Intratumoral and peritumoral radiomics based on multi-parametric magnetic resonance imaging for predicting microsatellite instability in endometrial cancer
- 1The First Affiliated Hospital Shihezi University, Medical Imaging Center, Shihezi, China
Abstract
PURPOSE: This study aimed to evaluate the utility of intratumoral and peritumoral radiomics derived from multi-parametric magnetic resonance imaging for predicting microsatellite instability (MSI) in endometrial cancer (EC).
METHODS: We retrospectively analyzed 161 patients with pathologically confirmed EC, assigning them to a training (n = 112) and a test cohort (n = 49) at a 7:3 ratio, and collected their full clinical and imaging data. We manually delineated regions of interest on axial T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (CE) T1-weighted imaging sequences, expanding them by 3, 5, and 7 mm to define peritumoral regions. We then extracted and selected radiomic features from both intratumoral and peritumoral areas. Using six machine learning algorithms, we developed separate radiomics models and assessed their performance via the area under the receiver operating characteristic curve (AUC). To complete the evaluation, we compared statistical differences using the DeLong test and generated calibration curves to verify predictive accuracy.
RESULTS: Clinical characteristics exhibited no significant correlation with MSI status. In single-sequence analysis, the CE model demonstrated the highest performance (training AUC: 0.912; test AUC: 0.856). The multi-parametric model (T2WI + DWI + CE) outperformed single-sequence models, achieving AUCs of 0.934 and 0.914 in the training and test cohorts, respectively. Peritumoral radiomics independently showed robust predictive value; specifically, the model derived from 3 mm peritumoral features (across DWI) yielded a training AUC of 0.898 and a test AUC of 0.790. Notably, the integration of intratumoral and peritumoral features maximized predictive accuracy. The final model, combining optimal intratumoral features (T2WI + DWI + CE) with 3 mm peritumoral DWI features, achieved the best performance with a remarkable AUC of 0.998 in the test cohort.
CONCLUSION: Peritumoral radiomics possesses strong independent predictive value and significantly enhances the performance of intratumoral models. The integration of intratumoral and peritumoral radiomics offers a precise, non-invasive method for preoperative MSI prediction, serving as a valuable tool to facilitate clinical decision-making.
CLINICAL SIGNIFICANCE: This study establishes a reliable, non-invasive radiomics approach for the preoperative assessment of MSI status.
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BibTeX
@article{he2026intratumoral,
title = {{Intratumoral and peritumoral radiomics based on multi-parametric magnetic resonance imaging for predicting microsatellite instability in endometrial cancer}},
author = {Xiaoyu He and Xi Yin},
journal = {Diagnostic and Interventional Radiology},
year = {2026},
month = nov,
doi = {10.4274/dir.2026.263743},
url = {https://doi.org/10.4274/dir.2026.263743},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Intratumoral and peritumoral radiomics based on multi-parametric magnetic resonance imaging for predicting microsatellite instability in endometrial cancer
AU - Xiaoyu He
AU - Xi Yin
PY - 2026
DA - 2026/11/02
JO - Diagnostic and Interventional Radiology
DO - 10.4274/dir.2026.263743
UR - https://doi.org/10.4274/dir.2026.263743
AB - PURPOSE: This study aimed to evaluate the utility of intratumoral and peritumoral radiomics derived from multi-parametric magnetic resonance imaging for predicting microsatellite instability (MSI) in endometrial cancer (EC). METHODS: We retrospectively analyzed 161 patients with pathologically confirmed EC, assigning them to a training (n = 112) and a test cohort (n = 49) at a 7:3 ratio, and collected their full clinical and imaging data. We manually delineated regions of interest on axial T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (CE) T1-weighted imaging sequences, expanding them by 3, 5, and 7 mm to define peritumoral regions. We then extracted and selected radiomic features from both intratumoral and peritumoral areas. Using six machine learning algorithms, we developed separate radiomics models and assessed their performance via the area under the receiver operating characteristic curve (AUC). To complete the evaluation, we compared statistical differences using the DeLong test and generated calibration curves to verify predictive accuracy. RESULTS: Clinical characteristics exhibited no significant correlation with MSI status. In single-sequence analysis, the CE model demonstrated the highest performance (training AUC: 0.912; test AUC: 0.856). The multi-parametric model (T2WI + DWI + CE) outperformed single-sequence models, achieving AUCs of 0.934 and 0.914 in the training and test cohorts, respectively. Peritumoral radiomics independently showed robust predictive value; specifically, the model derived from 3 mm peritumoral features (across DWI) yielded a training AUC of 0.898 and a test AUC of 0.790. Notably, the integration of intratumoral and peritumoral features maximized predictive accuracy. The final model, combining optimal intratumoral features (T2WI + DWI + CE) with 3 mm peritumoral DWI features, achieved the best performance with a remarkable AUC of 0.998 in the test cohort. CONCLUSION: Peritumoral radiomics possesses strong independent predictive value and significantly enhances the performance of intratumoral models. The integration of intratumoral and peritumoral radiomics offers a precise, non-invasive method for preoperative MSI prediction, serving as a valuable tool to facilitate clinical decision-making. CLINICAL SIGNIFICANCE: This study establishes a reliable, non-invasive radiomics approach for the preoperative assessment of MSI status.
SN - 1305-3825
SN - 1305-3612
ER -