Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma
Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in pat...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-12-01
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| Series: | Visual Computing for Industry, Biomedicine, and Art |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42492-023-00149-0 |
| _version_ | 1797416222220877824 |
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| author | Hailin Li Weiyuan Huang Siwen Wang Priya S. Balasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen |
| author_facet | Hailin Li Weiyuan Huang Siwen Wang Priya S. Balasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen |
| author_sort | Hailin Li |
| collection | DOAJ |
| description | Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v e ), volume fraction of plasma space (v p ), volume transfer constant (K trans ), and reverse reflux rate constant (k ep ) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis. |
| first_indexed | 2024-03-09T06:00:20Z |
| format | Article |
| id | doaj.art-9acd6d96cdf44ee480b8226842bcc98d |
| institution | Directory Open Access Journal |
| issn | 2524-4442 |
| language | English |
| last_indexed | 2024-03-09T06:00:20Z |
| publishDate | 2023-12-01 |
| publisher | SpringerOpen |
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| series | Visual Computing for Industry, Biomedicine, and Art |
| spelling | doaj.art-9acd6d96cdf44ee480b8226842bcc98d2023-12-03T12:09:30ZengSpringerOpenVisual Computing for Industry, Biomedicine, and Art2524-44422023-12-016111410.1186/s42492-023-00149-0Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinomaHailin Li0Weiyuan Huang1Siwen Wang2Priya S. Balasubramanian3Gang Wu4Mengjie Fang5Xuebin Xie6Jie Zhang7Di Dong8Jie Tian9Feng Chen10Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang UniversityDepartment of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of SciencesDepartment of Psychiatry, Weill Cornell MedicineDepartment of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang UniversityDepartment of Radiology, Kiang Wu HospitalDepartment of Radiology, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated With Jinan University)CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of SciencesBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang UniversityDepartment of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University)Abstract Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (v e ), volume fraction of plasma space (v p ), volume transfer constant (K trans ), and reverse reflux rate constant (k ep ) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%–52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.https://doi.org/10.1186/s42492-023-00149-0Dynamic contrast-enhanced magnetic resonance imagingMagnetic resonance imagingRadiomicsPrognostic prediction |
| spellingShingle | Hailin Li Weiyuan Huang Siwen Wang Priya S. Balasubramanian Gang Wu Mengjie Fang Xuebin Xie Jie Zhang Di Dong Jie Tian Feng Chen Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma Visual Computing for Industry, Biomedicine, and Art Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction |
| title | Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| title_full | Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| title_fullStr | Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| title_full_unstemmed | Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| title_short | Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| title_sort | comprehensive integrated analysis of mr and dce mr radiomics models for prognostic prediction in nasopharyngeal carcinoma |
| topic | Dynamic contrast-enhanced magnetic resonance imaging Magnetic resonance imaging Radiomics Prognostic prediction |
| url | https://doi.org/10.1186/s42492-023-00149-0 |
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