Diagnostic of fatty liver using radiomics and deep learning models on non-contrast abdominal CT
A systematic comparison was conducted on the performance of 2D and 3D radiomics models, as well as deep learning models, in the diagnosis of four-category fatty liver. This comprehensive model comparison provides a broader perspective for determining the optimal model for liver fat diagnosis. It was found that the 2D radiomics models based on the random forest and Bagging decision tree algorithms show high consistency with the QCT-based classification diagnosis of fatty liver used by experienced radiologists.
Authors
- Zhen Bai (ORCID: https://orcid.org/0000-0001-9331-8525)
- Yan Wu (ORCID: https://orcid.org/0000-0002-8874-8886)
- Danyang Su
- Xiaopeng Yang (ORCID: https://orcid.org/0000-0002-2122-5490)
- Qiuju Miao (ORCID: https://orcid.org/0000-0002-9900-9042)
- Yuanbo Ma
- Jinlong Liu (ORCID: https://orcid.org/0000-0001-9683-9043)
- Haoran Zhang (ORCID: https://orcid.org/0000-0003-1027-9976)
- Mingyue Wang (ORCID: https://orcid.org/0000-0001-9370-3896)
Institutions
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1371/journal.pone.0310938
- Citations
- 5
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.77
Funders
- Health Commission of Henan Province