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 en...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Canadian Journal of Gastroenterology and Hepatology |
| Online Access: | http://dx.doi.org/10.1155/2022/2249447 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849413974969286656 |
|---|---|
| author | Ru Zhao Hong Zhao Ya-Qiong Ge Fang-Fang Zhou Long-Sheng Wang Hong-Zhen Yu Xi-Jun Gong |
| author_facet | Ru Zhao Hong Zhao Ya-Qiong Ge Fang-Fang Zhou Long-Sheng Wang Hong-Zhen Yu Xi-Jun Gong |
| author_sort | Ru Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-714a6b53d9044eefabb0491d2088358e |
| institution | Kabale University |
| issn | 2291-2797 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Canadian Journal of Gastroenterology and Hepatology |
| spelling | doaj-art-714a6b53d9044eefabb0491d2088358e2025-08-20T03:33:58ZengWileyCanadian Journal of Gastroenterology and Hepatology2291-27972022-01-01202210.1155/2022/2249447Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver FibrosisRu Zhao0Hong Zhao1Ya-Qiong Ge2Fang-Fang Zhou3Long-Sheng Wang4Hong-Zhen Yu5Xi-Jun Gong6Department of RadiologyDepartment of RadiologyGE Healthcare ChinaDepartment of RadiologyDepartment of RadiologyDepartment of PathologyDepartment of RadiologyPurpose. 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.http://dx.doi.org/10.1155/2022/2249447 |
| spellingShingle | Ru Zhao Hong Zhao Ya-Qiong Ge Fang-Fang Zhou Long-Sheng Wang Hong-Zhen Yu Xi-Jun Gong Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis Canadian Journal of Gastroenterology and Hepatology |
| title | Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis |
| title_full | Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis |
| title_fullStr | Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis |
| title_full_unstemmed | Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis |
| title_short | Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis |
| title_sort | usefulness of noncontrast mri based radiomics combined clinic biomarkers in stratification of liver fibrosis |
| url | http://dx.doi.org/10.1155/2022/2249447 |
| work_keys_str_mv | AT ruzhao usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT hongzhao usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT yaqiongge usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT fangfangzhou usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT longshengwang usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT hongzhenyu usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis AT xijungong usefulnessofnoncontrastmribasedradiomicscombinedclinicbiomarkersinstratificationofliverfibrosis |