Bone density measurement in patients with spinal metastatic tumors using chest quantitative CT deep learning model
Year: 2024
Type: article
Abstract: Objective: This study aims to develop a deep learning model using the 3DResUNet architecture to predict vertebral volumetric bone mineral density (vBMD) from Quantitative Computed Tomography (QCT) scans in patients with spinal metastatic tumors, enhancing osteoporosis screening capabilities. Methods: 749 patients with spinal metastatic tumors underwent QCT vertebral vBMD measurements. The dataset was randomly split into training (599 cases) and test sets (150 cases). The 3DResUNet model was trained for vBMD classification and prediction using QCT images processed with automated bone segmentation and ROI extraction. Results: (test set) between the model predictions and QCT measurements. The model achieved an area under the curve (AUC) of 0.977 (training set) and 0.966 (test set) for diagnosing Osteoporosis based on vBMD. Conclusion: The developed deep learning model using 3DResUNet effectively predicts vertebral vBMD from QCT scans in patients with spinal metastatic tumors. It provides accurate and automated vBMD measurements, potentially facilitating widespread osteoporosis screening in clinical practice, mainly where DXA availability is limited.
Source: Journal of bone oncology
Authors: Zhi Wang, Yiyun Tan, Kaibin Zeng, Hao Tan, Pingsen Xiao, Guanghui Su
Institution: Hunan Normal University
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,720
OpenAlex ID: W4403264272