Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study
Abstract Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models—including Clinical model, Radiomics models (RD), Deep Learning m...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13781-y |
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| author | Shigang Luo Yan Guo Yongqing Ye Qinglin Mu Wenguang Huang Guangcai Tang |
| author_facet | Shigang Luo Yan Guo Yongqing Ye Qinglin Mu Wenguang Huang Guangcai Tang |
| author_sort | Shigang Luo |
| collection | DOAJ |
| description | Abstract Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models—including Clinical model, Radiomics models (RD), Deep Learning models (DL), Radiomics–Deep Learning fusion models (RD-DL), and a Clinical–RD-DL combined model—for assessing the risk of OLNM in cervical cancer patients.The study included 130 patients from Center 1 (training set) and 55 from Center 2 (test set). Clinical data and imaging sequences (T1, T2, and DWI) were used to extract features for model construction. Model performance was assessed using the DeLong test, and SHAP analysis was used to examine feature contributions. Results showed that both the RD-combined (AUC = 0.803) and DL-combined (AUC = 0.818) models outperformed single-sequence models as well as the standalone Clinical model (AUC = 0.702). The RD-DL model yielded the highest performance, achieving an AUC of 0.981 in the training set and 0.903 in the test set. Notably, integrating clinical variables did not further improve predictive performance; the Clinical–RD-DL model performed comparably to the RD-DL model. SHAP analysis showed that deep learning features had the greatest impact on model predictions. Both RD and DL models effectively predict OLNM, with the RD-DL model offering superior performance. These findings provide a rapid, non-invasive clinical prediction method. |
| format | Article |
| id | doaj-art-02b51b7e4332498d9edb439808864c2f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-02b51b7e4332498d9edb439808864c2f2025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-13781-yPrediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center studyShigang Luo0Yan Guo1Yongqing Ye2Qinglin Mu3Wenguang Huang4Guangcai Tang5Department of Radiology, The First People’s Hospital of GuangyuanDepartment of Radiology, The First People’s Hospital of GuangyuanDepartment of Radiology, The First People’s Hospital of GuangyuanDepartment of Radiology, The First People’s Hospital of GuangyuanDepartment of Radiology, The First People’s Hospital of GuangyuanDepartment of Radiology, Affiliated Hospital of Southwest Medical UniversityAbstract Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models—including Clinical model, Radiomics models (RD), Deep Learning models (DL), Radiomics–Deep Learning fusion models (RD-DL), and a Clinical–RD-DL combined model—for assessing the risk of OLNM in cervical cancer patients.The study included 130 patients from Center 1 (training set) and 55 from Center 2 (test set). Clinical data and imaging sequences (T1, T2, and DWI) were used to extract features for model construction. Model performance was assessed using the DeLong test, and SHAP analysis was used to examine feature contributions. Results showed that both the RD-combined (AUC = 0.803) and DL-combined (AUC = 0.818) models outperformed single-sequence models as well as the standalone Clinical model (AUC = 0.702). The RD-DL model yielded the highest performance, achieving an AUC of 0.981 in the training set and 0.903 in the test set. Notably, integrating clinical variables did not further improve predictive performance; the Clinical–RD-DL model performed comparably to the RD-DL model. SHAP analysis showed that deep learning features had the greatest impact on model predictions. Both RD and DL models effectively predict OLNM, with the RD-DL model offering superior performance. These findings provide a rapid, non-invasive clinical prediction method.https://doi.org/10.1038/s41598-025-13781-yRadiomicsDeep learningOccult lymph node metastasisCervical cancer |
| spellingShingle | Shigang Luo Yan Guo Yongqing Ye Qinglin Mu Wenguang Huang Guangcai Tang Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study Scientific Reports Radiomics Deep learning Occult lymph node metastasis Cervical cancer |
| title | Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study |
| title_full | Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study |
| title_fullStr | Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study |
| title_full_unstemmed | Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study |
| title_short | Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study |
| title_sort | prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features a dual center study |
| topic | Radiomics Deep learning Occult lymph node metastasis Cervical cancer |
| url | https://doi.org/10.1038/s41598-025-13781-y |
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