Ru Zhao, Hong Zhao, Ya-Qiong Ge, Fang-Fang Zhou, Long-Sheng Wang, Hong-Zhen Yu, Xi-Jun Gong
{"title":"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。","authors":"Ru Zhao, Hong Zhao, Ya-Qiong Ge, Fang-Fang Zhou, Long-Sheng Wang, Hong-Zhen Yu, Xi-Jun Gong","doi":"10.1155/2022/2249447","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.</p>","PeriodicalId":48755,"journal":{"name":"Canadian Journal of Gastroenterology and Hepatology","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239804/pdf/","citationCount":"2","resultStr":"{\"title\":\"Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis.\",\"authors\":\"Ru Zhao, Hong Zhao, Ya-Qiong Ge, Fang-Fang Zhou, Long-Sheng Wang, Hong-Zhen Yu, Xi-Jun Gong\",\"doi\":\"10.1155/2022/2249447\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The study presents a combinational model that incorporates RADscore and clinical biomarkers, which is promising in classification of liver fibrosis.</p>\",\"PeriodicalId\":48755,\"journal\":{\"name\":\"Canadian Journal of Gastroenterology and Hepatology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2022-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239804/pdf/\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Gastroenterology and Hepatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/2249447\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Gastroenterology and Hepatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1155/2022/2249447","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
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.
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.