Habitats based multiparametric magnetic resonance imaging radiomics model for prediction of endometrial cancer molecular subtypes.
Motivation: Endometrial cancer (EC) is a highly heterogeneous cancer comprising of both histological and molecular subtypes. The p53abn subtype is associated with a poor prognosis particularly. Goal(s): We aimed to develop habitats based multiparametric MRI radiomics model for the prediction of EC molecular subtype and evaluated the performance. Approach: Our study is a dual-center retrospective research. Results: Our habitats model demonstrated good performance in both internal and external validations. It exhibited higher efficacy compared to radiomics and clinical models. Impact: Using a non-invasive modality method to trigger these subtypes of EC as early as possible will aid clinicians to establish individual treatment. This research also marks the first use of habitat analysis in the study of EC.
Authors
- Wentao Jin
- Tianping Wang (ORCID: https://orcid.org/0000-0001-6702-3926)
- He Zhang (ORCID: https://orcid.org/0000-0003-3782-1411)
- Wentao Li (ORCID: https://orcid.org/0000-0002-6829-686X)
- Haiming Li (ORCID: https://orcid.org/0000-0002-0890-4931)
- Guofu Zhang (ORCID: https://orcid.org/0000-0001-9329-7314)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2024-11-26
- DOI
- https://doi.org/10.58530/2024/3596
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00