Knight, Stacey

BACKGROUND: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization. METHODS: We included 18 079 patients with cardiac PET/CT from 6 sites. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis. RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19–1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23–1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20–1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23–1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18–1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0–1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6–1.17]) were independent predictors of reduced myocardial flow reserve. CONCLUSIONS: Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.

Abstract Background: Multiple genome-wide and candidate gene association studies have been conducted in search of common risk variants for breast cancer. Recent large meta analyses, consolidating evidence from these studies, have been consistent in highlighting the caspase-8 (CASP8) gene as important in this regard. To define a risk haplotype and map the CASP8 gene region with respect to underlying susceptibility variant/s, we screened four genes in the CASP8 region on 2q33-q34 for breast cancer risk. Methods: Two independent data sets from the United Kingdom and the United States, including 3,888 breast cancer cases and controls, were genotyped for 45 tagging single nucleotide polymorphisms (tSNP) in the expanded CASP8 region. SNP and haplotype association tests were carried out using Monte Carlo-based methods. Results: We identified a three-SNP haplotype across rs3834129, rs6723097, and rs3817578 that was significantly associated with breast cancer (P < 5 × 10−6), with a dominant risk ratio and 95% CI of 1.28 (1.21–1.35) and frequency of 0.29 in controls. Evidence for this risk haplotype was extremely consistent across the two study sites and also consistent with previous data. Conclusion: This three-SNP risk haplotype represents the best characterization so far of the chromosome upon which the susceptibility variant resides. Impact: Characterization of the risk haplotype provides a strong foundation for resequencing efforts to identify the underlying risk variant, which may prove useful for individual-level risk prediction, and provide novel insights into breast carcinogenesis. Cancer Epidemiol Biomarkers Prev; 21(1); 176–81. ©2011 AACR.