Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study

Rianne C J Beckers; Doenja M J Lambregts; Roald S Schnerr; Monique Maas; Sheng-Xiang Rao; Alfons G H Kessels; Thomas Thywissen; Geerard L Beets; Stefano Trebeschi; Janneke B Houwers; Cornelis H Dejong; Cornelis Verhoef; Regina G H Beets-Tan

doi:10.1016/j.ejrad.2017.04.019

Whole liver CT texture analysis to predict the development of colorectal liver metastases-A multicentre study

Eur J Radiol. 2017 Jul:92:64-71. doi: 10.1016/j.ejrad.2017.04.019. Epub 2017 Apr 26.

Affiliations

¹ GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands; Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands; Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands.
² Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands. Electronic address: [email protected].
³ Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands.
⁴ Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands.
⁵ Department of Radiology, Zhongshan Hospital, Fudan University,180 Fenglin Road Shangai 200032, China.
⁶ Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University, P.O. Box 6200, 6202 AZ Maastricht, , The Netherlands.
⁷ GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Surgery, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands.
⁸ GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE Amsterdam, The Netherlands.
⁹ GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands.
¹⁰ Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ Maastricht, The Netherlands; NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht University, P.O. Box 616, 6200 MD, The Netherlands; Department of Surgery, RWTH Universitätsklinikum Aachen, Pauwelsstraße 30, 52074 Aachen, Germany.
¹¹ Department of Surgical Oncology, Erasmus MC Cancer Institute, Groene Hilledijk 301, 3075 EA, Rotterdam, The Netherlands.

PMID: 28624022
DOI: 10.1016/j.ejrad.2017.04.019

Abstract

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.

Keywords: Colorectal cancer; Computed tomography; Liver metastases; Metachronous metastases; Occult disease; Texture analysis.

MeSH terms

Adult
Carcinoembryonic Antigen / physiology*
Colonic Neoplasms / diagnostic imaging*
Colonic Neoplasms / pathology
Female
Humans
Liver Neoplasms / diagnostic imaging*
Liver Neoplasms / pathology*
Male
Middle Aged
Retrospective Studies
Tomography, X-Ray Computed / methods*

Substances

Carcinoembryonic Antigen