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ORIGINAL RESEARCH article

Front. Med. Technol.
Sec. Diagnostic and Therapeutic Devices
Volume 7 - 2025 | doi: 10.3389/fmedt.2025.1485244

Ultrasound-based Radiomic and Clinical Nomogram for Early Intracranial Hypertension Detection in Patients with Decompressive Craniotomy

Provisionally accepted
Zunfeng Fu Zunfeng Fu Lin Peng Lin Peng Laicai Guo Laicai Guo Chao Qin Chao Qin Zunfeng Fu Zunfeng Fu Yanhong Yu Yanhong Yu Jiajun Zhang Jiajun Zhang Yan Liu Yan Liu *
  • The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China

The final, formatted version of the article will be published soon.

    Objective: This 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.This 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 hours after DC. Based on invasive intracranial pressure (ICPi) measurements, patients were grouped according to threshold values of 15mmHg 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.Results: Among 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, 普通(网站), 两端对齐, 行距: 2 倍 行距

    Keywords: ultrasound imaging, Severe traumatic brain injury, Intracranial Pressure, ultrasound radiomics, machine learning, optic nerve sheath diameter, Transcranial color doppler

    Received: 23 Aug 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Fu, Peng, Guo, Qin, Fu, Yu, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yan Liu, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.