非对比mri放射组学联合临床生物标志物在肝纤维化分层中的应用。

IF 2.7 4区 医学 Q2 Medicine
Canadian Journal of Gastroenterology and Hepatology Pub Date : 2022-06-21 eCollection Date: 2022-01-01 DOI:10.1155/2022/2249447
Ru Zhao, Hong Zhao, Ya-Qiong Ge, Fang-Fang Zhou, Long-Sheng Wang, Hong-Zhen Yu, Xi-Jun Gong
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引用次数: 2

摘要

目的:开发并验证基于异相T1W图像纹理特征和临床生物标志物预测肝纤维化的放射组学图。材料和方法:纳入临床诊断为慢性肝纤维化并行肝活检和非对比MRI检查的患者。结果:纳入最具鉴别特征的放射组学模型的ROC分析显示,训练组和试验组的auc分别为0.80和0.78。建立RADscore与纤维化指数的联合模型。训练组和试验组的ROC分析结果均为良好至优异,AUC分别为0.93和0.86。决策曲线显示,联合模型比单独的放射学和临床模型增加了更多的净效益。结论:该研究提出了一种结合RADscore和临床生物标志物的联合模型,在肝纤维化的分类中有前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.

Purpose: To develop and validate a radiomic nomogram based on texture features from out-of-phase T1W images and clinical biomarkers in prediction of liver fibrosis.

Materials and methods: Patients clinically diagnosed with chronic liver fibrosis who underwent liver biopsy and noncontrast MRI were enrolled. All patients were assigned to the nonsignificant fibrosis group with fibrosis stage <2 and the significant fibrosis group with stage ≥2. Texture parameters were extracted from out-of-phase T1-weighted (T1W) images and calculated using the Artificial Intelligent Kit (AK). Boruta and LASSO regressions were used for feature selection and a multivariable logistic regression was used for construction of a combinational model integrating radiomics and clinical biomarkers. The performance of the models was assessed by using the receiver operator curve (ROC) and decision curve.

Results: ROC analysis of the radiomics model that included the most discriminative features showed AUCs of the training and test groups were 0.80 and 0.78. A combinational model integrating RADscore and fibrosis 4 index was established. ROC analysis of the training and test groups showed good to excellent performance with AUC of 0.93 and 0.86. Decision curves showed the combinational model added more net benefit than radiomic and clinical models alone.

Conclusions: The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.

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来源期刊
CiteScore
4.80
自引率
0.00%
发文量
0
审稿时长
37 weeks
期刊介绍: Canadian Journal of Gastroenterology and Hepatology is a peer-reviewed, open access journal that publishes original research articles, review articles, and clinical studies in all areas of gastroenterology and liver disease - medicine and surgery. The Canadian Journal of Gastroenterology and Hepatology is sponsored by the Canadian Association of Gastroenterology and the Canadian Association for the Study of the Liver.
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