Deep Learning-Enabled Quantification of Tc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.
Journal:
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
PMID:
38724278
Abstract
Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with Tc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial Tc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of Tc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Patients who underwent SPECT/CT Tc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as Tc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.