CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy?. Issue 2 (April 2016)

• Record Type: Journal Article
• Authors: Rao, Sheng-Xiang; Lambregts, Doenja MJ; Schnerr, Roald S; Beckers, Rianne CJ; Maas, Monique; Albarello, Fabrizio; Riedl, Robert G; Dejong, Cornelis HC; Martens, Milou H; Heijnen, Luc A; Backes, Walter H; Beets, Geerard L; Zeng, Meng-Su; Beets-Tan, Regina GH
• Is Part Of: United European Gastroenterology journal. Volume 4:Issue 2(2016:Apr.)
• Page Start: 257
• Page End: 263
• Publication Date: 2016-04
• Subjects: Colorectal cancer -- liver metastases -- CT texture -- chemotherapy -- response assessment; Gastroenterology -- Periodicals; Periodicals; 616.33005
• DOI: 10.1177/2050640615601603
• Languages: English
• ISSNs: 2050-6406

Abstract

Background: Response Evaluation Criteria In Solid Tumors (RECIST) are known to have limitations in assessing the response of colorectal liver metastases (CRLMs) to chemotherapy. Objective: The objective of this article is to compare CT texture analysis to RECIST-based size measurements and tumor volumetry for response assessment of CRLMs to chemotherapy. Methods: Twenty-one patients with CRLMs underwent CT pre- and post-chemotherapy. Texture parameters mean intensity (M), entropy (E) and uniformity (U) were assessed for the largest metastatic lesion using different filter values (0.0 = no/0.5 = fine/1.5 = medium/2.5 = coarse filtration). Total volume (cm 3 ) of all metastatic lesions and the largest size of one to two lesions (according to RECIST 1.1) were determined. Potential predictive parameters to differentiate good responders ( n = 9; histological TRG 1–2) from poor responders ( n = 12; TRG 3–5) were identified by univariable logistic regression analysis and subsequently tested in multivariable logistic regression analysis. Diagnostic odds ratios were recorded. Results: The best predictive texture parameters were Δuniformity and Δentropy (without filtration). Odds ratios for Δuniformity and Δentropy in the multivariable analyses were 0.95 and 1.34, respectively. Pre- and post-treatment texture parameters, as well as the various size and volume measures, were not significant predictors. Odds ratios for Δsize and Δvolume in the univariable logistic regression were 1.08 and 1.05, respectively. Conclusions: Relative differences in CT texture occurring after treatment hold promise to assess the pathologic response to chemotherapy in patients with CRLMs and may be better predictors of response than changes in lesion size or volume.