4.5 Article

Quantitative Imaging Features and Postoperative Hepatic Insufficiency: A Multi-Institutional Expanded Cohort

Journal

JOURNAL OF THE AMERICAN COLLEGE OF SURGEONS
Volume 226, Issue 5, Pages 835-843

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jamcollsurg.2018.02.001

Keywords

-

Categories

Funding

  1. NIH/NCI [P30 CA008748]
  2. Clinical and Translation Science Center at Weill Cornell Medical Center
  3. MSKCC [UL1TR00457]

Ask authors/readers for more resources

BACKGROUND: Post-hepatectomy liver insufficiency (PHLI) is a significant cause of morbidity and mortality after liver resection. Quantitative imaging analysis using CT scans measures variations in pixel intensity related to perfusion. A preliminary study demonstrated a correlation between quantitative imaging features of the future liver remnant (FLR) parenchyma from preoperative CT scans and PHLI. The objective of this study was to explore the potential application of quantitative imaging analysis in PHLI in an expanded, multi-institutional cohort. STUDY DESIGN: We retrospectively identified patients from 5 high-volume academic centers who developed PHLI after major hepatectomy, and matched them to control patients without PHLI (by extent of resection, preoperative chemotherapy treatment, age [+/- 5 years], and sex). Quantitative imaging features were extracted from the FLR in the preoperative CT scan, and the most discriminatory features were identified using conditional logistic regression. Percent remnant liver volume (RLV) was defined as follows: (FLR volume)/(total liver volume) x 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression. RESULTS: From 2000 to 2015, 74 patients with PHLI and 74 matched controls were identified. The most common indications for surgery were colorectal liver metastases (53%), hepatocellular carcinoma (37%), and cholangiocarcinoma (9%). Two CT imaging features (FD1_4: image complexity; ACM1_10: spatial distribution of pixel intensity) were strongly associated with PHLI and remained associated with PHLI on multivariate analysis (p = 0.018 and p = 0.023, respectively), independent of clinical variables, including preoperative bilirubin and % RLV. CONCLUSIONS: Quantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool. ((C) 2018 by the American College of Surgeons. Published by Elsevier Inc. All rights reserved.)

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available