Finite element study on whether posterior upper wall fracture is a risk factor for the failure of short-segment pedicle screw fixation in the treatment of L1 burst fracture

Development and validation of a radiomics model using plain radiographs to predict spine fractures with posterior wall injury

PURPOSE

When spine fractures involve posterior wall damage, they pose a heightened risk of instability, consequently influencing treatment strategies. To enhance early diagnosis and refine treatment planning for these fractures, we implemented a radiomics analysis using deep learning techniques, based on both anteroposterior and lateral plain X-ray images.

METHODS

Retrospective data were collected for 130 patients with spine fractures who underwent anteroposterior and lateral imaging at two centers (Center 1, training cohort; Center 2, validation cohort) between January 2010 and June 2024. The Vision Transformer (ViT) technique was employed to extract imaging features. The features selected through multiple methods were then used to construct a machine learning model using NaiveBayes and Support Vector Machine (SVM). The model's performance was evaluated using the area under the curve (AUC) metric.

RESULTS

12 features were selected to form the deep learning features. The SVM model using a combination of anteroposterior and lateral plain images showed good performance in both centers with a high AUC for predicting spine fractures with posterior wall injury (Center 1, AUC: 0.909, 95% CI: 0.763-1.000; Center 2, AUC: 0.837, 95% CI: 0.678-0.996). The SVM model based on the combined images outperformed both the individual position images and a spine surgeon with 3 years of clinical experience in classification performance.

CONCLUSIONS

Our study demonstrates that a radiomic model created by integrating anteroposterior and lateral plain X-ray images of the spine can more effectively predict spine fractures with posterior wall injury, aiding clinicians in making accurate diagnoses and treatment decisions.

Radiomics analysis based on plain X-rays to detect spinal fractures with posterior wall injury

Purpose

Spinal fractures, particularly those involving posterior wall injury, pose a heightened risk of instability and significantly influence treatment strategies. This study aimed to improve early diagnosis and treatment planning for spinal fractures through radiomics analysis based on plain X-ray imaging.

Methods

This retrospective study analyzed plain X-rays of patients with spinal fractures at the thoracolumbar junction. Radiomic features were extracted from both anteroposterior and lateral plain spine radiographs to evaluate the utility of radiomics in detecting posterior wall injury. Diagnostic accuracy, sensitivity, and specificity of the radiomics models were assessed and compared with the performance of a spine surgeon.

Results

A total of 100 patients were included in the study, and four radiomic features were identified to construct radiomic signatures. In the training set, the RandomForest, ExtraTrees, and eXtreme Gradient Boosting (XGBoost) models achieved an area under the curve (AUC) of 1. In the validation set, the highest AUC value was 0.889, achieved by the RandomForest and XGBoost models. The diagnostic accuracy, sensitivity, and specificity of the radiomics models outperformed those of the spine surgeon.

Conclusions

Radiomics analysis based on plain X-ray imaging demonstrates significant potential for detecting posterior wall injury following spinal fractures. This approach offers a promising tool for early diagnosis and informed clinical decision-making in the management of spinal fractures.

Effect of Minimally Invasive Internal Arch Nailing Surgery on Tissue Traumatic Stress Response in Patients with Vertebral Fractures

Objective

To analyze the influence of minimally invasive arch root nailing internal fixation surgery on tissue traumatic stress response in patients with vertebral fractures and explore the advantages of this treatment.

Methods

One hundred and thirty-six patients with vertebral fractures admitted to our hospital from January 2020 to January 2022 were selected and divided into two groups based on the treatment method: the control group was treated with open arch root nail internal fixation surgery and the study group was treated with minimally invasive arch root nail internal fixation. The lumbar spine function, ODI, VAS, JOA score, complications, inflammation, and stress response were compared between the two groups.

Results

After the operation, the ratio of intervertebral space and anterior edge height increased, and the Cobb angle decreased in both groups; the surgical incision, hospital stay, and operation time in the study group were shorter than those in the control group, and the intraoperative drainage volume and intraoperative blood loss were smaller than those in the control group (P < 0.05); before surgery, there was no significant difference in ODI and VAS scores between the two groups (P > 0.05). After surgery, the ODI and VAS scores in the two groups were significantly decreased, and the JOA score was significantly increased; complications occurred in the control group and the study. The incidence of complications in the study group was lower than that in the control group (P < 0.05); after surgery, compared with the control group, the serum TNF-α, CRP levels, and stress response indexes of the study group decreased more significantly (P < 0.05).

Conclusion

Minimally invasive pedicle screw fixation has high safety and obvious advantages. The patient's stress response index and pain level are low, and it will not cause obvious damage to the patient. The postoperative lumbar spine function is significantly improved, which is beneficial to the patient's postoperative recovery. It is easy to operate, will not damage the thoracic and lumbar vertebrae significantly, and the fluoroscopy time is relatively short, and it has a good recovery effect. Therefore, minimally invasive internal arch nailing surgery can be used as the preferred treatment for patients with vertebral fractures.