Our objective was to evaluate the association of arterial ¹⁸F-FDG uptake and calcifications in large arteries as detected by ¹⁸F-FDG PET/CT with the subsequent occurrence of vascular events in otherwise asymptomatic cancer patients. Methods: Clinical follow-up information was obtained for 932 cancer patients examined with whole-body ¹⁸F-FDG PET/CT (median follow-up time, 29 mo). Among this cohort, 279 patients had died from their oncologic disease. In 15 of 932 patients (1.6%), a vascular event, defined as ischemic stroke, myocardial infarction, or revascularization, was registered. The maximal standardized uptake value (SUV) was divided by the blood-pool SUV, yielding a target-to-background ratio (TBR) for each arterial segment. The mean TBR as well as a calcified plaque sum score per patient were calculated in the major vessels: ascending, descending, and abdominal aorta, aortic arch, as well as iliac and carotid arteries. Results: A significant correlation was observed between mean TBR and calcified plaque sum (P < 0.001). Although calcified plaque sum significantly correlated with all conventional risk factors for vascular events, mean TBR correlated only with age, the male sex, and hypertension. The Cox regression hazard model identified a mean TBR ≥ 1.7 and a calcified plaque sum ≥ 15 as independent predictors for the occurrence of a vascular event. Patients with both mean TBR and calcified plaque sum above these thresholds were identified as having the highest risk for a future vascular event. However, a mean TBR ≥ 1.7 had greater prognostic value than did a calcified plaque sum ≥ 15. Conclusion: In a large cohort of cancer patients, increased ¹⁸F-FDG uptake in major arteries emerged as the strongest predictor of a subsequent vascular event. Concomitant severe vascular calcifications seemed to impart a particularly high risk. Given the small event rate in the present study, larger, prospective trials of patients without cancer are required to substantiate these promising results.

We measured the uptake of the somatostatin receptor ligand ⁶⁸Ga-[1,4,7,10-tetraazacyclododecane-N,N′,N″,N′″-tetraacetic acid]-d-Phe¹,Tyr³-octreotate (DOTATATE) in the left anterior descending coronary artery (LAD) in association with calcified plaques (CPs) and cardiovascular risk factors. Methods: Seventy consecutive tumor patients were examined by whole-body ⁶⁸Ga-DOTATATE contrast-enhanced PET/CT. Blood-pool–corrected standardized uptake value (target-to-background ratio) was measured in the LAD, and CT images were used to detect CP. Cardiovascular risk factors and history of prior cardiovascular events were recorded. Results: ⁶⁸Ga-DOTATATE uptake was detectable in the LAD of all patients. Target-to-background ratio in the LAD correlated significantly with the presence of CP (R = 0.34; P < 0.01), prior vascular events (R = 0.26; P < 0.05), and male sex (R = 0.29; P < 0.05), whereas CP correlated with these parameters but also with age (R = 0.34; P < 0.01) and hypertension (R = 0.25; P < 0.05). Conclusion: In a series of oncologic patients, those with prior cardiovascular events and calcified atherosclerotic plaques showed significantly increased ⁶⁸Ga-DOTATATE uptake in the LAD, suggesting a potential role of this tracer for plaque imaging in the coronary arteries.

Purpose: Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. Methods: Fifty-two patients who underwent multi-parametric dual-tracer [¹⁸F]FMC and [⁶⁸Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [⁶⁸Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (M_LH). Furthermore, M_BCR and M_OPR predictive model schemes were built by combining M_LH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [⁶⁸Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. Results: The area under the receiver operator characteristic curve (AUC) of the M_LH model (0.86) was higher than the AUC of the [⁶⁸Ga]Ga-PSMA-11 SUV_max analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the M_BCR and M_OPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. Conclusion: Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

Previously proposed classifications for carotid plaque and cerebral parenchymal hemorrhages are used to estimate the age of hematoma according to its signal intensities on T1w and T2w MR images. Using these classifications, we systematically investigated the value of cardiovascular magnetic resonance (CMR) in determining the age of vessel wall hematoma (VWH) in patients with spontaneous cervical artery dissection (sCAD).