Objectives CT texture analysis has shown promise to differentiate colorectal cancer patients with/without hepatic metastases. Aim To investigate whether whole-liver CT texture analysis can also predict the development of colorectal liver metastases. Material and methods Retrospective multicentre study (n = 165). Three subgroups were assessed: patients [A] without metastases (n = 57), [B] with synchronous metastases (n = 54) and [C] who developed metastases within ≤24 months (n = 54). Whole-liver texture analysis was performed on primary staging CT. Mean grey-level intensity, entropy and uniformity were derived with different filters (σ0.5–2.5). Univariable logistic regression (group A vs. B) identified potentially predictive parameters, which were tested in multivariable analyses to predict development of metastases (group A vs. C), including subgroup analyses for early (≤6 months), intermediate (7–12 months) and late (13–24 months) metastases. Results Univariable analysis identified uniformity (σ0.5), sex, tumour site, nodal stage and carcinoembryonic antigen as potential predictors. Uniformity remained a significant predictor in multivariable analysis to predict early metastases (OR 0.56). None of the parameters could predict intermediate/late metastases. Conclusions Whole-liver CT-texture analysis has potential to predict patients at risk of developing early liver metastases ≤6 months, but is not robust enough to identify patients at risk of developing metastases at later stage.

Additional Metadata
Keywords Colorectal cancer, Computed tomography, Liver metastases, Metachronous metastases, Occult disease, Texture analysis
Persistent URL dx.doi.org/10.1016/j.ejrad.2017.04.019, hdl.handle.net/1765/99547
Journal European Journal of Radiology
Citation
Beckers, R.C.J. (Rianne C.J.), Lambregts, D.M.J. (Doenja M.J.), Schnerr, R.S. (Roald S.), Maas, M. (Monique), Rao, S.-X. (Sheng-Xiang), Kessels, A, … Beets-Tan, R.G. (2017). Whole liver CT texture analysis to predict the development of colorectal liver metastases—A multicentre study. European Journal of Radiology, 92, 64–71. doi:10.1016/j.ejrad.2017.04.019