CT texture analysis in bladder carcinoma: histologic grade characterization

Zhenhao Liu; Jiayuan Shi; Haiyi Wang; Huiyi YE; Zhanbo Wang; Tie Yang; Xin MA; Xu Bai

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CT texture analysis in bladder carcinoma: histologic grade characterization

VernacularTitle: 基于CT图像的纹理分析在膀胱癌不同病理级别鉴别中的价值
Author: Zhenhao LIU ¹ ; Jiayuan SHI ² ; Haiyi WANG ³ ; Huiyi YE ³ ; Zhanbo WANG ⁴ ; Tie YANG ³ ; Xin MA ⁵ ; Xu BAI ³
Author Information

1. Department of Radiology, Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, Changzhi 046000, China
2. Department of Radiology, Shaanxi Sengong Hospital, Xi′an 710300, China
3. Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
4. Department of Pathology, Chinese PLA General Hospital, Beijing 100853, China
5. Department of Urology, Chinese PLA General Hospital, Beijing 100853, China
Publication Type:Clinical Trail
Keywords: Urinary bladder neoplasms; Tomography, spiral computed; Diagnosis, differential; Histological grading; Quantitative texture analysis
From: Chinese Journal of Oncology 2018;40(5):379-383
CountryChina
Language:Chinese
Abstract: Objective:To explore the value of CT texture analysis (CTTA) in differentiating the pathological grade of urothelial carcinoma of the bladder (UCB).
Methods:A total of 53 lesions from 43 patients with bladder cancer confirmed by postoperative pathology were retrospectively analyzed, including 27 cases of high-grade urothelial carcinoma (HGUC) and 26 cases of low-grade urothelial carcinoma (LGUC). All the patients took pelvic CT and enhanced scanning in the same CT scanner with same scanning parameters. Lesions on both plain and enhanced CT images were delineated on software by two radiologists to extract the corresponding volumes of interest (VOI) and then 92 parameters based on feature classes were generated. The average values of two radiologists were obtained. The difference parameters between HGUC group and LGUC group were screened by nonparametric test, and the receiver operating characteristic (ROC) was drawn. The corresponding optimal thresholds were determined and diagnostic effect was assessed.
Results:Nine difference texture parameters between HGUC group and LGUC group were selected, including 5 parameters on unenhanced images, namely, skewness, root mean squared, cluster shade, zone percentage and large area high gray level emphasis. There were 4 parameters on enhanced images, namely, skewness, kurtosis, cluster shade and zone percentage. The largest area under curve of 0.840±0.058 (95% CI 0.726-0.955) was obtained from skewness generated by VOI of unenhanced images. The cut-off value of skewness was 0.186 5, which permitted the diagnosis of HGUC with sensitivity of 92.59%, specificity of 73.08%, positive predictive value of 78.13%, negative predictive value of 90.48% and accuracy of 83.02%.
Conclusion:CTTA can effectively distinguish between LGUC and HGUC. Skewness from unenhanced CT images had the optimal diagnostic performance.