Dataset Information

Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.

ABSTRACT: Identification of histologic grading of urothelial carcinoma still depends on histopathologic examination. As an emerging and promising imaging technology, radiomic texture analysis is a noninvasive technique and has been studied to differentiate various tumors. This study explored the value of computed tomography (CT) texture analysis for the differentiation of low-grade urothelial carcinoma (LGUC), high-grade urothelial carcinoma (HGUC), and their invasive properties.Radiologic data were analyzed retrospectively for 94 patients with pathologically proven urothelial carcinomas from November 2016 to April 2019. Pathologic examination demonstrated that tumors were: high grade in 43 cases, and low grade in 51 cases; and nonmuscle invasive (NMI) in 37 cases, and muscle invasive (MI) in 37 cases. Maximum tumor diameters on CT scan were manually outlined as regions of interest and 78 texture features were extracted automatically. Three-phasic CT images were used to measure texture parameters, which were compared with postoperative pathologic grading and invasive results. The independent sample t test or Mann-Whitney U test was used to compare differences in parameters. Receiver-operating characteristic curves for statistically significant parameters were used to confirm efficacy.Of the 78 features extracted from each phase of CT images, 26 (33%), 20 (26%), and 22 (28%) texture parameters were significant (P?

SUBMITTER: Wang Z

PROVIDER: S-EPMC7440185 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.

Wang Zihua Z He Yufang Y Wang Nianhua N Zhang Ting T Wu Hongzhen H Jiang Xinqing X Mo Lei L

Medicine 20200501 18

Identification of histologic grading of urothelial carcinoma still depends on histopathologic examination. As an emerging and promising imaging technology, radiomic texture analysis is a noninvasive technique and has been studied to differentiate various tumors. This study explored the value of computed tomography (CT) texture analysis for the differentiation of low-grade urothelial carcinoma (LGUC), high-grade urothelial carcinoma (HGUC), and their invasive properties.Radiologic data were analy ...[more]

PMID: 32358396

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Project description:PurposeTumorous texture is a marker for tumor tissue inhomogeneity. Based on this assumption, this study aims to evaluate the value of computed tomography texture analysis for imaging-based prediction of perioperative complications during laparoscopic partial tumor nephrectomy.MethodsA total of 106 patients with histologically confirmed renal cell carcinoma and pre-operative CT were included and volumetric texture analysis of the tumors was performed by two readers. Texture analysis parameter ratios and differences were calculated using the kidney parenchyma as reference ("reference-corrected"). Regression analysis was performed, regarding the value of the texture analysis parameters, for assessment of the tumor nuclear grade and the prediction of peri- and postoperative complications and approximated blood loss. Moreover, the inter-rater agreement in terms of the intra-class correlation coefficient (ICC) was calculated.ResultsRegarding the reference-corrected values, the predictive value of texture analysis parameters for severe perioperative complications was highest for the standard deviation of the mean attenuation (Area under curve/AUC, .615; sensitivity, 93.8%, specificity, 30.0%), followed by the uniformity (AUC, .599; sensitivity, 62.5%, specificity, 60.0%), and the uniformity of distribution of positive pixels (AUC, .597; sensitivity, 62.5%; specificity, 61.1%). Regarding the blood loss, the uniformity of positive pixel values (UPP; AUC, 0.638), uniformity (AUC, 0.635), and entropy (AUC, 0.633) yielded the best predictive values, whilst the tumor grade was a weaker predictor (AUC, 0.574). The applied texture analysis parameters did not correlate with the time of surgery or the warm ischemic time. All measured parameters were better predictors for complications than the tumor diameter alone. The inter-rater agreement was almost perfect (ICC, .982).ConclusionCT and CT texture analysis parameters are valuable for prediction of perioperative outcome before laparoscopic partial nephrectomy in patients with renal cell carcinoma.

Project description:Background: Although inflammatory breast cancer (IBC) has poor overall survival (OS), there is little information about using imaging features for predicting the prognosis. Computed tomography (CT)-based texture analysis, a non-invasive technique to quantify tumor heterogeneity, could be a potentially useful imaging biomarker. The aim of the article was to investigate the usefulness of chest CT-based texture analysis to predict OS in IBC patients. Methods: Of the 3,130 patients with primary breast cancers between 2006 and 2016, 104 patients (3.3%) with IBC were identified. Among them, 98 patients who underwent pre-treatment contrast-enhanced chest CT scans, got treatment in our institution, and had a follow-up period of more than 2 years were finally included for CT-based texture analysis. Texture analysis was performed on CT images of 98 patients, using commercially available software by two breast radiologists. Histogram-based textural features, such as quantification of variation in CT attenuation (mean, standard deviation, mean of positive pixels [MPP], entropy, skewness, and kurtosis), were recorded. To dichotomize textural features for survival analysis, receiver operating characteristic curve analysis was used to determine cutoff points. Clinicopathologic variables, such as age, node stage, metastasis stage at the time of diagnosis, hormonal receptor positivity, human epidermal growth factor receptor 2 positivity, and molecular subtype, were assessed. A Cox proportional hazards model was used to determine the association of textural features and clinicopathologic variables with OS. Results: During a mean follow-up period of 47.9 months, 41 of 98 patients (41.8%) died, with a median OS of 20.0 months. The textural features of lower mean attenuation, standard deviation, MPP, and entropy on CT images were significantly associated with worse OS, as was the M1 stage among clinicopathologic variables (all P-values < 0.05). In multivariate analysis, lower mean attenuation (hazard ratio [HR], 3.26; P = 0.003), lower MPP (HR, 3.03; P = 0.002), and lower entropy (HR, 2.70; P = 0.009) on chest CT images were significant factors independent from the M1 stage for predicting worse OS. Conclusions: Lower mean attenuation, MPP, and entropy on chest CT images predicted worse OS in patients with IBC, suggesting that CT-based texture analysis provides additional predictors for OS.

Dataset Information

Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.

Publications

Clinical value of texture analysis in differentiation of urothelial carcinoma based on multiphase computed tomography images.

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