Digital histology quantification of intra-hepatic fat in patients undergoing liver resection

被引:9
作者
Parkin, E. [1 ,2 ]
O'Reilly, D. A. [1 ,2 ]
Plumb, A. A. [3 ]
Manoharan, P. [3 ]
Rao, M. [4 ]
Coe, P. [1 ,2 ]
Frystyk, J. [5 ]
Ammori, B. [2 ]
Carino, N. de Liguori [2 ]
Deshpande, R. [2 ]
Sherlock, D. J. [2 ]
Renehan, A. G. [1 ,6 ]
机构
[1] Univ Manchester, Inst Canc Sci, Manchester M20 4BX, Lancs, England
[2] North Manchester Gen Hosp, Dept Hepatobiliary Surg, Manchester, Lancs, England
[3] Christie Hosp NHS Fdn Trust, Dept Radiol, Manchester, Lancs, England
[4] Royal Oldham Hosp, Dept Histopathol, Oldham, England
[5] Aarhus Univ, Med Res Lab, Dept Clin Med, DK-8000 Aarhus C, Denmark
[6] Christie Hosp NHS Fdn Trust, Dept Colorectal Surg, Manchester, Lancs, England
来源
EJSO | 2015年 / 41卷 / 08期
关键词
Hepatic steatosis; Obesity; Insulin resistance; Visceral adiposity; Liver surgery; SUBCUTANEOUS ADIPOSE-TISSUE; HEPATIC STEATOSIS; METABOLIC RISK; CHEMOTHERAPY; DISEASE; STEATOHEPATITIS; METASTASES; PREDICTOR; SURVIVAL; OBESITY;
D O I
10.1016/j.ejso.2015.05.003
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
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 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 (beta(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 overestimated IHF in the upper range. Larger studies are indicated to test whether a correction of imaging-derived IHF estimates is valid. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1020 / 1027
页数:8
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