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

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
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article

Habitats based multiparametric magnetic resonance imaging radiomics model for prediction of endometrial cancer molecular subtypes.

Wentao Jin, Tianping Wang, He Zhang, Wentao Li et al.
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
Radiomics and Machine Learning in Medical Imaging
article

Habitats based multiparametric magnetic resonance imaging radiomics model for prediction of endometrial cancer molecular subtypes.

Wentao Jin, Tianping Wang, He Zhang, Wentao Li, Haiming Li, Guofu Zhang
article en

Abstract

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.

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
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Openalex Percentile: Top 62%
Radiomics and Machine Learning in Medical Imaging
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Habitats based multiparametric magnetic resonance imaging radiomics model for prediction of endometrial cancer molecular subtypes. — Wentao Jin, Tianping Wang, et al. · 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 (2024) | TGRS Research Map | TGRS