Literature DB >> 30615554

Radiomics Analysis of Gadoxetic Acid-enhanced MRI for Staging Liver Fibrosis.

Hyo Jung Park1, Seung Soo Lee1, Bumwoo Park1, Jessica Yun1, Yu Sub Sung1, Woo Hyun Shim1, Yong Moon Shin1, So Yeon Kim1, So Jung Lee1, Moon-Gyu Lee1.   

Abstract

Purpose To develop and validate a radiomics-based model for staging liver fibrosis by using gadoxetic acid-enhanced hepatobiliary phase MRI. Materials and Methods In this retrospective study, 436 patients (mean age, 51 years; age range, 18-86 years; 319 men [mean age, 51 years; age range, 18-86 years]; 117 women [mean age, 50 years; age range, 18-79 years]) with pathologic analysis-proven liver fibrosis who underwent gadoxetic acid-enhanced MRI from June 2015 to December 2016 were randomized in a three-to-one ratio into development (n = 329) and test (n = 107) cohorts, respectively. In the development cohort, a model was developed to calculate radiomics fibrosis index (RFI) by using logistic regression with elastic net regularization to differentiate stage F3-F4 from stage F0-F2. Optimal RFI cutoffs to diagnose clinically significant fibrosis (stage F2-F4), advanced fibrosis (stage F3-F4), and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. In the test cohort, the diagnostic performance of RFI was compared with that of normalized liver enhancement, aspartate transaminase-to-platelet ratio index (APRI), and fibrosis-4 index by using the Obuchowski index. Results In the test cohort, RFI (Obuchowski index, 0.86) significantly outperformed normalized liver enhancement (Obuchowski index, 0.77; P < .03), APRI (Obuchowski index, 0.60; P < .001), and fibrosis-4 index (Obuchowski index, 0.62; P < .001) for staging liver fibrosis. By using the cutoffs, RFI had sensitivities and specificities as follows: 81% (95% confidence interval: 71%, 89%) and 78% (95% confidence interval: 63%, 89%) for diagnosing stage F2-F4, respectively; 79% (95% confidence interval: 67%, 88%) and 82% (95% confidence interval: 69%, 91%), respectively, for diagnosing stage F3-F4; and 92% (95% confidence interval: 79%, 98%) and 75% (95% confidence interval: 62%, 83%), respectively, for diagnosing stage F4. Conclusion Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 30615554     DOI: 10.1148/radiol.2018181197

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  31 in total

1.  Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer.

Authors:  Shuai Ma; Huihui Xie; Huihui Wang; Jiejin Yang; Chao Han; Xiaoying Wang; Xiaodong Zhang
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

2.  Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography.

Authors:  ByukGyung Choi; In Young Choi; Sang Hoon Cha; Suk Keu Yeom; Hwan Hoon Chung; Seung Hwa Lee; Jaehyung Cha; Ju-Han Lee
Journal:  Jpn J Radiol       Date:  2020-07-14       Impact factor: 2.374

3.  Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma.

Authors:  Yun Bian; Yan Fang Liu; Hui Jiang; Yinghao Meng; Fang Liu; Kai Cao; Hao Zhang; Xu Fang; Jing Li; Jieyu Yu; Xiaochen Feng; Qi Li; Li Wang; Jianping Lu; Chengwei Shao
Journal:  Abdom Radiol (NY)       Date:  2021-06-29

4.  Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features.

Authors:  Enming Cui; Wansheng Long; Juanhua Wu; Qing Li; Changyi Ma; Yi Lei; Fan Lin
Journal:  Abdom Radiol (NY)       Date:  2021-03-22

5.  Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection.

Authors:  Zhen Zhang; Jie Chen; Hanyu Jiang; Yi Wei; Xin Zhang; Likun Cao; Ting Duan; Zheng Ye; Shan Yao; Xuelin Pan; Bin Song
Journal:  Ann Transl Med       Date:  2020-07

Review 6.  Radiomics: a primer on high-throughput image phenotyping.

Authors:  Kyle J Lafata; Yuqi Wang; Brandon Konkel; Fang-Fang Yin; Mustafa R Bashir
Journal:  Abdom Radiol (NY)       Date:  2021-08-25

7.  Radiomics nomograms based on R2* mapping and clinical biomarkers for staging of liver fibrosis in patients with chronic hepatitis B: a single-center retrospective study.

Authors:  Die Zhang; Yi Cao; Yi Sun; Xia Zhao; Cheng Peng; Jing Zhao; Xiaohui Bao; Lifei Wang; Chen Zhang
Journal:  Eur Radiol       Date:  2022-09-23       Impact factor: 7.034

8.  CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma.

Authors:  Yinghao Meng; Jieyu Yu; Mengmeng Zhu; Jian Zhou; Na Li; Fang Liu; Hao Zhang; Xu Fang; Jing Li; Xiaocheng Feng; Li Wang; Hui Jiang; Jianping Lu; Chengwei Shao; Yun Bian
Journal:  Abdom Radiol (NY)       Date:  2022-04-22

9.  A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI.

Authors:  Aboelyazid Elkilany; Uli Fehrenbach; Timo Alexander Auer; Tobias Müller; Wenzel Schöning; Bernd Hamm; Dominik Geisel
Journal:  Sci Rep       Date:  2021-05-24       Impact factor: 4.379

10.  Development and Validation of a Novel Computed-Tomography Enterography Radiomic Approach for Characterization of Intestinal Fibrosis in Crohn's Disease.

Authors:  Xuehua Li; Dong Liang; Jixin Meng; Jie Zhou; Zhao Chen; Siyun Huang; Baolan Lu; Yun Qiu; Mark E Baker; Ziyin Ye; Qinghua Cao; Mingyu Wang; Chenglang Yuan; Zhihui Chen; Shengyu Feng; Yuxuan Zhang; Marietta Iacucci; Subrata Ghosh; Florian Rieder; Canhui Sun; Minhu Chen; Ziping Li; Ren Mao; Bingsheng Huang; Shi-Ting Feng
Journal:  Gastroenterology       Date:  2021-02-17       Impact factor: 33.883

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