Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy
ObjectiveThis study aims to develop and validate a nomogram that combines traditional ultrasound radiomics features with clinical parameters to assess early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI). The study...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Medical Technology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmedt.2025.1485244/full |
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| author | Zunfeng Fu Lin Peng Laicai Guo Chao Qin Yanhong Yu Jiajun Zhang Yan Liu |
| author_facet | Zunfeng Fu Lin Peng Laicai Guo Chao Qin Yanhong Yu Jiajun Zhang Yan Liu |
| author_sort | Zunfeng Fu |
| collection | DOAJ |
| description | ObjectiveThis study aims to develop and validate a nomogram that combines traditional ultrasound radiomics features with clinical parameters to assess early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI). The study incorporates the Shapley Additive Explanations (SHAP) method to interpret the radiomics model.MethodsThis study included 199 patients with severe TBI (training cohort: n = 159; testing cohort: n = 40). Postoperative ultrasound images of the optic nerve sheath (ONS) were obtained at 6 and 18 h after DC. Based on invasive intracranial pressure (ICPi) measurements, patients were grouped according to threshold values of 15 mmHg and 20 mmHg. Radiomics features were extracted from ONS images, and feature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating radiomics features with clinical-ultrasound variables, and its diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). The SHAP method was adopted to explain the prediction models.ResultsAmong the machine learning models, the LR model demonstrated superior predictive efficiency and robustness at threshold values of 15 mmHg and 20 mmHg. At a threshold of 20 mmHg, the AUC values for the training and testing cohorts were 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model, respectively. Similarly, at a threshold of 15 mmHg, the AUC values were consistent across models: 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model. Notably, the nomogram model outperformed the clinical model. Decision curve analysis (DCA) further confirmed a higher net benefit for predicting intracranial hypertension across all models.ConclusionThe nomogram model, which integrates both clinical-semantic and radiomics features, demonstrated strong performance in predicting intracranial hypertension across different threshold values. It shows promise for enhancing non-invasive ICP monitoring and supporting individualized therapeutic strategies. |
| format | Article |
| id | doaj-art-d1cc31b3ea1c4553a672614823ea3e9e |
| institution | Kabale University |
| issn | 2673-3129 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medical Technology |
| spelling | doaj-art-d1cc31b3ea1c4553a672614823ea3e9e2025-02-05T07:32:28ZengFrontiers Media S.A.Frontiers in Medical Technology2673-31292025-02-01710.3389/fmedt.2025.14852441485244Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomyZunfeng Fu0Lin Peng1Laicai Guo2Chao Qin3Yanhong Yu4Jiajun Zhang5Yan Liu6Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of General Practice, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of Neuro-intensive Care Unit, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaDepartment of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, ChinaObjectiveThis study aims to develop and validate a nomogram that combines traditional ultrasound radiomics features with clinical parameters to assess early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI). The study incorporates the Shapley Additive Explanations (SHAP) method to interpret the radiomics model.MethodsThis study included 199 patients with severe TBI (training cohort: n = 159; testing cohort: n = 40). Postoperative ultrasound images of the optic nerve sheath (ONS) were obtained at 6 and 18 h after DC. Based on invasive intracranial pressure (ICPi) measurements, patients were grouped according to threshold values of 15 mmHg and 20 mmHg. Radiomics features were extracted from ONS images, and feature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating radiomics features with clinical-ultrasound variables, and its diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). The SHAP method was adopted to explain the prediction models.ResultsAmong the machine learning models, the LR model demonstrated superior predictive efficiency and robustness at threshold values of 15 mmHg and 20 mmHg. At a threshold of 20 mmHg, the AUC values for the training and testing cohorts were 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model, respectively. Similarly, at a threshold of 15 mmHg, the AUC values were consistent across models: 0.803 and 0.735 for the clinical model, 0.908 and 0.891 for the radiomics model, and 0.918 and 0.902 for the nomogram model. Notably, the nomogram model outperformed the clinical model. Decision curve analysis (DCA) further confirmed a higher net benefit for predicting intracranial hypertension across all models.ConclusionThe nomogram model, which integrates both clinical-semantic and radiomics features, demonstrated strong performance in predicting intracranial hypertension across different threshold values. It shows promise for enhancing non-invasive ICP monitoring and supporting individualized therapeutic strategies.https://www.frontiersin.org/articles/10.3389/fmedt.2025.1485244/fullultrasound imagingsevere traumatic brain injuryintracranial pressureultrasound radiomicsmachine learningoptic nerve sheath diameter |
| spellingShingle | Zunfeng Fu Lin Peng Laicai Guo Chao Qin Yanhong Yu Jiajun Zhang Yan Liu Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy Frontiers in Medical Technology ultrasound imaging severe traumatic brain injury intracranial pressure ultrasound radiomics machine learning optic nerve sheath diameter |
| title | Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| title_full | Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| title_fullStr | Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| title_full_unstemmed | Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| title_short | Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| title_sort | ultrasound based radiomics and clinical factors based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy |
| topic | ultrasound imaging severe traumatic brain injury intracranial pressure ultrasound radiomics machine learning optic nerve sheath diameter |
| url | https://www.frontiersin.org/articles/10.3389/fmedt.2025.1485244/full |
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