used to identify the triple-negative subtype tumors, resulting in a sensitivity of 100% and specificity of 51%. Lastly, the ROC curve showed the cut-off 15.40 (AUC = 0.921, P < 0.0001) could classify luminal A & luminal B (HER2-negative), and luminal B (HER2-positive) & HER2-positive & triple-negative, yielding a sensitivity of 94% and specificity of 79%.
CONCLUSIONS: The uptake of [18F]AlF-NOTA-FAPI-04 is significantly correlated with the molecular subtypes of breast cancer, and [18F]AlF-NOTA-FAPI-04 PET/CT is a potential tool for noninvasive identification of luminal A subtypes and guidance of FAP-targeted therapies.

BACKGROUND: Primary hyperparathyroidism is a common endocrine disorder characterised by excessive parathormone secretion that results in hypercalcemia, primarily caused by parathyroid adenoma. Accurate localisation of hyperfunctioning tissue is essential for curative surgical treatment. Although conventional imaging modalities like ultrasonography and 99mTc-MIBI scintigraphy (SPECT) along with 18F-fluorocholine PET/CT are commonly employed, there are cases with false-negative imaging results.
METHODS: This case report presents a patient with primary hyperparathyroidism and a parathyroid adenoma detected solely through 68Ga-PSMA-11 PET/CT, typically used for prostate cancer diagnosis. The lesion observed in the PET/CT was confirmed as a parathyroid adenoma through laboratory evaluation, while other imaging techniques failed to detect it.
CONCLUSIONS: This finding suggests that the PSMA ligands\' particular affinity for neovascularisation in focal changes may facilitate the visualisation of parathyroid adenomas. The utilisation of 68Ga-PSMA-11 PET/CT in primary hyperparathyroidism could potentially improve the preoperative localization of parathyroid adenomas when conventional imaging methods are inconclusive.

BACKGROUND: Fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) has been suggested as a useful imaging method for diagnosing cyst infections in patients with autosomal dominant polycystic kidney disease (ADPKD). The aim of this article is to provide evidence-based data in this setting.
METHODS: A systematic literature review (exploring several bibliographic databases) and a bivariate meta-analysis were carried out to calculate the pooled diagnostic performance of [18F]FDG PET/CT in diagnosing probable cyst infection in ADPKD.
RESULTS: Ten studies (282 PET/CT scans and 249 patients) were included in the analysis. The pooled sensitivity and specificity of [18F]FDG PET/CT in this setting were 84.6% (95% confidence interval: 75.4-90.7) and 94.9% (95% confidence interval: 72.6-99.2), respectively, without statistical heterogeneity or significant publication bias. [18F]FDG PET/CT significantly changed patient management in more than half of ADPKD patients with suspected cyst infection.
CONCLUSIONS: [18F]FDG PET/CT has high performance in diagnosing probable cyst infections in ADPKD patients with an impact on management in the majority of patients. Although more studies are warranted, the provided evidence-based data are an important step towards the integration of [18F]FDG PET/CT in clinical and diagnostic guidelines on probable cyst infection in ADPKD patients.

Non-somatostatin receptor expressing hypovascular insulinomas can be challenging to prove through imaging. This case highlights the utility of a structured approach to molecular imaging in patients with confirmed endogenous hyperinsulinemia. A 54-year-old woman was admitted because of a sudden loss of consciousness. Her relative reported that she complained of dizziness, intense sweating, blurry vision, and upper extremity tingling before becoming unresponsive for 20 min, after which the patient had little recollection of the event. She experienced similar episodes of shorter duration, trouble recalling everyday events, and unintentional weight gain of over 10 kg during the previous two years. Abdominal magnetic resonance imaging (MRI) and multidetector computerized tomography (MDCT) were unremarkable. Selective arterial calcium stimulation significantly increased hepatic venous insulin concentrations when the superior mesenteric and gastroduodenal arteries were stimulated. Technetium-99m (99mTc) octreotide single-photon emission computed tomography (SPECT) did not localize the lesion. Gallium-68 DOTA-Exendin-4 PET/CT acquisition was performed. A single intense 2 cm hyperperfused pancreatic lesion was located anteriorly in the head of the pancreas. Earlier targeted PET/CT imaging and recognition of significant neuropsychiatric symptoms attributable to the patient\'s hypoglycemic state might have accelerated the resolution of her condition and obviated the need for unnecessary testing.

The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.