MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma
ObjectiveTumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1192953/full |
| _version_ | 1797826876015640576 |
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| author | Mengxing Wu Mengxing Wu Weilin Xu Yinjiao Fei Yurong Li Yurong Li Jinling Yuan Jinling Yuan Lei Qiu Lei Qiu Yumeng Zhang Guanhua Chen Yu Cheng Yuandong Cao Xinchen Sun Xinchen Sun Shu Zhou |
| author_facet | Mengxing Wu Mengxing Wu Weilin Xu Yinjiao Fei Yurong Li Yurong Li Jinling Yuan Jinling Yuan Lei Qiu Lei Qiu Yumeng Zhang Guanhua Chen Yu Cheng Yuandong Cao Xinchen Sun Xinchen Sun Shu Zhou |
| author_sort | Mengxing Wu |
| collection | DOAJ |
| description | ObjectiveTumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC).MethodsA total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram.ResultsPlatelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram.ConclusionA clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy. |
| first_indexed | 2024-04-09T12:39:16Z |
| format | Article |
| id | doaj.art-fb0cd295ab15497bb270d3d9f49c8855 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T12:39:16Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fb0cd295ab15497bb270d3d9f49c88552023-05-15T05:01:03ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-05-011310.3389/fonc.2023.11929531192953MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinomaMengxing Wu0Mengxing Wu1Weilin Xu2Yinjiao Fei3Yurong Li4Yurong Li5Jinling Yuan6Jinling Yuan7Lei Qiu8Lei Qiu9Yumeng Zhang10Guanhua Chen11Yu Cheng12Yuandong Cao13Xinchen Sun14Xinchen Sun15Shu Zhou16Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, ChinaDepartment of Radiotherapy, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, ChinaDepartment of Oncology, The Second Hospital of Nanjing, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaThe First School of Clinical Medicine, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, ChinaObjectiveTumor residue after concurrent chemoradiotherapy (CCRT) in nasopharyngeal carcinoma (NPC) patients often predicts poor prognosis. Thus, the objective of this retrospective study is to develop a nomogram that combines magnetic resonance (MRI) radiomics features and clinical features to predict the early response of locally advanced nasopharyngeal carcinoma (LA-NPC).MethodsA total of 91 patients with LA-NPC were included in this study. Patients were randomly divided into training and validation cohorts at a ratio of 3:1. Univariate and multivariate analyses were performed on the clinical parameters of the patients to select clinical features to build a clinical model. In the training cohort, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used to select radiomics features for construction of a radiomics model. The logistic regression algorithm was then used to combine the clinical features with the radiomics features to construct the clinical radiomics nomogram. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical radiomics nomogram.ResultsPlatelet lymphocyte ratio (PLR) and nasopharyngeal tumor volume were identified as independent predictors of early response in patients with locally advanced nasopharyngeal carcinoma. A total of 5502 radiomics features were extracted, from which 25 radiomics features were selected to construct the radiomics model. The clinical radiomics nomogram demonstrated the highest AUC in both the training and validation cohorts (training cohort 0.975 vs 0.973 vs 0.713; validation cohort 0.968 vs 0.952 vs 0.706). The calibration curve and DCA indicated good predictive performance for the nomogram.ConclusionA clinical radiomics nomogram, which combines clinical features with radiomics features based on MRI, can predict early tumor regression in patients with LA-NPC. The performance of the nomogram is superior to that of either the clinical model or radiomics model alone. Therefore, it can be used to identify patients without CR at an early stage and provide guidance for personalized therapy.https://www.frontiersin.org/articles/10.3389/fonc.2023.1192953/fulllocally advanced nasopharyngeal carcinomaradiomicsclinical featuresnuclear magnetic resonanceearly response and remissionnomogram |
| spellingShingle | Mengxing Wu Mengxing Wu Weilin Xu Yinjiao Fei Yurong Li Yurong Li Jinling Yuan Jinling Yuan Lei Qiu Lei Qiu Yumeng Zhang Guanhua Chen Yu Cheng Yuandong Cao Xinchen Sun Xinchen Sun Shu Zhou MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma Frontiers in Oncology locally advanced nasopharyngeal carcinoma radiomics clinical features nuclear magnetic resonance early response and remission nomogram |
| title | MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| title_full | MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| title_fullStr | MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| title_full_unstemmed | MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| title_short | MRI-based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| title_sort | mri based clinical radiomics nomogram may predict the early response after concurrent chemoradiotherapy in locally advanced nasopharyngeal carcinoma |
| topic | locally advanced nasopharyngeal carcinoma radiomics clinical features nuclear magnetic resonance early response and remission nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1192953/full |
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