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.

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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
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