MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study
Abstract
Objectives: Preoperative identification of FIGO 2023 stage I endometrial cancer (EC) is important for treatment planning. We aimed to develop and validate a deep-learning radiomics nomogram (DLRN) based on multiparametric MRI to distinguish stage I EC from stage II–IV disease.
Materials and methods: This study retrospectively enrolled patients from three centers. Patients from Centers 1 and 2 were randomly split into a training set ( n = 616), and an internal test set ( n = 158), and Center 3 served as an external test set ( n = 232). The DLRN integrated deep-learning features, intratumoral and peritumoral radiomics features, and clinical characteristics. The DLRN was compared with clinical, radiomics, and deep-learning models. Discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), with AUCs compared using DeLong’s test.
Results: A total of 1006 patients (mean age, 56.5 ± 9.7 years) were analyzed. In the internal test set, the DLRN achieved an AUC of 0.852, higher than the clinical (0.673; p = 0.001), original radiomics (0.765; p = 0.030), and combined radiomics (0.793; p = 0.050) models. In the external test set, the DLRN remained robust (AUC = 0.829) and outperformed the clinical, original radiomics, and combined radiomics models (AUC = 0.680, 0.681, and 0.743; all p < 0.005). With DLRN assistance, radiologists showed improved diagnostic performance.
Conclusion: The MRI-based DLRN showed robust discrimination between FIGO 2023 stage I and stage II–IV EC across test sets, supporting its potential for preoperative risk stratification and decision support.
Key Points: Question The introduction of the FIGO 2023 staging system has made the differentiation between stages more challenging, highlighting the need for better tools.
Findings A new MRI-based deep-learning radiomics nomogram (DLRN) accurately identifies high-risk patients, significantly outperforming traditional and individual clinical prediction methods.
Critical relevance statement This tool helps doctors make better preoperative treatment plans, potentially improving patient outcomes in endometrial cancer.
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BibTeX
@article{chu2026mribased,
title = {{MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study}},
author = {Xiao Chu and Liting Gao and Xinyi Wang and Tao Quan and Yuxin Ding and Ying Chen and Genji Bai and Mingxiang Wei and Shuangqing Chen},
journal = {Insights into Imaging},
year = {2026},
month = nov,
doi = {10.1186/s13244-026-02428-9},
url = {https://link.springer.com/article/10.1186/s13244-026-02428-9},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study
AU - Xiao Chu
AU - Liting Gao
AU - Xinyi Wang
AU - Tao Quan
AU - Yuxin Ding
AU - Ying Chen
AU - Genji Bai
AU - Mingxiang Wei
AU - Shuangqing Chen
PY - 2026
DA - 2026/11/06
JO - Insights into Imaging
DO - 10.1186/s13244-026-02428-9
UR - https://link.springer.com/article/10.1186/s13244-026-02428-9
AB - Objectives: Preoperative identification of FIGO 2023 stage I endometrial cancer (EC) is important for treatment planning. We aimed to develop and validate a deep-learning radiomics nomogram (DLRN) based on multiparametric MRI to distinguish stage I EC from stage II–IV disease. Materials and methods: This study retrospectively enrolled patients from three centers. Patients from Centers 1 and 2 were randomly split into a training set ( n = 616), and an internal test set ( n = 158), and Center 3 served as an external test set ( n = 232). The DLRN integrated deep-learning features, intratumoral and peritumoral radiomics features, and clinical characteristics. The DLRN was compared with clinical, radiomics, and deep-learning models. Discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), with AUCs compared using DeLong’s test. Results: A total of 1006 patients (mean age, 56.5 ± 9.7 years) were analyzed. In the internal test set, the DLRN achieved an AUC of 0.852, higher than the clinical (0.673; p = 0.001), original radiomics (0.765; p = 0.030), and combined radiomics (0.793; p = 0.050) models. In the external test set, the DLRN remained robust (AUC = 0.829) and outperformed the clinical, original radiomics, and combined radiomics models (AUC = 0.680, 0.681, and 0.743; all p < 0.005). With DLRN assistance, radiologists showed improved diagnostic performance. Conclusion: The MRI-based DLRN showed robust discrimination between FIGO 2023 stage I and stage II–IV EC across test sets, supporting its potential for preoperative risk stratification and decision support. Key Points: Question The introduction of the FIGO 2023 staging system has made the differentiation between stages more challenging, highlighting the need for better tools. Findings A new MRI-based deep-learning radiomics nomogram (DLRN) accurately identifies high-risk patients, significantly outperforming traditional and individual clinical prediction methods. Critical relevance statement This tool helps doctors make better preoperative treatment plans, potentially improving patient outcomes in endometrial cancer.
SN - 1869-4101
ER -