Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer
Abstract Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic res...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87966-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585701784289280 |
|---|---|
| author | Zhichao Wang Yan Hu Jun Cai Jinyuan Xie Chao Li Xiandong Wu Jingjing Li Haifeng Luo Chuchu He |
| author_facet | Zhichao Wang Yan Hu Jun Cai Jinyuan Xie Chao Li Xiandong Wu Jingjing Li Haifeng Luo Chuchu He |
| author_sort | Zhichao Wang |
| collection | DOAJ |
| description | Abstract Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups. The hybrid radiomics model (HMRadSum) was developed by extracting quantitative imaging features and deep learning features from multiparametric MRI using emerging attention mechanism. Tumor markers were subsequently predicted utilizing an XGBoost classifier. Model performance and interpretability were evaluated using standard classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanations (SHAP) techniques. For the MSI prediction task, the HMRadSum model achieved area-under-curve (AUC) value of 0.945 (95% CI 0.862-1.000) and accuracy of 0.889. For the Ki-67 prediction task, the AUC and accuracy of HMRadSum model was 0.888 (95% CI 0.743-1.000) and 0.810. This hybrid radiomics model effectively extracted features associated with EC gene expression, providing potential clinical implications for personalized diagnosis, treatment, and treatment strategy optimization. |
| format | Article |
| id | doaj-art-46dd583e2fb749418f000b0dd793b052 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-46dd583e2fb749418f000b0dd793b0522025-01-26T12:33:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-87966-wPotential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancerZhichao Wang0Yan Hu1Jun Cai2Jinyuan Xie3Chao Li4Xiandong Wu5Jingjing Li6Haifeng Luo7Chuchu He8Department of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Joint Surgery and Sports Medicine, Jingmen Central HospitalDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityDepartment of Oncology, The First Affiliated Hospital of Yangtze UniversityAbstract Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups. The hybrid radiomics model (HMRadSum) was developed by extracting quantitative imaging features and deep learning features from multiparametric MRI using emerging attention mechanism. Tumor markers were subsequently predicted utilizing an XGBoost classifier. Model performance and interpretability were evaluated using standard classification metrics, Gradient-weighted Class Activation Mapping (Grad-CAM), and SHapley Additive exPlanations (SHAP) techniques. For the MSI prediction task, the HMRadSum model achieved area-under-curve (AUC) value of 0.945 (95% CI 0.862-1.000) and accuracy of 0.889. For the Ki-67 prediction task, the AUC and accuracy of HMRadSum model was 0.888 (95% CI 0.743-1.000) and 0.810. This hybrid radiomics model effectively extracted features associated with EC gene expression, providing potential clinical implications for personalized diagnosis, treatment, and treatment strategy optimization.https://doi.org/10.1038/s41598-025-87966-wRadiomicsAttention mechanismSHAP analysisEndometrial cancerMachine learning |
| spellingShingle | Zhichao Wang Yan Hu Jun Cai Jinyuan Xie Chao Li Xiandong Wu Jingjing Li Haifeng Luo Chuchu He Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer Scientific Reports Radiomics Attention mechanism SHAP analysis Endometrial cancer Machine learning |
| title | Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer |
| title_full | Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer |
| title_fullStr | Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer |
| title_full_unstemmed | Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer |
| title_short | Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer |
| title_sort | potential value of novel multiparametric mri radiomics for preoperative prediction of microsatellite instability and ki 67 expression in endometrial cancer |
| topic | Radiomics Attention mechanism SHAP analysis Endometrial cancer Machine learning |
| url | https://doi.org/10.1038/s41598-025-87966-w |
| work_keys_str_mv | AT zhichaowang potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT yanhu potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT juncai potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT jinyuanxie potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT chaoli potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT xiandongwu potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT jingjingli potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT haifengluo potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer AT chuchuhe potentialvalueofnovelmultiparametricmriradiomicsforpreoperativepredictionofmicrosatelliteinstabilityandki67expressioninendometrialcancer |