Rianne C J Beckers1, Doenja M J Lambregts2, Roald S Schnerr3, Monique Maas4, Sheng-Xiang Rao5, Alfons G H Kessels6, Thomas Thywissen3, Geerard L Beets7, Stefano Trebeschi8, Janneke B Houwers9, Cornelis H Dejong10, Cornelis Verhoef11, Regina G H Beets-Tan8. 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: d.lambregts@nki.nl. 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.
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.
OBJECTIVES: CT texture analysis has shown promise to differentiate colorectal cancerpatients 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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