Quantification of Colorectal Liver Metastases using FDG PET Volumetric and Heterogeneity Features for Improved Prediction of Clinical Outcome

Objectives: There is an urgent clinical need for improved prognostication of patients with colorectal liver metastases to assist choice of therapy. In this work, we aim to improve prediction of clinical outcome, by moving beyond conventional standard uptake value (SUV) measures, and to perform volumetric and heterogeneity quantification of FDG uptake. Methods: We analyzed pre-treatment FDG PET images from 52 patients with colorectal intrahepatic-only metastases (29/23 males/females; mean age 62.9 years [SD 9.8; range 32-82]), and assessed prediction of overall survival (OS), progression-free survival (PFS) and event-free survival (EFS). The number of events for OS (death), PFS (progression defined as local recurrence in the liver, or new metastases anywhere) and EFS (progression or death) were 40, 25 and 44, respectively. First, tumor segmentations were performed: (1,2) 40% and 50% background-corrected SUVmax [1], (3) SUV>2.5, and (4) SUV>3.0 thresholding, all in 3D (Hermes Hybrid Viewer PDR). A total of 51 features were extracted from each patient. These included non-imaging features such as age, sex, and pre/post treatments. They also included number of liver metastases observed in each PET scan. We also extracted 41 quantitative imaging measures, including SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (n=5), the recently introduced class of generalized effective total uptake (gETU) measures [2] which place varying degrees of emphasis on volumetric vs. uptake information (n=10), intensity histogram (n=19) and intensity-volume histogram (n=7) measures [3, 4]. All metrics were standardized according to the framework of the image biomarker standardization initiative (IBSI) [5]. Feature selection was then performed. Metrics with Pearson correlations (r) >0.95 were considered relatively redundant, and a total of 26 features were retained. Finally, univariate and multivariate analyses were performed, which included statistical considerations (to discourage false discovery and overfitting), assessing prediction of OS, PFS and EFS. Specifically, Kaplan-Meier survival analyses were carried out, where the subjects were divided into high-risk and low-risk groups of nearly equal patients, from which the hazard ratios (HR) were computed via Cox proportional hazards regression. Results: When using SUV metrics, the 4 segmentation methods performed fairly similarly, but when performing volumetric analysis, 40% and 50% background-corrected SUVmax thresholding resulted in improved performance especially in PFS (results for 40% method summarized in the rest of analysis). Table 1 reports univariate Cox regression analysis for OS, PFS and EFS (we report on the performance of SUVmax/peak/mean, MTV, TLG, and any metric that were found to be significant). SUV metrics performed relatively poorly for different prediction tasks (SUVmax HR=1.48, 1.21 and 1.16; SUVpeak HR=2.05, 1.93, and 1.64, for OS, PFS and EFS, respectively). By contrast, the number of liver metastases and metabolic tumor volume (MTV) each performed well (with respective HR values of 2.71, 2.61 and 2.42, and 2.62, 1.96 and 2.29, for OS, PFS and EFS; OS results were statistically significant after correction for multiple testing). TLG also resulted in similar performance to MTV. Furthermore, multivariate analysis (Table 2) revealed that inclusion of volumetric and/or heterogeneity features further enhanced prediction; specifically, HR=3.34, 4.42 and 2.69 (p-values=0.0006, 0.0010 and 0.0024) for OS, PFS and EFS, respectively. Conclusions: It was demonstrated that FDG PET volumetric and heterogeneity features, in contrast to commonly invoked SUV parameters, hold significant potential for improved prediction of clinical outcome in patients with colorectal liver metastases.

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