A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma
PurposeThis paper aimed to establish and verify a radiomics model based on magnetic resonance imaging (MRI) for predicting the progression-free survival of nasopharyngeal carcinoma (NPC) after induction chemotherapy (IC).Materials and MethodsThis cohort consists of 288 patients with clinical patholo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.792535/full |
| _version_ | 1811333626130857984 |
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| author | Lu Liu Wei Pei Hai Liao Qiang Wang Donglian Gu Lijuan Liu Danke Su Guanqiao Jin |
| author_facet | Lu Liu Wei Pei Hai Liao Qiang Wang Donglian Gu Lijuan Liu Danke Su Guanqiao Jin |
| author_sort | Lu Liu |
| collection | DOAJ |
| description | PurposeThis paper aimed to establish and verify a radiomics model based on magnetic resonance imaging (MRI) for predicting the progression-free survival of nasopharyngeal carcinoma (NPC) after induction chemotherapy (IC).Materials and MethodsThis cohort consists of 288 patients with clinical pathologically confirmed NPC, which was collected from January 2015 to December 2018. All NPC patients were randomly divided into two cohorts: training (n=202) and validation (n=86). Radiomics features from the MRI images of NPC patients were extracted and selected before IC. The patients were classified into high- and low-risk groups according to the median of Radscores. The significant imaging features and clinical variables in the univariate analysis were constructed for progression-free survival (PFS) using the multivariate Cox regression model. A survival analysis was performed using Kaplan–Meier with log-rank test and then each model’s stratification ability was evaluated.ResultsEpstein–Barr virus (EBV) DNA before treatment was an independent predictor for PFS (p < 0.05). Based on the pyradiomic platform, we extracted 1,316 texture parameters in total. Finally, 16 texture features were used to build the model. The clinical radiomics-based model had good prediction capability for PFS, with a C-index of 0.827. The survival curve revealed that the PFS of the high-risk group was poorer than that of the low-risk group.ConclusionThis research presents a nomogram that merges the radiomics signature and the clinical feature of the plasma EBV DNA load, which may improve the ability of preoperative prediction of progression-free survival and facilitate individualization of treatment in NPC patients before IC. |
| first_indexed | 2024-04-13T16:55:46Z |
| format | Article |
| id | doaj.art-6cba4dd257f9423bbb8a15b4e1997090 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-13T16:55:46Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-6cba4dd257f9423bbb8a15b4e19970902022-12-22T02:38:49ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.792535792535A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal CarcinomaLu Liu0Wei Pei1Hai Liao2Qiang Wang3Donglian Gu4Lijuan Liu5Danke Su6Guanqiao Jin7Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaDepartment of Radiology, Guangxi Medical University Cancer Hospital, Nanning, ChinaPurposeThis paper aimed to establish and verify a radiomics model based on magnetic resonance imaging (MRI) for predicting the progression-free survival of nasopharyngeal carcinoma (NPC) after induction chemotherapy (IC).Materials and MethodsThis cohort consists of 288 patients with clinical pathologically confirmed NPC, which was collected from January 2015 to December 2018. All NPC patients were randomly divided into two cohorts: training (n=202) and validation (n=86). Radiomics features from the MRI images of NPC patients were extracted and selected before IC. The patients were classified into high- and low-risk groups according to the median of Radscores. The significant imaging features and clinical variables in the univariate analysis were constructed for progression-free survival (PFS) using the multivariate Cox regression model. A survival analysis was performed using Kaplan–Meier with log-rank test and then each model’s stratification ability was evaluated.ResultsEpstein–Barr virus (EBV) DNA before treatment was an independent predictor for PFS (p < 0.05). Based on the pyradiomic platform, we extracted 1,316 texture parameters in total. Finally, 16 texture features were used to build the model. The clinical radiomics-based model had good prediction capability for PFS, with a C-index of 0.827. The survival curve revealed that the PFS of the high-risk group was poorer than that of the low-risk group.ConclusionThis research presents a nomogram that merges the radiomics signature and the clinical feature of the plasma EBV DNA load, which may improve the ability of preoperative prediction of progression-free survival and facilitate individualization of treatment in NPC patients before IC.https://www.frontiersin.org/articles/10.3389/fonc.2022.792535/fullnasopharyngeal carcinomaradiomicsmagnetic resonance imaginginduction chemotherapysurvival models |
| spellingShingle | Lu Liu Wei Pei Hai Liao Qiang Wang Donglian Gu Lijuan Liu Danke Su Guanqiao Jin A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma Frontiers in Oncology nasopharyngeal carcinoma radiomics magnetic resonance imaging induction chemotherapy survival models |
| title | A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma |
| title_full | A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma |
| title_fullStr | A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma |
| title_full_unstemmed | A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma |
| title_short | A Clinical-Radiomics Nomogram Based on Magnetic Resonance Imaging for Predicting Progression-Free Survival After Induction Chemotherapy in Nasopharyngeal Carcinoma |
| title_sort | clinical radiomics nomogram based on magnetic resonance imaging for predicting progression free survival after induction chemotherapy in nasopharyngeal carcinoma |
| topic | nasopharyngeal carcinoma radiomics magnetic resonance imaging induction chemotherapy survival models |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.792535/full |
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