A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage
Background and purposeHematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with sponta...
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.974183/full |
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| author | Wei Xu Wei Xu Hongquan Guo Huiping Li Qiliang Dai Kangping Song Fangyi Li Junjie Zhou Jingjiang Yao Zhen Wang Xinfeng Liu |
| author_facet | Wei Xu Wei Xu Hongquan Guo Huiping Li Qiliang Dai Kangping Song Fangyi Li Junjie Zhou Jingjiang Yao Zhen Wang Xinfeng Liu |
| author_sort | Wei Xu |
| collection | DOAJ |
| description | Background and purposeHematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.Materials and methodsWe retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.ResultsThe combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).ConclusionThe NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks. |
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| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-12T13:45:29Z |
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| series | Frontiers in Neurology |
| spelling | doaj.art-14ca537316c24094a8d66ebd6416a9a02022-12-22T03:30:42ZengFrontiers Media S.A.Frontiers in Neurology1664-22952022-10-011310.3389/fneur.2022.974183974183A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhageWei Xu0Wei Xu1Hongquan Guo2Huiping Li3Qiliang Dai4Kangping Song5Fangyi Li6Junjie Zhou7Jingjiang Yao8Zhen Wang9Xinfeng Liu10Department of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, ChinaDepartment of Neurology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, ChinaDepartment of Rehabilitation, Hunan Provincial People's Hospital (The First-Affiliated Hospital of Hunan Normal University), Changsha, ChinaDepartment of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, ChinaDepartment of Neurology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Neurology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Neurology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, ChinaDepartment of Neurology, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, ChinaBackground and purposeHematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.Materials and methodsWe retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.ResultsThe combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).ConclusionThe NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks.https://www.frontiersin.org/articles/10.3389/fneur.2022.974183/fullintracerebral hemorrhageradiomics analysishematoma expansionnomogramcomputed tomography |
| spellingShingle | Wei Xu Wei Xu Hongquan Guo Huiping Li Qiliang Dai Kangping Song Fangyi Li Junjie Zhou Jingjiang Yao Zhen Wang Xinfeng Liu A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage Frontiers in Neurology intracerebral hemorrhage radiomics analysis hematoma expansion nomogram computed tomography |
| title | A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| title_full | A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| title_fullStr | A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| title_full_unstemmed | A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| title_short | A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| title_sort | non contrast computed tomography based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage |
| topic | intracerebral hemorrhage radiomics analysis hematoma expansion nomogram computed tomography |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2022.974183/full |
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