Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma
Abstract The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-01270-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849728227900129280 |
|---|---|
| author | Kai Xie Huan Jiang Xinwei Chen Youquan Ning Qiang Yu Fajin Lv Rui Liu Yuan Zhou Lin Xu Qiang Yue Juan Peng |
| author_facet | Kai Xie Huan Jiang Xinwei Chen Youquan Ning Qiang Yu Fajin Lv Rui Liu Yuan Zhou Lin Xu Qiang Yue Juan Peng |
| author_sort | Kai Xie |
| collection | DOAJ |
| description | Abstract The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I–II and III–IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97). Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. These features were fused with crucial radiomics features to create a combined model. The performance of the models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with the radiologist performances. The predictive capability of the combined model for Progression-Free Survival (PFS) was evaluated via Kaplan–Meier survival analysis and the Harrell’s Concordance Index (C-index). In the external test set, the combined model had an AUC of 0.877 (95% CI 0.807–0.946), outperforming the DL model (AUC: 0.811) and the optimal radiomics model (AUC: 0.835). The combined model significantly outperformed both the DL (p = 0.017) and the optimal radiomics models (p = 0.039), and the radiologists (both p < 0.050). Moreover, the combined model demonstrated great prognostic predictive value in patients with LSCC, achieving a C-index of 0.624 for PFS. This combined model enhances preoperative LSCC staging, aiding in making more informed clinical decisions. |
| format | Article |
| id | doaj-art-99056f22d79a492f9ffcd0fb90ab604f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-99056f22d79a492f9ffcd0fb90ab604f2025-08-20T03:09:35ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01270-1Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinomaKai Xie0Huan Jiang1Xinwei Chen2Youquan Ning3Qiang Yu4Fajin Lv5Rui Liu6Yuan Zhou7Lin Xu8Qiang Yue9Juan Peng10Department of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversitySchool of Intelligent Medicine, Chengdu University of TCMDepartment of Radiology, West China Hospital of Sichuan UniversityDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityAbstract The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to preoperatively distinguish between Stages I–II and III–IV of LSCC. Data from 401 histologically confirmed LSCC patients were collected from two centers (training set: 213; internal test set: 91; external test set: 97). Radiomics features were extracted from the MRI images, and seven radiomics models based on single and combined sequences were developed via random forest (RF). A DL model was constructed via ResNet 18, where DL features were extracted from its final fully connected layer. These features were fused with crucial radiomics features to create a combined model. The performance of the models was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and compared with the radiologist performances. The predictive capability of the combined model for Progression-Free Survival (PFS) was evaluated via Kaplan–Meier survival analysis and the Harrell’s Concordance Index (C-index). In the external test set, the combined model had an AUC of 0.877 (95% CI 0.807–0.946), outperforming the DL model (AUC: 0.811) and the optimal radiomics model (AUC: 0.835). The combined model significantly outperformed both the DL (p = 0.017) and the optimal radiomics models (p = 0.039), and the radiologists (both p < 0.050). Moreover, the combined model demonstrated great prognostic predictive value in patients with LSCC, achieving a C-index of 0.624 for PFS. This combined model enhances preoperative LSCC staging, aiding in making more informed clinical decisions.https://doi.org/10.1038/s41598-025-01270-1Multiparametric MRILaryngeal squamous cell carcinomaCancer stagingRadiomicsDeep learning |
| spellingShingle | Kai Xie Huan Jiang Xinwei Chen Youquan Ning Qiang Yu Fajin Lv Rui Liu Yuan Zhou Lin Xu Qiang Yue Juan Peng Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma Scientific Reports Multiparametric MRI Laryngeal squamous cell carcinoma Cancer staging Radiomics Deep learning |
| title | Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| title_full | Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| title_fullStr | Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| title_full_unstemmed | Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| title_short | Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| title_sort | multiparameter mri based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma |
| topic | Multiparametric MRI Laryngeal squamous cell carcinoma Cancer staging Radiomics Deep learning |
| url | https://doi.org/10.1038/s41598-025-01270-1 |
| work_keys_str_mv | AT kaixie multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT huanjiang multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT xinweichen multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT youquanning multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT qiangyu multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT fajinlv multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT ruiliu multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT yuanzhou multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT linxu multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT qiangyue multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma AT juanpeng multiparametermribasedmodelintegratingradiomicsanddeeplearningforpreoperativestagingoflaryngealsquamouscellcarcinoma |