Digital histology quantification of intra-hepatic fat in patients undergoing liver resection
European Journal of Surgical Oncology (EJSO)(2015)
摘要
BACKGROUND:High intra-hepatic fat (IHF) content is associated with insulin resistance, visceral adiposity, and increased morbidity and mortality following liver resection. However, in clinical practice, IHF is assessed indirectly by pre-operative imaging [for example, chemical-shift magnetic resonance (CS-MR)]. We used the opportunity in patients undergoing liver resection to quantify IHF by digital histology (D-IHF) and relate this to CT-derived anthropometrics, insulin-related serum biomarkers, and IHF estimated by CS-MR.
METHODS:A reproducible method for quantification of D-IHF using 7 histology slides (inter- and intra-rater concordance: 0.97 and 0.98) was developed. In 35 patients undergoing resection for colorectal cancer metastases, we measured: CT-derived subcutaneous and visceral adipose tissue volumes, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), fasting serum adiponectin, leptin and fetuin-A. We estimated relative IHF using CS-MR and developed prediction models for IHF using a factor-clustered approach.
RESULTS:The multivariate linear regression models showed that D-IHF was best predicted by HOMA-IR (Beta coefficient(per doubling): 2.410, 95% CI: 1.093, 5.313) and adiponectin (β(per doubling): 0.197, 95% CI: 0.058, 0.667), but not by anthropometrics. MR-derived IHF correlated with D-IHF (rho: 0.626; p = 0.0001), but levels of agreement deviated in upper range values (CS-MR over-estimated IHF: regression versus zero, p = 0.009); this could be adjusted for by a correction factor (CF: 0.7816).
CONCLUSIONS:Our findings show IHF is associated with measures of insulin resistance, but not measures of visceral adiposity. CS-MR over-estimated IHF in the upper range. Larger studies are indicated to test whether a correction of imaging-derived IHF estimates is valid.
更多查看译文
关键词
Hepatic steatosis,Obesity,Insulin resistance,Visceral adiposity,Liver surgery
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要