高级检索
当前位置: 首页 > 详情页

Predicting Progression of Kidney Injury Based on Elastography Ultrasound and Radiomics Signatures

文献详情

资源类型:
WOS体系:

收录情况: ◇ SCIE ◇ 预警期刊

单位: [1]Shanghai Jiao Tong Univ, Mol Cell Lab Kidney Dis, Dept Nephrol,Renji Hosp,Sch Med, Shanghai Peritoneal Dialysis Res Ctr,Uremia Diag, Shanghai 200127, Peoples R China [2]Shanghai Jiao Tong Univ, Renji Hosp, Sch Med, Dept Ultrasound, Shanghai 200127, Peoples R China [3]China Japan Friendship Hosp, Dept Ultrasound, Beijing 100029, Peoples R China
出处:
ISSN:

关键词: tissue elasticity imaging kidney prognosis regression analysis machine learning

摘要:
Background: Shear wave elastography ultrasound (SWE) is an emerging non-invasive candidate for assessing kidney stiffness. However, its prognostic value regarding kidney injury is unclear. Methods: A prospective cohort was created from kidney biopsy patients in our hospital from May 2019 to June 2020. The primary outcome was the initiation of renal replacement therapy or death, while the secondary outcome was eGFR < 60 mL/min/1.73 m(2). Ultrasound, biochemical, and biopsy examinations were performed on the same day. Radiomics signatures were extracted from the SWE images. Results: In total, 187 patients were included and followed up for 24.57 +/- 5.52 months. The median SWE value of the left kidney cortex (L_C_median) is an independent risk factor for kidney prognosis for stage 3 or over (HR 0.890 (0.796-0.994), p < 0.05). The inclusion of 9 out of 2511 extracted radiomics signatures improved the prognostic performance of the Cox regression models containing the SWE and the traditional index (chi-square test, p < 0.001). The traditional Cox regression model had a c-index of 0.9051 (0.8460-0.9196), which was no worse than the machine learning models, Support Vector Machine (SVM), SurvivalTree, Random survival forest (RSF), Coxboost, and Deepsurv. Conclusions: SWE can predict kidney injury progression with an improved performance by radiomics and Cox regression modeling.

基金:
语种:
WOS:
中科院(CAS)分区:
出版当年[2021]版:
大类 | 4 区 医学
小类 | 3 区 医学:内科
最新[2025]版:
大类 | 3 区 医学
小类 | 3 区 医学:内科
JCR分区:
出版当年[2020]版:
Q2 MEDICINE, GENERAL & INTERNAL
最新[2023]版:
Q1 MEDICINE, GENERAL & INTERNAL

影响因子: 最新[2023版] 最新五年平均[2021-2025] 出版当年[2020版] 出版当年五年平均[2016-2020] 出版前一年[2019版] 出版后一年[2021版]

第一作者:
第一作者单位: [1]Shanghai Jiao Tong Univ, Mol Cell Lab Kidney Dis, Dept Nephrol,Renji Hosp,Sch Med, Shanghai Peritoneal Dialysis Res Ctr,Uremia Diag, Shanghai 200127, Peoples R China
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:1320 今日访问量:0 总访问量:817 更新日期:2025-05-01 建议使用谷歌、火狐浏览器 常见问题

版权所有:重庆聚合科技有限公司 渝ICP备12007440号-3 地址:重庆市两江新区泰山大道西段8号坤恩国际商务中心16层(401121)