Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.
Authors
Affiliations (7)
Affiliations (7)
- Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, No. 366 Taishan Street, Taishan District, Tai'an, 271000, Shandong Province, China.
- Department of Ultrasound, The Affiliated Hospital of Qilu Medical University, Xintai, China.
- College of Medical Imaging and Laboratory, Jining Medical University, Jining, China.
- College of Radiology, Shandong First Medical University, Tai'an, China.
- Department of Public Health Management, The Second Affiliated Hospital of Shandong, First Medical University, Tai'an, China.
- Department of Ultrasound, The Second Affiliated Hospital of Shandong First Medical University, No. 366 Taishan Street, Taishan District, Tai'an, 271000, Shandong Province, China. [email protected].
- Department of General Practice, The Second Affiliated Hospital of Shandong First Medical University, No. 366 Taishan Street, Taishan District, Tai'an, 271000, Shandong Province, China. [email protected].
Abstract
To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressive craniectomy (DC) in patients with severe traumatic brain injury (TBI) and to demonstrate its potential clinical value as a noninvasive tool for guiding timely intervention and improving patient outcomes. This study included 238 patients with severe TBI (training cohort: n = 166; testing cohort: n = 72). Postoperative ultrasound images of the optic nerve sheath (ONS) and Spectral doppler imaging of middle cerebral artery (MCASDI) were obtained at 6 and 18 h after DC. Patients were grouped according to threshold values of 15 mmHg and 20 mmHg based on invasive intracranial pressure (ICPi) measurements. Clinical-semantic features were collected, and radiomics features were extracted from ONS images, and Additionally, deep transfer learning (DTL) features were generated using RseNet101. Predictive models were developed using the Light Gradient Boosting Machine (light GBM) machine learning algorithm. Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. A combined nomogram was developed by integrating DLR (deep learning radiomics) features with clinical-ultrasound variables, and its diagnostic performance over different thresholds was evaluated using Receiver Operating Characteristic (ROC) curve analysis and decision curve analysis (DCA). The nomogram model demonstrated superior performance over the clinical model at both 15 mmHg and 20 mmHg thresholds. For 15 mmHg, the AUC was 0.974 (95% confidence interval [CI]: 0.953-0.995) in the training cohort and 0.919 (95% CI: 0.845-0.993) in the testing cohort. For 20 mmHg, the AUC was 0.968 (95% CI: 0.944-0.993) in the training cohort and 0.889 (95% CI: 0.806-0.972) in the testing cohort. DCA curves showed net clinical benefit across all models. Among DLR models based on ONS, MCASDI, or their pre-fusion, the ONS-based model performed best in the testing cohorts. The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting early IH in post-DC patients. It shows promise for enhancing non-invasive ICP monitoring and supporting individualized therapeutic strategies.