Medicine-Engineering Interdisciplinary

Tumor Displacement Prediction and Augmented Reality Visualization in Brain Tumor Resection Surgery

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  • 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; 3. State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200030, China

Received date: 2023-09-07

  Accepted date: 2023-12-14

  Online published: 2025-07-31

Abstract

The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery. And the study will be integrated with augmented reality technology to achieve three-dimensional visualization, thereby enhancing the complete resection rate of tumor and the success rate of surgery. Based on the preoperative MRI data of the patients, a 3D virtual model is reconstructed and 3D printed. A brain biomimetic model is created using gel injection molding. By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables, the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery. An orthogonal experiment is conducted on the biomimetic model to establish a predictive model, and this model is incorporated into the augmented reality navigation system. To validate the predictive model, five participants wore HoloLens2 devices, overlaying the patient’s 3D virtual model onto the physical head model. Subsequently, the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models (actual coordinates and predicted coordinates, respectively). The difference between these coordinates represents the model’s prediction error. The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from .0.678 7mm to 0.295 7mm and .0.431 4mm to 0.225 3mm, respectively. The relative errors for each experimental group are within 10%, demonstrating a good fit of the model. This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations. It also provides a new approach for surgeons. By combining augmented reality visualization, it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.

Cite this article

Wang Jiayu, Wang Shuyi, Wei Yongxu, Liao Chencong, Shang Hanbing, Wang Xue, Kang Ning . Tumor Displacement Prediction and Augmented Reality Visualization in Brain Tumor Resection Surgery[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(4) : 733 -743 . DOI: 10.1007/s12204-024-2576-8

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