Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis
Our findings suggest that AI-driven tumor segmentation methods can enhance clinical decision-making by providing reliable and reproducible results, ultimately emphasizing the auxiliary role of automated tumor contouring in clinical practice. The findings will have important implications for the application of radiomics in clinical practice.
Authors
- Yongyun Zhu (ORCID: https://orcid.org/0000-0002-3059-9232)
- Yang Lu (ORCID: https://orcid.org/0000-0002-2564-1993)
- Ying Yu (ORCID: https://orcid.org/0000-0003-1413-2277)
- Guangbin Cui (ORCID: https://orcid.org/0000-0002-7935-9803)
- Ya Gao (ORCID: https://orcid.org/0000-0003-4056-679X)
- Xiaoyan Qu
- Zhi-Ying Ma (ORCID: https://orcid.org/0000-0002-1252-1335)
- Lin‐Feng Yan (ORCID: https://orcid.org/0000-0001-8824-6117)
- Gangfeng Li (ORCID: https://orcid.org/0000-0003-1602-9468)
- Tao Zhang (ORCID: https://orcid.org/0000-0001-7279-8929)
- Yang Yang (ORCID: https://orcid.org/0000-0002-5070-4511)
- Yue Cui (ORCID: https://orcid.org/0000-0002-5939-8717)
- Xuehui Hou (ORCID: https://orcid.org/0000-0003-0633-0601)
- Zhen Zhou (ORCID: https://orcid.org/0000-0002-9125-9581)
- Weixiong Tan
Institutions
- Tang Du Hospital (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1186/s12880-025-01596-2
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80