• Linda M Pak, Jayasree Chakraborty, Mithat Gonen, William C Chapman, Do Richard K G RKG Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY., Bas Groot Koerkamp, Kees Verhoef, Ser Yee Lee, Marco Massani, Eric P van der Stok, Amber L Simpson, and Memorial Sloan Kettering Cancer Center Hepatopancreatobiliary Service.
• Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
• J. Am. Coll. Surg. 2018 May 1; 226 (5): 835-843.

BackgroundPost-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 DesignWe 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) × 100. Significant clinical and imaging features were combined in a multivariate analysis using conditional logistic regression.ResultsFrom 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.ConclusionsQuantitative imaging features are independently associated with PHLI and are a promising preoperative risk stratification tool.Copyright © 2018 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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