Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma
PurposeTo propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC.Materials and methodsClinical data of 77 HPSCC patien...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1129918/full |
| _version_ | 1827985873414651904 |
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| author | Hongjia Liu Hongjia Liu Dan Zhao Yuliang Huang Yuliang Huang Chenguang Li Zhengkun Dong Zhengkun Dong Hongbo Tian Yijie Sun Yanye Lu Chen Chen Hao Wu Hao Wu Yibao Zhang Yibao Zhang |
| author_facet | Hongjia Liu Hongjia Liu Dan Zhao Yuliang Huang Yuliang Huang Chenguang Li Zhengkun Dong Zhengkun Dong Hongbo Tian Yijie Sun Yanye Lu Chen Chen Hao Wu Hao Wu Yibao Zhang Yibao Zhang |
| author_sort | Hongjia Liu |
| collection | DOAJ |
| description | PurposeTo propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC.Materials and methodsClinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models.ResultsPCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components.ConclusionThis study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy. |
| first_indexed | 2024-04-09T23:21:23Z |
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| id | doaj.art-09c80e7ce0c646c79e91e78fced8f396 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T23:21:23Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-09c80e7ce0c646c79e91e78fced8f3962023-03-21T15:42:14ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.11299181129918Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinomaHongjia Liu0Hongjia Liu1Dan Zhao2Yuliang Huang3Yuliang Huang4Chenguang Li5Zhengkun Dong6Zhengkun Dong7Hongbo Tian8Yijie Sun9Yanye Lu10Chen Chen11Hao Wu12Hao Wu13Yibao Zhang14Yibao Zhang15Institute of Medical Technology, Peking University Health Science Center, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United KingdomKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaInstitute of Medical Technology, Peking University Health Science Center, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaSchool of Basic Medical Sciences, Peking University Health Science Center, Beijing, ChinaInstitute of Medical Technology, Peking University Health Science Center, Beijing, ChinaSchool of Electronics Engineering and Computer Science, Peking University, Beijing, ChinaInstitute of Medical Technology, Peking University Health Science Center, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaInstitute of Medical Technology, Peking University Health Science Center, Beijing, ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, ChinaPurposeTo propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC.Materials and methodsClinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models.ResultsPCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components.ConclusionThis study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.https://www.frontiersin.org/articles/10.3389/fonc.2023.1129918/fullhypopharyngeal squamous cell carcinomaradiomicsdosiomicsCox regressionlocoregional recurrence |
| spellingShingle | Hongjia Liu Hongjia Liu Dan Zhao Yuliang Huang Yuliang Huang Chenguang Li Zhengkun Dong Zhengkun Dong Hongbo Tian Yijie Sun Yanye Lu Chen Chen Hao Wu Hao Wu Yibao Zhang Yibao Zhang Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma Frontiers in Oncology hypopharyngeal squamous cell carcinoma radiomics dosiomics Cox regression locoregional recurrence |
| title | Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| title_full | Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| title_fullStr | Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| title_full_unstemmed | Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| title_short | Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| title_sort | comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma |
| topic | hypopharyngeal squamous cell carcinoma radiomics dosiomics Cox regression locoregional recurrence |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1129918/full |
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