Computational protocol: Computed tomography texture analysis to facilitate therapeutic decision making in hepatocellular carcinoma

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Protocol publication

[…] For each pre-treatment examination, 1.25-mm axial images obtained at the portal venous phase through the largest cross-sectional area of the tumor were selected and transferred to two personal computers for texture analysis. The process of texture analysis comprised three steps: (1) image filtration, (2) wavelet analysis and (3) feature extraction. The first two steps were performed using MATLAB 2014a software (MathWorks Inc., Natick, MA).(1) Image filtration: Laplacian of Gaussian (LoG) spatial band-pass filters were used to reduce the sensitivity to noise. Filter width and sigma (σ) are the two parameters that characterize LoG filter weighting. Three σ values (0, 1.0, and 1.5) and a single filter-width of σ*5 pixels were used (). Pixels with attenuation of less than −50 HU were removed. The filtration process produced a series of images displaying textual features at different filters.(2) Wavelet analysis: The use of the wavelet transform for texture analysis was first proposed by Mallat []. This transform provides a robust methodology for texture analysis in different scales. Initially, it decomposes each image and receives its texture by using a series of elemental functions called wavelet and scaling, where “s” governs the scaling and “u” the translation, as follows: φs,u(x)=1sφ(x−us)(s∈R+u∈R)As a result, the Haar wavelet transform decomposes each original image into nine images with different scales, called trends and fluctuations: Wf(s,u)=∫Rf(x)1sφ(x−us)dxThe former are averaged versions of the original image, and the latter contains the high frequencies. Each image is decomposed into 1, 2, or 3 levels and reconstructed in three directions (diagonal, horizontal and vertical).(3) Texture feature extraction: Two radiologists (Readers 1 and 2, with 5 and 4 years of experience in abdominal CT interpretation, respectively) independently performed textural feature extraction and quantification using ImageJ software (National Institutes of Health, Bethesda, MD). For each reader, a user-defined irregular ROI was drawn manually around the largest cross-sectional tumor outline and copied to the nine derived texture feature maps. Subsequently, the values of the texture features were measured and saved for further analysis. [...] The Shapiro-Wilk test was applied to assess normality. Differences in patient demographics and characteristics for those undergoing LR or TACE were tested using independent-sample t-tests, Mann-Whitney U tests and Chi-square tests. Inter-observer agreement on textual features was evaluated using intraclass correlation coefficients (ICCs) [].Patient demographics and subjective imaging features were included for adjustment in the analyses. Univariate Cox regression was used as a preliminary screening of candidate variables. Variables of statistical significance in the univariate analysis (P < 0.10) were used as input variables for the subsequent multivariate Cox regression models (Forward: LR method). Textural features at each filter were tested in separate models to assess the independent effects of the CT texture of the primary tumor on OS. Thus, six multivariate models were created (one per group per filter; LR and TACE groups, and three filters). Afterwards, the median values of the independent texture parameters were used to separate patients in the LR and TACE groups for subsequent Kaplan-Meier analysis.To explore the potential role of texture features in deciding between LR and TACE, patients were first divided into two subgroups according to the identified prognostic markers in the LR and TACE groups. Cox regression for all patients was performed to determine whether subgrouping was an independent factor for OS and TTP. Next, one way-ANOVA or Kruskal-Wallis H was used to compare the identified textural parameters among the subgroups. Post hoc multiple comparisons were performed using Bonferroni's correction or Dunnett's T3 test.The thresholds of the identified factors in Cox regression models were also determined using standard receiver operating characteristic curves. contain detailed discussions regarding this approach.All statistical analyses were performed with SPSS 20.0 (IBM SPSS Statistics, Armonk, NY). A two-tailed P value of less than 0.05 was considered statistically significant. […]

Pipeline specifications

Software tools ImageJ, SPSS
Applications Miscellaneous, Microscopic phenotype analysis
Organisms Homo sapiens
Diseases Carcinoma, Hepatocellular, Neoplasms