Literature DB >> 31668402

Usefulness of CT texture analysis in differentiating benign and malignant renal tumours.

Y Deng1, E Soule2, E Cui3, A Samuel4, S Shah4, C Lall2, C Sundaram5, K Sandrasegaran6.   

Abstract

AIM: To elucidate visually imperceptible differences between benign and malignant renal tumours using computed tomography texture analysis (CTTA) using filtration histogram based parameters.
MATERIALS AND METHODS: A retrospective study was performed by texture analysis of pretreatment contrast-enhanced CT examinations in 354 histopathologically confirmed renal cell carcinomas (RCCs) and 147 benign renal tumours. A region-of-interest was drawn encompassing the largest cross-section of the tumour on venous phase axial CT. CTTA features of entropy, kurtosis, mean positive pixel density, and skewness at different spatial filters were calculated and compared in an attempt to differentiate benign lesions from malignancy.
RESULTS: Entropy with fine spatial filter was significantly higher in RCC than benign renal tumours (p=0.022). Entropy with fine and medium filters was higher in RCC than lipid-poor angiomyolipoma (p=0.050 and 0.052, respectively). Entropy >5.62 had high specificity of 85.7%, but low sensitivity of 31.3%, respectively, for predicting RCC.
CONCLUSIONS: Differences in entropy were helpful in differentiating RCC from lipid-poor angiomyolipoma, and chromophobe RCC from oncocytoma. This technique may be useful to differentiate lesions that appear equivocal on visual assessment or alter management in poor surgical candidates.
Copyright © 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2019        PMID: 31668402     DOI: 10.1016/j.crad.2019.09.131

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  6 in total

1.  Pre-treatment MRI predictor of high-grade malignant parotid gland cancer.

Authors:  Akira Baba; Hisashi Kessoku; Taisuke Akutsu; Eiji Shimura; Satoshi Matsushima; Ryo Kurokawa; Yoshiaki Ota; Takayuki Suzuki; Yuki Kawasumi; Hideomi Yamauchi; Koshi Ikeda; Hiroya Ojiri
Journal:  Oral Radiol       Date:  2021-01-02       Impact factor: 1.852

2.  MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma.

Authors:  Abdul Razik; Ankur Goyal; Raju Sharma; Devasenathipathy Kandasamy; Amlesh Seth; Prasenjit Das; Balaji Ganeshan
Journal:  Br J Radiol       Date:  2020-08-26       Impact factor: 3.039

3.  Prediction of the World Health Organization Grade of rectal neuroendocrine tumors based on CT histogram analysis.

Authors:  Ping Liang; Chuou Xu; Fangqin Tan; Shichao Li; Mingzhen Chen; Daoyu Hu; Ihab Kamel; Yaqi Duan; Zhen Li
Journal:  Cancer Med       Date:  2020-12-01       Impact factor: 4.452

4.  Review of Value of CT Texture Analysis and Machine Learning in Differentiating Fat-Poor Renal Angiomyolipoma from Renal Cell Carcinoma.

Authors:  Yuhan Zhang; Xu Li; Yang Lv; Xinquan Gu
Journal:  Tomography       Date:  2020-12

5.  Radiological differences in computed tomography findings and texture analysis between cystic lymph node metastases of human papillomavirus-positive oropharyngeal cancer and second branchial cysts.

Authors:  Akira Baba; Hisashi Kessoku; Ryo Kurokawa; Hideomi Yamauchi; Taisuke Akutsu; Eiji Shimura; Koshi Ikeda; Hiroya Ojiri
Journal:  Pol J Radiol       Date:  2021-03-25

6.  A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors.

Authors:  Mohamed Shehata; Ahmed Alksas; Rasha T Abouelkheir; Ahmed Elmahdy; Ahmed Shaffie; Ahmed Soliman; Mohammed Ghazal; Hadil Abu Khalifeh; Reem Salim; Ahmed Abdel Khalek Abdel Razek; Norah Saleh Alghamdi; Ayman El-Baz
Journal:  Sensors (Basel)       Date:  2021-07-20       Impact factor: 3.576

  6 in total

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