Prediction of cardiac allograft vasculopathy using splenic switch-off on myocardial PET.

BACKGROUND: Heart transplantation (HTx) is a definitive therapy for refractory heart failure. Cardiac allograft vasculopathy (CAV), characterized by diffuse arteriopathy involving the epicardial coronary arteries and microvasculature, is the major cause of death for patients with HTx. 13N-ammonia positron emission tomography (NH3-PET) can offer diagnostic and prognostic utility for CAV. The splenic switch-off (SSO) detected in NH3-PET is a hemodynamic indicator of favorable response to adenosine. We hypothesized that both CAV and SSO reflected a pathology that progresses in parallel with systemic vascular endothelial dysfunction. Therefore, we quantitatively evaluated splenic adenosine reactivity measured using NH3-PET as an index of endothelial function, and examined its predictability for CAV. METHODS: Forty-eight patients who underwent NH3-PET after HTx were analyzed. The spleen ratio was calculated as the mean standardized uptake value, measured by placing an ROI on the spleen, at stress divided by that at rest. SSO was defined by a cutoff determined using receiver operating characteristic (ROC) analysis for the spleen ratio. The endpoint was appearance or progression of CAV. Predictability of SSO was analyzed using Kaplan-Meier analysis. RESULTS: The endpoint occurred in 9 patients during a mean follow-up of 45 ±â€¯17 months. ROC curve analysis demonstrated a cutoff of 0.94 for spleen ratio. Patients without SSO displayed a significantly higher CAV rate than those with SSO (p = 0.022). CONCLUSIONS: SSO reflects the endothelial function of systemic blood vessels and was a predictor of CAV in patients with HTx.

Risk Stratification Using Right Ventricular Longitudinal Strain Ratio Derived from 13N-Ammonia PET in Patients with Ischemic Heart Disease.

Purpose To investigate whether right ventricular (RV) myocardial strain ratio (RVMSR) assessed using nitrogen 13 ammonia (13N-NH3) PET can predict cardiovascular events in patients with ischemic heart disease (IHD). Materials and Methods This retrospective study included 480 consecutive patients (mean age, 66 years ± 12 [SD]; 334 males and 146 females) with IHD who underwent 13N-NH3 PET. RVMSR was defined as the ratio of RV strain during stress to that at rest. The primary end point was major adverse cardiac events (MACEs), defined as cardiac death or heart failure hospitalization. The ability of RVMSR to predict MACE was assessed using receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. Cox proportional hazards regression analysis was used to calculate hazard ratios (HRs) with 95% CIs. Results ROC curve analysis identified a sensitivity and specificity of 84% and 82%, respectively, for predicting MACE from RVMSR. Patients with reduced RVMSR (<110.2) displayed a significantly higher rate of MACE than those with a preserved RVMSR (34 of 240 vs four of 240; P < .001). Cox proportional hazards regression analysis of imaging parameters, including myocardial flow reserve, indicated that RVMSR was an independent predictor of MACE (HR, 0.94 [95% CI: 0.92, 0.97]; P < .001). Conclusion RVMSR was an independent predictor of MACE and has potential to aid in the risk stratification of patients with IHD. Keywords: Right Ventricular Myocardial Strain Ratio, Myocardial Flow Reserve, Ischemic Heart Disease, 13N-Ammonia Positron Emission Tomography Supplemental material is available for this article. © RSNA, 2024.

Large-vessel vasculitis induced by pegfilgrastim and immune checkpoint inhibitor in a patient with small-cell lung cancer.

A 75-year-old woman with stage IVB (cT3N3M1c) extensive disease small-cell lung cancer was treated with carboplatin, etoposide, and atezolizumab. Ten days after pegfilgrastim initiation, during the second chemotherapy cycle, she experienced back pain. Contrast-enhanced computed tomography revealed soft tissue thickening around the descending aorta and brachiocephalic artery. She was diagnosed with atezolizumab and pegfilgrastim-induced large-vessel vasculitis (LVV) and was treated with prednisolone, which was tapered and discontinued after 14 weeks, with no symptom recurrence. LVV should be included in the differential diagnosis of patients with nonspecific body pain when pegfilgrastim and immune checkpoint inhibitors are used in combination.

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECTSPT) against PET in 17 segments according to the American Heart Association (AHA). METHODS: This retrospective study evaluated the patient-to-patient stress, resting SPECT, and PET datasets of 71 patients. The SPECTSPT generation model was trained (stress: 979 image pairs, rest: 987 image pairs) and validated (stress: 421 image pairs, rest: 425 image pairs) using 31 cases of SPECT and PET image pairs using an image-to-image translation network. Forty of 71 cases of left ventricular base-to-apex short-axis images were translated to SPECTSPT in the stress and resting state (stress: 1830 images, rest: 1856 images). Representative voxel values of SPECT and SPECTSPT in the 17 AHA segments against PET were compared. The stress, resting, and difference scores of 40 cases of SPECT and SPECTSPT were also compared in each of the 17 segments. RESULTS: For AHA 17-segment-wise analysis, stressed SPECT but not SPECTSPT voxel values showed significant error from PET at basal anterior regions (segments #1, #6), and at mid inferoseptal regions (segments #8, #9, and #10). SPECT, but not SPECTSPT, voxel values at resting state showed significant error at basal anterior regions (segments #1, #2, and #6), and at mid inferior regions (segments #8, #9, and #11). Significant SPECT overscoring was observed against PET in basal-to-apical inferior regions (segments #4, #10, and #15) during stress. No significant overscoring was observed in SPECTSPT at stress, and only moderate over and underscoring in the basal inferior region (segment #4) was found in the resting and difference states. CONCLUSIONS: Our PET-supervised deep learning model is a new approach to correct well-known inferior wall attenuation in SPECT myocardial perfusion imaging. As standalone SPECT systems are used worldwide, the SPECTSPT generation model may be applied as a low-cost and practical clinical tool that provides powerful auxiliary information for the diagnosis of myocardial blood flow.