An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

<b>Aim:</b> This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.<b>Methods:</b> Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.<b>Results:</b> The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.<b>Conclusion:</b> The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

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Publication Details

Journal
Future Oncology
Published
2024-09-17
DOI
https://doi.org/10.1080/14796694.2024.2398984
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Caihong Liang, Yujiao Zhao, Xiaodong Ji, L Huang et al.
1 links
Future Oncology
Radiomics and Machine Learning in Medical Imaging
article

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Caihong Liang, Yujiao Zhao, Xiaodong Ji, L Huang, Jinxia Zhu, Wen Shen, Xiangfeng Xu, Zhiyi Song, Ling Liu
article en

Abstract

<b>Aim:</b> This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.<b>Methods:</b> Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.<b>Results:</b> The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.<b>Conclusion:</b> The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

Future OncologyVol. 21(22)
Tianjin University of Traditional Chinese Medicine (CN), Nankai University (CN), Tianjin First Center Hospital (CN), Wuqing District People's Hospital (CN), Health First (US), Tianjin Central Hospital of Gynecology Obstetrics (CN), Siemens Healthcare (United States) (US), Campbell Collaboration (NO), Indiana University School of Medicine, Siemens (China) (CN), Tianjin Medical University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Tianjin City
Openalex Percentile: Top 74%
Radiomics and Machine Learning in Medical Imaging
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