Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement

Sep 30, 2026

Secondary decompressive craniectomy is often incorporated into ICU tiered care to control elevated intracranial pressure after traumatic brain injury. However, advance identification of high-risk patients can enable earlier intervention and help avoid further clinical deterioration. This study develops a radiomics-based machine learning predictive model to estimate the likelihood of secondary decompressive craniectomy in TBI patients following emergent craniotomy with bone flap replacement.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages11

Reading Time28 min

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Abstract

Background

Methods

Results

Conclusion

Main Text

Clinical background and rationale

Secondary decompressive craniectomy decision context

Chen et al. Chinese Neurosurgical Journal (2026) 12:1 [https://doi.org/10.1186/s41016-025-00423-5](https://doi.org/10.1186/s41016-025-00423-5) Chinese Neurosurgical Journa RESEARCH Open Access Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement Tiange Chen1,2†, Ganzhi Liu1,2†, Ziyuan Liu1,2, Jiacheng Liu1,2, Jinfang Liu 1,2 and Zhongyi Sun 1,2* Abstract Background Secondary decompressive craniectomy (DC) is commonly integrated into tiered therapeutic protocols in the intensive care unit (ICU) to manage elevated intracranial pressure following traumatic brain injury (TBI) . Identifying high-risk patients in advance could enable early intervention and help prevent further deterioration. This study aims to develop a machine learning-based predictive model using radiomics to assess the likelihood of secondary DC in TBI patients. Methods A total of 65 patients were enrolled and divided into training and test cohorts through stratified random sampling with a 7:3 ratio. Radiomic features were extracted from pre-evacuation CT data. The most relevant features were identified through importance score computation, and various predictive models were assessed using distinct machine learning algorithms and data sources. Model performance was benchmarked to construct an optimal predictive model. Results No statistically significant differences were observed in demographic and clinical characteristics between the DC and non-DC groups. The model based solely on demographic and clinical data did not achieve satisfactory performance, with an AUC below 0.5 in the test cohort. In radiomic modeling, the randomForest model demonstrated consistent performance, achieving an AUC of 0.83 in the test cohort. The multiomic model, which incorporated demographic, clinical, and radiomic features, showed improved predictive performance, with the cforest model achieving an AUC of 0.87 in the training cohort and 0.86 in the test cohort. Conclusion We developed radiomics-based predictive models to assess the likelihood of progressively refractory intracranial hypertension leading to secondary DC in a selected cohort ofTBI patients who had undergone emergent craniotomy for hematoma evacuation with bone flap replacement. The model relying solely on radiomic features extracted from the lesion demonstrated satisfactory performance. When these features were integrated with demographic and clinical data to create a multiomic model, predictive performance further improved. These findings highlight the model’s potential to identify high-risk patients, enabling early intervention to prevent further deterioration. Keywords Radiomics, Traumatic brain injury, Decompressive craniectomy, Machine learning †Tiange Chen and Ganzhi Liu contributed equally to this work. *Correspondence: Zhongyi Sun [zhongyi_sun@hotmail.com](zhongyi_sun@hotmail.com) Full list of author information is available at the end of the article © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/. The Creative Commons P

FAQs

What clinical problem does the study address?

The study addresses the need to predict which TBI patients will require secondary decompressive craniectomy due to progressive, refractory intracranial hypertension after emergent craniotomy.

How were data and features used to build the models?

Radiomic features were extracted from pre-evacuation CT data, and predictive models were evaluated using different machine learning algorithms and data sources, including demographic/clinical-only and combined multiomic inputs.

What were the key model performance findings?

A demographic-and-clinical-only model showed poor test performance (AUC below 0.5). In radiomic modeling, a randomForest model reached an AUC of 0.83 in the test cohort, and the multiomic cforest model achieved AUCs of 0.87 (training) and 0.86 (test).

What does the study conclude about radiomics and multiomic models?

Radiomics-only features can provide satisfactory predictive performance, and integrating radiomic features with demographic and clinical data improves predictive accuracy for identifying high-risk patients.

Sep 30, 2026

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