Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis.

BACKGROUND: We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT). METHODS: We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptakeribs). A 2D U-net architecture was used to develop whole-heart segmentations for CT scans. Uptake was determined by calculating a heart-to-blood pool (HBP) ratio between the maximal counts value of the total heart region and the maximal counts value of the most superior ROI. RESULTS: Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort. CONCLUSION: We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.

Systemic lupus erythematosus predicts increased left ventricular mass.

BACKGROUND: Systemic lupus erythematosus (SLE) is associated with premature atherosclerosis and vascular stiffening. Whether SLE alters left ventricular (LV) structure and function in the absence of valvular and clinical coronary artery disease is unknown. METHODS AND RESULTS: SLE patients without clinical or echocardiographic evidence of valvular or coronary disease were age and gender matched to a reference group (n=173 in both groups). Subjects underwent echocardiography to quantify LV structure and function and carotid ultrasonography to detect atherosclerosis. Disease characteristics and radial applanation tonometry to measure arterial stiffness were evaluated in SLE patients. The 2 groups were similar in subjects' body size, hypertension and diabetes status, smoking status, and cholesterol levels. LV mass (38.3 versus 32.8 g/m(2.7)), ejection fraction (71% versus 67%), and prevalence of LV hypertrophy (17.9% versus 6.4%) were higher in SLE patients than in referent subjects (all P<0.001). The combination of SLE and hypertension further increased LV mass. In multivariable analysis, LV mass was associated with SLE (P<0.001) in addition to body mass index, diabetes mellitus, and hypertension. Among SLE patients, LV mass was associated with arterial stiffness (P<0.001). Carotid atherosclerosis, SLE duration, damage index, serum creatinine, and homocysteine were significantly related to LV mass in univariate but not multivariable analyses. CONCLUSIONS: SLE predicts increased LV mass, possibly because of inflammation-related arterial stiffening. Excess LV hypertrophy may contribute to the increased cardiac morbidity and mortality observed in SLE patients.