Literature DB >> 30799634

Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.

En-Ming Cui1, Fan Lin2, Qing Li3, Rong-Gang Li3, Xiang-Meng Chen1, Zhuang-Sheng Liu1, Wan-Sheng Long1.   

Abstract

Entities:  

Keywords:  Renal cell carcinoma; angiomyolipoma; computed tomography (CT); machine learning

Mesh：

Year:  2019        PMID： 30799634     DOI： 10.1177/0284185119830282

Source DB:  PubMed          Journal:  Acta Radiol        ISSN： 0284-1851            Impact factor:   1.990


× No keyword cloud information.
  11 in total

Review 1.  Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.

Authors:  Rodrigo Suarez-Ibarrola; Simon Hein; Gerd Reis; Christian Gratzke; Arkadiusz Miernik
Journal:  World J Urol       Date:  2019-11-05       Impact factor: 4.226

Review 2.  CT-based radiomics for differentiating renal tumours: a systematic review.

Authors:  Abhishta Bhandari; Muhammad Ibrahim; Chinmay Sharma; Rebecca Liong; Sonja Gustafson; Marita Prior
Journal:  Abdom Radiol (NY)       Date:  2020-11-02

3.  Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading.

Authors:  Israa Alnazer; Omar Falou; Pascal Bourdon; Thierry Urruty; Rémy Guillevin; Mohamad Khalil; Ahmad Shahin; Christine Fernandez-Maloigne
Journal:  J Med Imaging (Bellingham)       Date:  2022-09-13

4.  A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images.

Authors:  Kai Wu; Peng Wu; Kai Yang; Zhe Li; Sijia Kong; Lu Yu; Enpu Zhang; Hanlin Liu; Qing Guo; Song Wu
Journal:  Eur Radiol       Date:  2021-11-20       Impact factor: 7.034

Review 5.  Radiomics to better characterize small renal masses.

Authors:  Teele Kuusk; Joana B Neves; Maxine Tran; Axel Bex
Journal:  World J Urol       Date:  2021-01-26       Impact factor: 4.226

6.  Clinical utility of contrast-enhanced ultrasonography in the diagnosis of benign and malignant small renal masses among Asian population.

Authors:  Lin Shen; Yanyan Li; Na Li; Yajie Zhao; Qin Zhou; Zhanzhan Li
Journal:  Cancer Med       Date:  2019-10-23       Impact factor: 4.452

7.  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

8.  A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma.

Authors:  Yanqing Ma; Weijun Ma; Xiren Xu; Zheng Guan; Peipei Pang
Journal:  Sci Rep       Date:  2021-02-25       Impact factor: 4.379

9.  Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis.

Authors:  Wei Yu; Gao Liang; Lichuan Zeng; Yang Yang; Yinghua Wu
Journal:  BMJ Open       Date:  2021-12-22       Impact factor: 2.692

10.  Change and consistency in Acta Radiologica over 100 years.

Authors:  Mats Geijer; Henrik S Thomsen
Journal:  Acta Radiol       Date:  2021-10-22       Impact factor: 1.990
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.