Staging liver fibrosis : comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
OBJECTIVES: To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease.
MATERIALS AND METHODS: Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures.
RESULTS: In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042).
CONCLUSIONS: Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:49 |
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| Enthalten in: |
Abdominal radiology (New York) - 49(2024), 4 vom: 21. März, Seite 1165-1174 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Xiao, Longyang [VerfasserIn] |
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| Links: |
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| Themen: |
Chronic liver disease |
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| Anmerkungen: |
Date Completed 22.03.2024 Date Revised 22.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1007/s00261-023-04142-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 245 | 1 | 0 | |a Staging liver fibrosis |b comparison of radiomics model and fusion model based on multiparametric MRI in patients with chronic liver disease |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a OBJECTIVES: To develop and compare radiomics model and fusion model based on multiple MR parameters for staging liver fibrosis in patients with chronic liver disease | ||
| 520 | |a MATERIALS AND METHODS: Patients with chronic liver disease who underwent multiparametric abdominal MRI were included in this retrospective study. Multiparametric MR images were imported into 3D-Slicer software for drawing bounding boxes on MR images. By using a 3D-Slicer extension of SlicerRadiomics, radiomics features were extracted from these MR images. The z-score normalization method was used for post-processing radiomics features. The least absolute shrinkage and selection operator method (LASSO) was performed for selecting significant radiomics features. The logistic regression analysis was used for building the radiomics model. A fusion model was built by integrating serum fibrosis biomarkers of aspartate transaminase-to-platelet ratio index (APRI) and the fibrosis-4 index (FIB-4) with radiomics signatures | ||
| 520 | |a RESULTS: In the training cohort, AUCs of radiomics and fusion model were 0.707-0.842 and 0.718-0.854 for differentiating different groups. In the testing cohort, AUCs were 0.514-0.724 and 0.609-0.728. For the training cohort, there was no significant difference of AUCs between radiomics and fusion model (p > 0.05). For the testing cohort, AUCs of fusion model were higher than those of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 (p = 0.011 & 0.042) | ||
| 520 | |a CONCLUSIONS: Radiomics model and fusion model based on multiparametric MRI exhibited the feasibility for staging liver fibrosis in patients with CLD, and APRI and FIB-4 could improve the diagnostic performance of radiomics model in differentiating F1-3 vs. F4 and F1-2 vs. F4 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Chronic liver disease | |
| 650 | 4 | |a Liver fibrosis | |
| 650 | 4 | |a Multiparametric MRI | |
| 650 | 4 | |a Radiomics model | |
| 650 | 4 | |a Serum fibrosis biomarkers | |
| 700 | 1 | |a Zhao, Haichen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Shunli |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Wenlu |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yuanxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Lili |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Baoxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhiming |e verfasserin |4 aut | |
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