Literature DB >> 33633296

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

Yanqing Ma1, Weijun Ma2, Xiren Xu3, Zheng Guan3, Peipei Pang4.   

Abstract

This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann-Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, VUP, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923-0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910-0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.

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Year:  2021        PMID: 33633296      PMCID: PMC7907210          DOI: 10.1038/s41598-021-84244-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  27 in total

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Authors:  Robert S Lim; Trevor A Flood; Matthew D F McInnes; Luke T Lavallee; Nicola Schieda
Journal:  Eur Radiol       Date:  2017-08-04       Impact factor: 5.315

2.  Enhancement characteristics of papillary renal neoplasms revealed on triphasic helical CT of the kidneys.

Authors:  Brian R Herts; Deirdre M Coll; Andrew C Novick; Nancy Obuchowski; Grant Linnell; Susan L Wirth; Mark E Baker
Journal:  AJR Am J Roentgenol       Date:  2002-02       Impact factor: 3.959

3.  Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images.

Authors:  Lifen Yan; Zaiyi Liu; Guangyi Wang; Yanqi Huang; Yubao Liu; Yuanxin Yu; Changhong Liang
Journal:  Acad Radiol       Date:  2015-05-29       Impact factor: 3.173

4.  Angiomyolipoma with minimal fat: can it be differentiated from clear cell renal cell carcinoma by using standard MR techniques?

Authors:  Nicole Hindman; Long Ngo; Elizabeth M Genega; Jonathan Melamed; Jesse Wei; Julia M Braza; Neil M Rofsky; Ivan Pedrosa
Journal:  Radiology       Date:  2012-09-25       Impact factor: 11.105

5.  Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.

Authors:  Jun Shu; Yongqiang Tang; Jingjing Cui; Ruwu Yang; Xiaoli Meng; Zhengting Cai; Jingsong Zhang; Wanni Xu; Didi Wen; Hong Yin
Journal:  Eur J Radiol       Date:  2018-10-05       Impact factor: 3.528

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

Authors:  En-Ming Cui; Fan Lin; Qing Li; Rong-Gang Li; Xiang-Meng Chen; Zhuang-Sheng Liu; Wan-Sheng Long
Journal:  Acta Radiol       Date:  2019-02-24       Impact factor: 1.990

7.  Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT.

Authors:  Jonathan R Young; Daniel Margolis; Steven Sauk; Allan J Pantuck; James Sayre; Steven S Raman
Journal:  Radiology       Date:  2013-02-04       Impact factor: 11.105

8.  Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

Authors:  Hansang Lee; Helen Hong; Junmo Kim; Dae Chul Jung
Journal:  Med Phys       Date:  2018-03-25       Impact factor: 4.071

9.  Quantitative Contour Analysis as an Image-based Discriminator Between Benign and Malignant Renal Tumors.

Authors:  Felix Y Yap; Darryl H Hwang; Steven Y Cen; Bino A Varghese; Bhushan Desai; Brian D Quinn; Megha Nayyar Gupta; Nieroshan Rajarubendra; Mihir M Desai; Manju Aron; Gangning Liang; Monish Aron; Inderbir S Gill; Vinay A Duddalwar
Journal:  Urology       Date:  2018-01-02       Impact factor: 2.633

10.  A Deep Learning-Based Radiomics Model for Differentiating Benign and Malignant Renal Tumors.

Authors:  Leilei Zhou; Zuoheng Zhang; Yu-Chen Chen; Zhen-Yu Zhao; Xin-Dao Yin; Hong-Bing Jiang
Journal:  Transl Oncol       Date:  2018-12-17       Impact factor: 4.243

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  3 in total

1.  Differentiating renal epithelioid angiomyolipoma from clear cell carcinoma: using a radiomics model combined with CT imaging characteristics.

Authors:  Taek Min Kim; Hyungwoo Ahn; Hyo Jeong Lee; Min Gwan Kim; Jeong Yeon Cho; Sung Il Hwang; Sang Youn Kim
Journal:  Abdom Radiol (NY)       Date:  2022-06-13

2.  MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.

Authors:  Lian Jian; Yan Liu; Yu Xie; Shusuan Jiang; Mingji Ye; Huashan Lin
Journal:  Front Oncol       Date:  2022-05-26       Impact factor: 5.738

3.  A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC.

Authors:  Yankun Gao; Xiaoying Zhao; Xia Wang; Chao Zhu; Cuiping Li; Jianying Li; Xingwang Wu
Journal:  J Oncol       Date:  2022-08-26       Impact factor: 4.501

  3 in total

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