Enabling Early Identification of Malignant Vertebral Compression Fractures Through 2.5D Convolutional Neural Network
Chengbin Huang1, Enli Li1, Jiasen Hu2
1Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Study Design:
This study used a retrospective data analysis approach combined with model development and validation.
Objectives:
The present study introduces a 2.5D convolutional neural network (CNN) model leveraging CT imaging to facilitate the early detection of malignant vertebral compression fractures (MVCFs), potentially reducing reliance on invasive biopsies.
Summary Of Background Data:
Vertebral histopathologic biopsy is recognized as the gold standard for differentiating between osteoporotic and malignant vertebral compression fractures (VCFs). Nevertheless, its application is restricted due to its invasive nature and high cost, highlighting the necessity for alternative methods to identify MVCFs.
Materials And Methods:
The clinical, imaging, and pathologic data of patients who underwent vertebral augmentation and biopsy at institution 1 and institution 2 were collected and analyzed. On the basis of the vertebral CT images of these patients, 2D, 2.5D, and 3D CNN models were developed to identify the patients with osteoporotic vertebral compression fractures (OVCF) and MVCF. To verify the clinical application value of the CNN model, two rounds of reader studies were performed.
Results:
The 2.5D CNN model performed well, and its performance in identifying MVCF patients was significantly superior to that of the 2D and 3D CNN models. In the training data set, the area under the receiver operating characteristic curve (AUC) of the 2.5D CNN model was 0.996 and an F1 score of 0.915. In the external cohort test, the AUC was 0.815 and an F1 score of 0.714. And clinicians' ability to identify MVCF patients has been enhanced by the 2.5D CNN model. With the assistance of the 2.5D CNN model, the AUC of senior clinicians was 0.882, and the F1 score was 0.774. For junior clinicians, the 2.5D CNN model-assisted AUC was 0.784 and the F1 score was 0.667.
Conclusions:
The development of our 2.5D CNN model marks a significant step toward noninvasive identification of MVCF patients. The 2.5D CNN model may be a potential model to assist clinicians in better identifying MVCF patients.
