Deep Learning–Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis

作者：Robert J. H. Miller, A. Shanbhag, Anna M. Michalowska, P. Kavanagh, Joanna X. Liang, V. Builoff, N. Fine, D. Dey, Daniel S. Berman, P. Slomka · 发表于：Journal of Nuclear Medicine · 年份：2024 · DOI：10.2967/jnumed.124.267542 · 被引用次数：35 · 研究领域：Medicine

Visual Abstract Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-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 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-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. Results: 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...