An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage
Copyright © 2024 Elsevier Inc. All rights reserved..
OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it.
METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram.
RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically.
CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:183 |
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| Enthalten in: |
World neurosurgery - 183(2024) vom: 13. März, Seite e638-e648 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Luo, Zhixian [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 13.03.2024 Date Revised 13.03.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.wneu.2023.12.160 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM36672486X |
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| 245 | 1 | 3 | |a An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2024 Elsevier Inc. All rights reserved. | ||
| 520 | |a OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it | ||
| 520 | |a METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram | ||
| 520 | |a RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically | ||
| 520 | |a CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Online dynamic nomogram | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a Radiomics | |
| 650 | 4 | |a Recurrent intracerebral hemorrhage | |
| 700 | 1 | |a Zhou, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Mengying |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Haoli |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, Xinyi |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Zhenghao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Meihao |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Zusen |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yunjun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Dongqin |e verfasserin |4 aut | |
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