Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker.

By V. Goh, B. Ganeshan, P. Nathan, J. Juttla, Anup Vinayan, K. Miles. 2011

356 citations recorded by the source corpus.

PURPOSE To assess changes in tumor computed tomographic (CT) texture after two cycles of treatment with tyrosine kinase inhibitors (TKIs) and to determine if tumor texture correlates with measured time to progression in patients with metastatic renal cell cancer who received TKIs. MATERIALS AND METHODS A waiver of institutional review board approval was obtained for this retrospective analysis. Contrast material-enhanced CT texture parameters were assessed in 39 patients with metastatic renal cell cancer who received a TKI. A total of 87 metastases were analyzed at baseline and after two treatment cycles. Changes in tumor entropy and uniformity were derived with a software algorithm that selectively filters and extracts texture at different scales (fine to coarse detail: 1.0-2.5) and were recorded. Response assessment was also obtained by using response evaluation criteria in solid tumors (RECIST), as well as Choi and modified Choi criteria. The correlation of texture parameters and standard criteria with measured time to progression was assessed by using Kaplan-Meier analysis and a Cox regression model. Statistical significance was set at 5%. RESULTS Tumor entropy decreased by 3%-45% and uniformity increased by 5%-21% for the different scale values after administration of a TKI. With a threshold change of -2% or less for uniformity at a coarse scale value of 2.5, Kaplan-Meier curves of the proportion of patients without disease progression were significantly different and better than those for standard response assessment (P = .008 vs P = .267, P = .053, and P = .042 for RECIST, Choi, and modified Choi criteria, respectively). Cox regression analysis showed that texture uniformity was an independent predictor of time to progression (odds ratio, 4.02; 95% confidence interval: 1.52, 10.65; P = .005). CONCLUSION CT texture analysis reflecting tumor heterogeneity is an independent factor associated with time to progression and has potential as a predictive imaging biomarker of response of metastatic renal cancer to targeted therapy.

Subfields: Biomedical Engineering, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

K. Miles
Koichiro Yasaka
B. Ganeshan
Fergus Davnall
Hiroyuki Akai
Akira Kunimatsu
Shigeru Kiryu
C. Yip

Citations in the corpus

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, unknown): “2011 [96] T: 39 First-order statistics (Entropy”
Deep learning with convolutional neural network in radiology (2018, background): “Radiomics studies have successfully cor‐ related information extracted from radiological images with clinically important information in estimating subtypes of tumors [8–10], estimating prognosis of patients with tumors [11–15], evaluating the risk of recurrence of tumors [16], and evaluating treatm”
Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? (2012, background): “…carcinoma Texture analysis Percentage change in coarse texture uniformity of≤−2 % after 2 cycles of TKI correlated with shorter time to progression Goh et al., 2011 [41] Prognosis assessment Liver texture in patients with colorectal cancer but no known metastases Texture analysis Coarse…”