Background Recurrence after resection of colorectal liver metastases (CRLMs) occurs in up to 75% of patients. Preoperative prediction of hepatic recurrence may inform therapeutic strategies at the time of initial resection. Texture analysis (TA) is an established technique that quantifies pixel intensity variations (heterogeneity) on cross-sectional imaging. We hypothesized that tumoral and parenchymal changes that are predictive of overall survival (OS) and recurrence in the future liver remnant (FLR) can be detected using TA on preoperative computed tomography images. Methods Patients who underwent resection for CRLM between 2003 and 2007 with appropriate preoperative computed tomography scans were included (n=198) in this retrospective study. Texture features extracted from the tumor and FLR and clinicopathologic variables were incorporated into a multivariable survival model. Results Quantitative imaging features of the FLR were an independent predictor of both OS and hepatic disease-free survival. Tumor texture showed significant association with OS. TA of the FLR allowed patient stratification into two groups with significantly different risks of hepatic recurrence (HR, 2.09; 95% CI, 1.33–3.28; p=0.001). Patients with homogeneous parenchyma had approximately twice the risk of hepatic recurrence (41% vs. 20%). Conclusion TA of the tumor and FLR are independently associated with OS. TA of the FLR is independently associated with HDFS. Patients with homogeneous parenchyma had a significantly higher risk of hepatic recurrence. Preoperative TA of the liver represents a potential biomarker to identify patients at risk of liver recurrence after resection for CRLM.

Background Texture analysis is a promising method of analyzing imaging data to potentially enhance diagnostic capability. This approach involves automated measurement of pixel intensity variation that may offer further insight into disease progression than standard imaging techniques alone. We postulate that postoperative liver insufficiency, a major source of morbidity and mortality, correlates with preoperative heterogeneous parenchymal enhancement that can be quantified with texture analysis of cross-sectional imaging. Study Design A retrospective case-matched study (waiver of informed consent and HIPAA authorization, approved by the institutional review board) was performed comparing patients who underwent major hepatic resection and developed liver insufficiency (n=12) to a matched group of patients with no postoperative liver insufficiency (n=24) by procedure, remnant volume, and year of procedure. Texture analysis (with gray-level co-occurrence matrices) was used to quantify the heterogeneity of liver parenchyma on preoperative computed tomography (CT) scans. Statistical significance was evaluated using Wilcoxon’s signed rank and Pearson’s chi-squared tests. Results No statistically significant differences were found between study groups for preoperative patient demographics and clinical characteristics, with the exception of gender (p<0.05). Two texture features differed significantly between the groups: correlation (linear dependency of gray levels on neighboring pixels) and entropy (randomness of brightness variation) (p<0.05). Conclusions In this preliminary study, the texture of liver parenchyma on preoperative CT, was significantly more varied, less symmetric, and less homogeneous in patients with postoperative liver insufficiency; thus texture analysis has the potential to provide an additional means of preoperative risk stratification.

Soft-tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface-based metrics, and subsurface validation has largely been performed via phantom experiments. The proposed method involves the analysis of two deformation-correction algorithms for open hepatic image-guided surgery systems via subsurface targets digitized with tracked intraoperative ultrasound (iUS). Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration and for use in retrospective deformation-correction algorithms. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. Mean closest-point distances between the feature contours delineated in the iUS images and corresponding three-dimensional anatomical model generated from preoperative tomograms were computed to quantify the extent to which the deformation-correction algorithms improved registration accuracy. The results for six patients, including eight anatomical targets, indicate that deformation correction can facilitate reduction in target error of [Formula: see text].

• Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

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