Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics
Purpose: The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). Methods: In a retrospective analysis, we in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | European Journal of Radiology Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047723000692 |
| _version_ | 1797366579884720128 |
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| author | Yuzhen Xi Hao Dong Mengze Wang Shiyu Chen Jing Han Miao Liu Feng Jiang Zhongxiang Ding |
| author_facet | Yuzhen Xi Hao Dong Mengze Wang Shiyu Chen Jing Han Miao Liu Feng Jiang Zhongxiang Ding |
| author_sort | Yuzhen Xi |
| collection | DOAJ |
| description | Purpose: The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). Methods: In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. Results: The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. Conclusion: The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning. |
| first_indexed | 2024-03-08T17:06:45Z |
| format | Article |
| id | doaj.art-493b5156709c4b89bb2552314ed5893a |
| institution | Directory Open Access Journal |
| issn | 2352-0477 |
| language | English |
| last_indexed | 2024-03-08T17:06:45Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj.art-493b5156709c4b89bb2552314ed5893a2024-01-04T04:39:36ZengElsevierEuropean Journal of Radiology Open2352-04772024-06-0112100543Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomicsYuzhen Xi0Hao Dong1Mengze Wang2Shiyu Chen3Jing Han4Miao Liu5Feng Jiang6Zhongxiang Ding7Department of Radiology, 903th RD Hospital of PLA, Hangzhou, ChinaDepartment of Radiology, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Zhejiang Cancer Hospital, Hangzhou, ChinaDepartment of Radiology, 903th RD Hospital of PLA, Hangzhou, ChinaDepartment of Radiology, Zhejiang KangJing Hospital, Hangzhou, ChinaDepartment of Radiology, 903th RD Hospital of PLA, Hangzhou, China; Corresponding authors.Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China; Corresponding authors.Department of Radiology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Cancer Center, Hangzhou, China; Correspondence to: Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Hangzhou, Zhejiang, China.Purpose: The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). Methods: In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. Results: The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. Conclusion: The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.http://www.sciencedirect.com/science/article/pii/S2352047723000692Nasopharyngeal carcinomaMRIMachine learningNomogramLong-term |
| spellingShingle | Yuzhen Xi Hao Dong Mengze Wang Shiyu Chen Jing Han Miao Liu Feng Jiang Zhongxiang Ding Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics European Journal of Radiology Open Nasopharyngeal carcinoma MRI Machine learning Nomogram Long-term |
| title | Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics |
| title_full | Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics |
| title_fullStr | Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics |
| title_full_unstemmed | Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics |
| title_short | Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics |
| title_sort | early prediction of long term survival of patients with nasopharyngeal carcinoma by multi parameter mri radiomics |
| topic | Nasopharyngeal carcinoma MRI Machine learning Nomogram Long-term |
| url | http://www.sciencedirect.com/science/article/pii/S2352047723000692 |
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