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OBJECTIVE. The purpose of this article is to evaluate interobserver, intraobserver, and interplatform variability and compare the previously established association between texture metrics and tumor histologic subtype using three commercially available CT texture analysis (CTTA) software platforms on the same dataset of large (> 7 cm) renal cell carcinomas (RCCs). MATERIALS AND METHODS. CT-based texture analysis was performed on contrast-enhanced MDCT images of large (> 7 cm) untreated RCCs in 124 patients (median age, 62 years; 82 men and 42 women) using three different software platforms. Using this previously studied cohort, texture features were compared across platforms. Features were correlated with histologic subtype, and strength of association was compared between platforms. Single-slice and volumetric measures from one platform were compared. Values for interobserver and intraobserver variability on a tumor subset ( n = 30) were assessed across platforms. RESULTS. Metrics including mean gray-level intensity, SD, and volume correlated fairly well across platforms (concordance correlation coefficient [CCC], 0.66-0.99; mean relative difference [MRD], 0.17-5.97%). Entropy showed high variability (CCC, 0.04; MRD, 44.5%). Mean, SD, mean of positive pixels (MPP), and entropy were associated with clear cell histologic subtype on almost all platforms ( p < .05). Mean, SD, entropy, and MPP were highly reproducible on most platforms on both interobserver and intraobserver analysis. CONCLUSION. Select texture metrics were reproducible across platforms and readers, but other metrics were widely variable. If clinical models are developed that use CTTA for medical decision making, these differences in reproducibility of some features across platforms need to be considered, and standardization is critical for more widespread adaptation and implementation.

Objective: The purpose of the present study is to determine whether CT texture features of newly diagnosed primary renal cell carcinomas (RCCs) correlate with pathologic features and oncologic outcomes., Materials and Methods: CT texture analysis was performed on large (> 7 cm; mean size, 9.9 cm) untreated RCCs in 157 patients (52 women and 105 men; mean age, 60.3 years). Measures of tumor heterogeneity, including entropy, kurtosis, skewness, mean, mean of positive pixels, and SD of pixel distribution histogram were derived from multiphasic CT using various filter settings: unfiltered (spatial scaling factor, 0), fine (spatial scaling factor, 2), medium (spatial scaling factor, 3-4), or coarse (spatial scaling factor, 5-6). Texture values were correlated with histologic subtype, nuclear grade, pathologic stage, and clinical outcome., Results: When a coarse filter setting (spatial scaling factor, 6) was used, entropy on portal venous phase CT images was positively associated with clear cell histologic findings (odds ratio [OR], 134; 95% CI, 16-1110; p < 0.001) and was negatively associated with non-clear cell subtype findings (papillary spatial scale factor, 6; OR, 0.016; 95% CI, 0.002-0.132; p < 0.001). ROC curve analysis for entropy (on portal venous phase images obtained with a spatial scaling factor of 6) revealed an AUC of 0.943 (95% CI, 0.892-0.993) for clear cell histologic findings, with similar values noted for non-clear cell histologic findings. The mean of positive pixels and the SD of the pixel distribution histogram were statistically significantly associated with histologic cell type in a similar fashion. Entropy, the SD of the pixel distribution histogram, and the mean of positive pixels were associated with nuclear grade, most prominently when fine or medium texture filters were used (p < 0.05). There was a statistically significant association of texture features noted on unenhanced CT, including the SD of the pixel distribution histogram, the mean of positive pixels, and entropy, with the time to disease recurrence and death due to disease (e.g., for entropy noted on unenhanced CT images obtained with a spatial scaling factor of 6, the hazard ratio was 3.49 [95% CI, 1.55-7.84]; p = 0.002)., Conclusion: CT texture features (in particular, entropy, the mean of positive pixels, and the SD of the pixel distribution histogram) are associated with tumor histologic findings, nuclear grade, and outcome measures. The contrast phase does seem to affect heterogeneity measures.